Series Content Consultant:
Larry H. Bernstein, MD, FCAP, Emeritus CSO, LPBI Group
Volume Content Consultant:
Prof. Marcus W. Feldman
https://www.youtube.com/watch?v=aT-Jb0lKVT8
BURNET C. AND MILDRED FINLEY WOHLFORD PROFESSOR IN THE SCHOOL OF HUMANITIES AND SCIENCES
Stanford University, Co-Director, Center for Computational, Evolutionary and Human Genetics (2012 – Present)
Latest in Genomics Methodologies for Therapeutics:
Gene Editing, NGS & BioInformatics,
Simulations and the Genome Ontology
2019
Volume Two
https://www.amazon.com/dp/B08385KF87
Product details
- File Size:3138 KB
- Print Length:217 pages
- Publisher:Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston; 1 edition (December 28, 2019)
- Publication Date:December 28, 2019
- Sold by:Amazon Digital Services LLC
- Language:English
- ASIN:B08385KF87
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Prof. Marcus W. Feldman, PhD, Editor
Prof. Stephen J. Williams, PhD, Editor
and
Aviva Lev-Ari, PhD, RN, Editor
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Aviva Lev-Ari, PhD, RN
UC, Berkeley, PhD’83
Editor-in-Chief BioMed e-Book Series
Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston
avivalev-ari@alum.berkeley.edu
We recommend the reader to browse through our Genomics, Volume 1:
Series B: Frontiers in Genomics Research
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VOLUME 1: Genomics Orientations for Personalized Medicine.
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Preface and Volume Introduction – Voice of Professor Williams
WATCH VIDEO
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Since Volume Two covers more advanced topics in Genomics than Volume One, the editors recommend the e-Reader to review the following electronic Table of Contents of Volume One and consider reading one article in each Chapter before or in parallel to reading Volume Two. Each article title is a live link to the article in the Journal.
List of Contributors to Volume One
Larry Bernstein, MD, FCAP, Senior Editor
Introduction 1.1, 1.2, 1.4, 1.5, 2.2, 2.6, 3.1, 3.2, 3.3, 3.6, 4.6, 4.8, 5.8, 5.9, 5.10, 6.1, 6.2, 6.3, 6.5, 6.7, 6.8, 6.9, 6.10, 6.11, 6.12, 6.13, 6.14, 6.16, 6.17, 8.5, 8.6, 9.6, 10.4, 10.5, 10.6, 10.7, 11.1, 11.7, 11.10, 11.11, 12.2, 12.3, 12.4, 12.6, 12.8, 13.8, 13.9, 14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, 15.5, 15.8, 15.9, 15.9.4, 15.11, 16.1, 16.2, 16.3, 16.4, 16.5, 17.2, 18.1, 18.2, 18.5, 18.6, 20.2, 20.3, 20.4, 20.5, 20.6, Introduction-21, Summary-21, Volume Summary, Epilogue
Stephen J. Williams, PhD, Editor
2.3, 2.7, 6.15, 7.6, 8.8, 11.8, 12.5, 12.7, 15.3, 20.7, Introduction-21
Aviva Lev-Ari, PhD, RN, Editor-in-Chief, BioMed e-Books Series
1.6, 2.1, 2.5, 3.4, 3.5, 3.7, 3.8, 4.1, 4.4, 4.5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 6.18, 7.1, 7.2, 7.3, 7.4, 7.5, 8.1, 8.2, 8.3, 8.7, 8.9, 9.1, 9.2, 9.3, 9.4, 9.5, 9.8, 10.1, 10.2, 10.3, 10.8, 11.2, 11.3, 11.4, 11.5, 11.9, 12.1, 13.5 13.7, 15.1, 15.2, 15.4, 15.6, 15.7, 15.9.1, 15.9.2, 15.9.3, 15.9.5, 15.10, 17.1, 18.3, 18.4, 19.4, 19.5, 20.1, 20.8, 21.1.1, 21.1.2, 21.1.3, 21.1.4, 21.2.1, 21.2.2, 21.2.3, 21.2.4, 21.3.1, 21.3.2, 21.4.2
Sudipta Saha, PhD
1.3, 6.6, 11.6, 13.2, 13.3, 13.4, 19.1, 19.2, 19.6, 19.7, 19.8, 19.9, 19.10
Ritu Saxena, PhD
4.2, 6.4, 9.7, 13.6, 14.1, 17.3, 17.4, 17.5, 19.3
Tilda Barlyia, PhD
8.4, 13.1, 14.2
Marcus W Feldman, PhD, Professor of Computational Biology, Stanford University, Department of Biology
2.4, Part 5
4.7, 4.9, 4.10
electronic Table of Contents, Volume One
Chapter 1
1.2 CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA
1.3 DNA – The Next-Generation Storage Media for Digital Information
1.4 CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC
1.5 Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
1.6 Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology
Chapter 2
2.2 DNA structure and Oligonucleotides
2.3 Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell
2.6 The Binding of Oligonucleotides in DNA and 3-D Lattice Structures
2.7 Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies
Chapter 3
3.1 Big Data in Genomic Medicine
3.2 CRACKING THE CODE OF HUMAN LIFE: The Birth of Bioinformatics & Computational Genomics – Part IIB
3.3 Expanding the Genetic Alphabet and linking the Genome to the Metabolome
3.5 MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified
3.6 Identification of Biomarkers that are Related to the Actin Cytoskeleton
Chapter 4
4.1 ENCODE Findings as Consortium
4.2 ENCODE: The Key to Unlocking the Secrets of Complex Genetic Diseases
4.5 Human Genome Project – 10th Anniversary: Interview with Kevin Davies, PhD – The $1000 Genome
4.6 Quantum Biology And Computational Medicine
4.7 The Underappreciated EpiGenome
4.8 Unraveling Retrograde Signaling Pathways
4.9 “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA
4.10 DNA: One man’s trash is another man’s treasure, but there is no JUNK after all
Chapter 5
5.1 Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1
5.2 Computational Genomics Center: New Unification of Computational Technologies at Stanford
5.3 Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3
5.4 Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz
5.5 Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS
5.6 NGS Market: Trends and Development for Genotype-Phenotype Associations Research
5.9 Transphosphorylation of E-coli Proteins and Kinase Specificity
5.10 Genomics of Bacterial and Archaeal Viruses
Chapter 6
6.1 Directions for Genomics in Personalized Medicine
6.3 Mitochondrial Damage and Repair under Oxidative Stress
6.4 Mitochondria: More than just the “Powerhouse of the Cell”
6.5 Mechanism of Variegation in Immutans
6.7 Cardiac Ca2+ Signaling: Transcriptional Control
6.8 Unraveling Retrograde Signaling Pathways
6.12 Zebrafish—Susceptible to Cancer
6.13 RNA Virus Genome as Bacterial Chromosome
6.14 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome
6.15 Telling NO to Cardiac Risk- DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)
6.16 Transphosphorylation of E-coli proteins and kinase specificity
6.17 Genomics of Bacterial and Archaeal Viruses
Chapter 7
7.2 Consumer Market for Personal DNA Sequencing: Part 4
7.6 Personalized Medicine: Clinical Aspiration of Microarrays
Chapter 8
8.1 Personalized Medicine as Key Area for Future Pharmaceutical Growth
8.2 Inaugural Genomics in Medicine – The Conference Program, 2/11-12/2013, San Francisco, CA
8.4 Nanotechnology, Personalized Medicine and DNA Sequencing
8.6 Transcript Dynamics of Proinflammatory Genes
8.8 Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]
Chapter 9
9.1 Personal Tale of JL’s Whole Genome Sequencing
9.6 The Initiation and Growth of Molecular Biology and Genomics – Part I
9.7 Personalized Medicine-based Cure for Cancer Might Not Be Far Away
9.8 Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)
Chapter 10
10.2 Imatinib (Gleevec) May Help Treat Aggressive Lymphoma: Chronic Lymphocytic Leukemia (CLL)
10.4 Treatment for Metastatic HER2 Breast Cancer
10.5 Personalized Medicine in NSCLC
10.6 Gene Sequencing – to the Bedside
10.7 DNA Sequencing Technology
10.8 Nobel Laureate Jack Szostak Previews his Plenary Keynote for Drug Discovery Chemistry
Chapter 11
11.1 mRNA Interference with Cancer Expression
11.2 Angiogenic Disease Research Utilizing microRNA Technology: UCSD and Regulus Therapeutics
11.4 A microRNA Prognostic Marker Identified in Acute Leukemia
11.5 MIT Team: Microfluidic-based approach – A Vectorless delivery of Functional siRNAs into Cells.
11.7 When Clinical Application of miRNAs?
11.8 How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis,
11.9 Potential Drug Target: Glycolysis Regulation – Oxidative Stress-responsive microRNA-320
11.10 MicroRNA Molecule May Serve as Biomarker
11.11 What about Circular RNAs?
Chapter 12
12.1 The “Cancer Establishments” Examined by James Watson, Co-discoverer of DNA w/Crick, 4/1953
12.2 Otto Warburg, A Giant of Modern Cellular Biology
12.3 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?
12.4 Hypothesis – Following on James Watson
12.5 AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo
12.6 AKT signaling variable effects
12.8 Phosphatidyl-5-Inositol signaling by Pin1
Chapter 13
13.1 Nanotech Therapy for Breast Cancer
13.5 Prostate Cancer: Androgen-driven “Pathomechanism” in Early onset Forms of the Disease
13.6 In focus: Melanoma Genetics
13.8 Breast Cancer and Mitochondrial Mutations
13.9 Long noncoding RNA network regulates PTEN transcription
Chapter 14
14.1 HBV and HCV-associated Liver Cancer: Important Insights from the Genome
14.2 Nanotechnology and HIV/AIDS treatment
14.3 IRF-1 Deficiency Skews the Differentiation of Dendritic Cells
14.5 Five Malaria Genomes Sequenced
14.6 Rheumatoid Arthritis Risk
14.7 Approach to Controlling Pathogenic Inflammation in Arthritis
14.8 RNA Virus Genome as Bacterial Chromosome
14.9 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome
Chapter 15
15.1 Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School
15.3 DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)
15.6 Gene Therapy Into Healthy Heart Muscle: Reprogramming Scar Tissue In Damaged Hearts
15.8 Ca2+ signaling: transcriptional control
15.9 Lp(a) Gene Variant Association
15.9.5 Gene, Meis1, Regulates the Heart’s Ability to Regenerate after Injuries.
15.11 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancers?
Chapter 16
16.1 Can Resolvins Suppress Acute Lung Injury?
16.2 Lipoxin A4 Regulates Natural Killer Cell in Asthma
16.3 Biological Therapeutics for Asthma
16.4 Genomics of Bronchial Epithelial Dysplasia
16.5 Progression in Bronchial Dysplasia
Chapter 17
17.1 Breakthrough Digestive Disorders Research: Conditions Affecting the Gastrointestinal Tract.
17.2 Liver Endoplasmic Reticulum Stress and Hepatosteatosis
17.3 Biomarkers-identified-for-recurrence-in-hbv-related-hcc-patients-post-surgery
17.4 Usp9x: Promising Therapeutic Target for Pancreatic Cancer
17.5 Battle of Steve Jobs and Ralph Steinman with Pancreatic cancer: How We Lost
Chapter 18
18.1 Ubiquitin Pathway Involved in Neurodegenerative Disease
18.3 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic Drugs and Cholinesterase Inhibitors
18.5 Cell Transplantation in Brain Repair
18.6 Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?
Chapter 19
19.1 Genetics and Male Endocrinology
19.2 Genomic Endocrinology and its Future
19.4 Therapeutic Targets for Diabetes and Related Metabolic Disorders
19.6 Personal Recombination Map from Individual’s Sperm Cell and its Importance
19.7 Gene Trap Mutagenesis in Reproductive Research
19.8 Pregnancy with a Leptin-Receptor Mutation
19.10 Reproductive Genetic Testing
Chapter 20
20.1 Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?
20.2 Understanding the Role of Personalized Medicine
20.3 Attitudes of Patients about Personalized Medicine
20.4 Genome Sequencing of the Healthy
20.5 Genomics in Medicine – Tomorrow’s Promise
20.6 The Promise of Personalized Medicine
20.8 Genomic Liberty of Ownership, Genome Medicine and Patenting the Human Genome
Chapter 21
Recent Advances in Gene Editing Technology Adds New Therapeutic Potential for the Genomic Era: Medical Interpretation of the Genomics Frontier – CRISPR – Cas9
Introduction
21.1 Introducing CRISPR/Cas9 Gene Editing Technology – Works by Jennifer A. Doudna
21.1.1 Ribozymes and RNA Machines – Work of Jennifer A. Doudna
21.2 CRISPR in Other Labs
21.2.1 CRISPR @MIT – Genome Surgery
21.2.2 The CRISPR-Cas9 System: A Powerful Tool for Genome Engineering and Regulation
Yongmin Yan and Department of Gastroenterology, Hepatology & Nutrition, University of Texas M.D. Anderson Cancer, Houston, USADaoyan Wei*
21.2.5 CRISPR & MAGE @ George Church’s Lab @ Harvard
21.3 Patents Awarded and Pending for CRISPR
21.3.1 Litigation on the Way: Broad Institute Gets Patent on Revolutionary Gene-Editing Method
21.3.2 The Patents for CRISPR, the DNA editing technology as the Biggest Biotech Discovery of the Century
2.4 CRISPR/Cas9 Applications
21.4.2 CRISPR: Applications for Autoimmune Diseases @UCSF
21.4.3 In vivo validated mRNAs
21.4.6 Level of Comfort with Making Changes to the DNA of an Organism
21.4.8 CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development
Summary
VOLUME TWO Starts Here
https://www.amazon.com/dp/B08385KF87
Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
- No other book covers the same topics in a single volume
- No other book incorporates 74 e-Proceedings created in real time by the Book’s authors and editors
- No other book incorporates four collections of Tweets representing quotes from speakers at global leading conferences on Genomics
- No other book has 13 locations of Videos and Audio Podcasts that serve to enrich the e-Reader’s experience
No other book has 326 articles on the topics included in the Book’s title: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
|
|
Articles included in this volume | e-Proceedings & Keynote Lectures included in this volume
[counted in total] |
Tweets Collections generated @conferences
included in this volume [counted in total] |
Videos/Audios
included in this volume |
| Preface | | | | | ||
| Part 1 | 120
Larry 36 Aviva 66 SJW 16 Saha 8 Gail 2 |
||||||||||||||||||||||||
|||||| |
|| | |
| Part 2 | 135
Larry 38 Aviva 78 SJW 8 Saha 4 |
|||||||||||||||| | || | || |
| Part 3 | 63
Larry 10 Aviva 25 D. Nir 8 SJW 8 Irina 5 Saha 4 Gail 3 |
|||||||||| | |||| | |
| Part 4 | 12
Aviva 7 Saha 3 SJW 2 |
||| | ||
| Part 5 | 1
Marc 1 |
|||
| Part 6 | 1
Aviva 1 |
|||
| Part 7 | 20
SJW 9 Larry 4 Aviva 4 Saha 2 |
| | ||
| Part 8 | 22
Aviva 6 Larry 5 SJW 5 Saha 5 Guests 2 D. Nir 1 |
|| | ||
| Epilogue | |||||| | |||
| Total | 326
Larry 93 Aviva 121 SJW 48 Saha 22 D. Nir 8 Irina 7 Gail 4 Larry+Aviva 8 SJW+Aviva 3 Guests 6 Marc 1
|
74 | 4 |
13 |
Abbreviated eTOCs
Preface & Introduction
Part 1: NGS
1.1 The Science
1.2 Technologies and Methodologies
1.3 Clinical Aspects
1.4 Business and Legal
Part 2: CRISPR for Gene Editing and DNA Repair
2.1 The Science
2.2 Technologies and Methodologies
2.3 Clinical Aspects
2.4 Business and Legal
Part 3: AI in Medicine
3.1 The Science
3.2 Technologies and Methodologies
3.3 Clinical Aspects
3.4 Business and Legal
3.5 Latest in Machine Learning (ML) Algorithms harnessed for Medical Diagnosis: Pattern Recognition & Prediction of Disease Onset
Part 4: Single Cell Genomics
4.1 The Science
4.2 Technologies and Methodologies
4.3 Clinical Aspects
4.4 Business and Legal
Part 5: Evolution Biology Genomics Modeling @Feldman Lab, Stanford University – Written and Curated by Prof. Marc Feldman
5.1 Human Genomic Variation, Population Diversity, and Genome-Wide Associations
Part 6: Simulation Modeling in Genomics
6.1 Mutation Analysis – Gene Encoding
6.2 Mitochondrial Variations
6.3 Variant Analysis
6.4 Variant Detection in Hereditary Cancer Genes
6.5 Immuno-Informatics
6.6 RNA Sequencing
6.7 Complex Insertions and Deletions
6.8 Evolutionary Biology
6.9 Simulation Programs
6.10 A comparison of tools for the simulation of genomic next-generation sequencing data
Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases
7.1 Genome-wide associations with complex diseases (GWAS)
7.2 Non-coding DNA and phenotypes—including diseases like cancer
7.3 Transcriptomic and ‘omic associations with phenotypes including cancer
7.4 Rare variants and diseases
7.5 Population-level genomics and the meaning of group differences
7.6 Targeting drugs for complex diseases
Part 8: Epigenomics and Genomic Regulation
8.1 Genomic controls on epigenomics
8.2 The ENCODE project and gene regulation
8.3 Small interfering RNAs and gene expression
8.4 Epigenomics in cancer
8.5 Environmental epigenomics
Summary
Epilogue
Preface and Introduction to Genomics Volume 2
Voice of Aviva Lev-Ari & Stephen Williams
PREFACE
Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology – this book is unique, among all books on similar topics, in the scope and breadth of its up-to-date coverage of Genomics Methodologies for Therapeutics. It integrates in a single volume four distinct perspectives: (a) basic science, (b) technologies & methodologies, (c) clinical aspects, and (d) business and legal aspects.
In terms of synergy, this book combines:
- Content curation in article format with embedded videos and audio podcasts
- Conference e-Proceedings based on real-time coverage by the authors and editors of this book.
- Archived tweets of quotes from speakers at leading Biotech conferences, posted in real-time by the same authors
The material in this book represents the scientific frontier in Biological Sciences and Medicine related to the Genomics aspects of Disease Onset.
It addresses:
- All aspects of life: the Cell, the Organ, the Human Body and Human Populations
- All methodologies of genomic data analysis: Next Generation Sequencing, Gene Editing, AI, Single Cell Genomics, Evolution Biology Genomics, Simulation Modeling in Genomics, Genotypes and Phenotypes Modeling, measurement of Epigenomics effects on disease, and developments in Pharmaco-Genomics
The book consists of eight parts:
Part 1: NGS
Part 2: CRISPR for Gene Editing and DNA Repair
Part 3: AI in Medicine
Part 4: Single Cell Genomics
Part 5: Evolution Biology Genomics Modeling @Feldman Lab, Stanford University
Part 6: Simulation Modeling in Genomics
Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases
Part 8: Epigenomics and Genomic Regulation
In terms of structure:
- Parts 1-4 have a similar structure, each one organized into four topics:
- The Science
- Technologies & Methodologies
- Clinical Aspects
- Business and Legal Aspects
- Part 5 provides the voice of a leading expert
- Part 6 contains a curated bibliography on simulation modeling applied to Ten genomics topics
- Parts 7 and 8 consist of a collection of individual articles
In summary: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
- No other book covers the same topics in a single volume
- No other book incorporates 74 e-Proceedings created in real time by the Book’s authors and editors
- No other book incorporates four collections of Tweets representing quotes from speakers at global leading conferences on Genomics
- No other book has 13 locations of Videos and Audio Podcasts that serve to enrich the e-Reader’s experience
No other book has 326 articles on the topics included in the Book’s title: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
Volume Introduction
Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine
This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine
Looking forward 25 years: the future of medicine.
Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y
Aviv Regev, PhD
Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.
- millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
- In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
- we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
- Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers
Feng Zhang, PhD
investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.
- fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
- Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
- 1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
- refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society
Elizabeth Jaffee, PhD
Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.
- a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
- developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.
Jeremy Farrar, OBE FRCP FRS FMedSci
Director, Wellcome Trust.
- shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
- building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.
John Nkengasong, PhD
Director, Africa Centres for Disease Control and Prevention.
- To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
- Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.
Eric Topol, MD
Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.
- In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
- enhanced capabilities to perform functions that are not feasible now.
- AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
- the concept of a learning health system will be redefined by AI.
Linda Partridge, PhD
Professor, Max Planck Institute for Biology of Ageing.
- Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.
Trevor Mundel, MD
President of Global Health, Bill & Melinda Gates Foundation.
- finding new ways to share clinical data that are as open as possible and as closed as necessary.
- moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
- working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
- deliver on the promise of global health equity.
Josep Tabernero, MD, PhD
Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).
- genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
- Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
- Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
- AI will help guide the development of individually matched
- genetic patient screenings
- the promise of liquid biopsy policing of disease?
Pardis Sabeti, PhD
Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.
- the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
- But this will only happen with a commitment from the global community.
Els Toreele, PhD
Executive director, Médecins Sans Frontières Access Campaign
- we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
- This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
- The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.
In addition to these perspectives we add here population-based sequencing studies as another one to be accelerated and augmented in the future and dominate the research field of Genomics in the coming two decades.
See below, Part 5: Evolution Biology Genomics Modeling @Feldman Lab, Stanford University
5.1 Human Genomic Variation, Population Diversity, and Genome-Wide Associations
Precision Genomic Medicine capabilities are enhanced by Population Studies. US, UK, France, and China launched population-based sequencing studies, the leading ones include:
- In the US, All of Us Research Program
1,000,000 genomes of all life stages, health statuses, races and ethnicities, and geographic regions, reflecting the human diversity of the US
- In China, Chinese Precision Medicine Initiative
Aims to sequence 100,000,000 genomes by 2030
- In France, France Genome Medicine Plan
Sequencing 235,000 genomes per year by 2020
Corresponds to 20,000 patients with rare disease and 50,000 patients with metastatic or refractory cancer
- In the United Kingdom, UK-based 100,000 Genomics England Project
100,000 genomes – sequencing completed in 2018
100 rare disease and seven common cancers
Sequencing 5,000,000 genomes in the next 5 years in both clinical and research environments
The American Private Health Care Sector in leading medical organizations is integrating genomics technologies into clinical care at these public healthcare systems. The leaders in this trend include the following:
- Geisinger Health System
MyCode project in partnership with Regeneron Pharmaceuticals
>190,00 patient-participants – expanded to include all consenting Geisinger patients
- Intermountain Healthcare
HerediGene in partnership with deCODE genetics
>500,000 patient-participants
- Renown Institute for Health Innovation
Healthy Nevada Project in partnership with Helix
50,000 participants
- AdventHealth
WholeMe Study in partnership with Helix
10,000 participants
Conferences on Personalized Medicine address integration of Genomics Medicine with clinical care and the reimbursement for genomic testing and pharmacogenomics. The leading two conferences on each Coast are:
and
- PMWC Silicon Valley, January 21-24, 2020
eProceedings 15th Annual Personalized Medicine Conference at Harvard Medical School – THE PARADIGM EVOLVES, November 13 – 14, 2019 • Harvard Medical School, Boston, MA
Panels on Population Studies for Genomic-based diagnosis and treatment are included in the program of PMWC Silicon Valley, January 21-24, 2020
- Successful Genomics Programs – Research and Clinical: In this session, we will hear updates from several population sequencing studies and learn about their objectives, protocols, and challenges – chaired by Clara Lajonchere (UCLA Institute for Precision Health)
o Includes an overview of:
– AdventHealth ‘WholeMe study’ by Steven Smith,
– Renown Health ‘Healthy Nevada Project’ by Anthony D. Slonim,
– University of Vermont Health Network ‘1K patient genome sequencing study’ by Debra G.B. Leonard,
– Stanford University ‘Clinical Genomics Services’ by Euan Ashley, and
– All of Us Research Program by Chris Lunt
- Unique Population Study Collaborations: This panel will dive into unique partnerships for population studies that have recently been launched. The line-up includes partnerships among various medical organizations and pharmaceutical and direct-to-consumer testing companies – chaired by Aleks Rajkovic (UCSF)
o The panelists include Jennifer Low (23andMe), Anthony D. Slonim (Renown Health), Lincoln Nadauld (Intermountain Healthcare), and Olena Morozova Vaske (UCSC)
- A talk by Shannon Muir will focus on the California State Government Efforts in Precision Medicine where she will discuss ongoing and future initiatives, programs, and partnerships funded by the State of California to advance precision health and medicine
The program also touches on very specific, practical, and relevant aspects that need to be considered when planning and executing a population study: (FULL 400-Speaker Program including below talks/sessions)
SOURCE
From: <pmwcintl55686.activehosted.com@s4.asa1.acemsa3.com> on behalf of Tal Behar <tal.behar@pmwcint.com>
Date: Wednesday, December 4, 2019 at 9:12 AM
To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>
Subject: Population Studies: Enabling Individualized Disease Treatment, Care, and Prevention
Book Structure and Editorials
- Introduction to Part 1: NGS – Voice of Dr. Williams
ALL CONTENTS IN PART 1 represent articles from the Journal archive
- Summary to Part 1: NGS – Voice of Dr. Williams
- Introduction to Part 2: CRISPR – Voice of Dr. Williams
ALL CONTENTS IN PART 2 represent articles from the Journal archive
- Summary to Part 2: CRISPR – Voice of Dr. Williams
- Introduction to Part 3: AI in Medicine – Voice of Aviva Lev-Ari and Dr. Williams
ALL CONTENTS IN PART 3 represent articles from the archive and NEW curations on AI by Final Improvement Team (FIT) members.
3.5 on Machine Learning Algorithms in Medicine by Dr. Dror Nir
- Summary to Part 3: AI in Medicine – Voice of Aviva Lev-Ari and Dr. Williams
- Introduction to Part 4: Single Cell Genomics – Voice of Dr. Williams
ALL CONTENTS IN PART 4 represent article curations and reporting by Aviva Lev-Ari, PhD, RN
- Summary to Part 4: Single Cell Genomics – Voice of Dr. Williams
- Introduction to Part 5: Genomics Modeling in Evolution – Voice of Professor Feldman
ALL CONTENTS IN PART 5 will be written by Prof. Feldman on scientific findings in Feldman Lab @Stanford – ONLY already published work
- Summary to Part 5: Genomics Modeling in Evolution – Voice of Professor Feldman
- Introduction to Part 6: Simulation Modeling – Voice of Dr. Williams
ALL CONTENTS IN PART 6 represent a Bibliography of articles on TEN topics – The Bibliography was curated by Aviva Lev-Ari, PhD, RN
- Summary to Part 6: Simulation Modeling – Voice of Dr. Williams
- Introduction to Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases – Voice of Dr. Williams
ALL CONTENTS IN PART 7 – curations by Scientists on FIT
- Summary to Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases – Voice of Dr. Williams
- Introduction to Part 8: Epigenomics and Genomic Regulation – Voice of Dr. Williams
ALL CONTENTS IN PART 8 – curations by Scientists on FIT
- Summary to Part 8: Epigenomics and Genomic Regulation – Voice of Dr. Williams
Summary to Volume 2 – Voice of Aviva Lev-Ari and Professor Williams
Epilogue – Voice of Aviva Lev-Ari and Professor Williams
List of Contributors
Links to Bios
Part 1: Next Generation Sequencing
Introduction to Part 1: NGS – Voice of Dr. Williams
1.1 The Science
1.2 Technologies and Methodologies
1.3 Clinical Aspects
1.4 Business and Legal
Summary to Part 1: NGS – Voice of Dr. Williams
1.1.1.4, 1.1.1.5, 1.1.1.7, 1.1.1.9, 1.1.1.10, 1.1.1.12, 1.1.1.14, 1.1.1.15, 1.1.2.11, 1.1.2.12, 1.1.2.14, 1.1.2.15, 1.1.2.16, 1.1.2.17, 1.1.3.8, 1.1.3.9, 1.1.3.13, 1.1.3.15, 1.1.3.16, 1.1.3.17, 1.1.3.18, 1.1.3.19, 1.1.3.20, 1.1.3.21, 1.1.4.1, 1.2.1.14, 1.2.1.15, 1.2.1.17, 1.2.2.10, 1.2.2.13, 1.2.2.14, 1.2.2.15, 1.3.1.2, 1.3.3.2, 1.4.1.3, 1.4.1.4
1.1.1.2, 1.1.1.8, 1.1.1.11, 1.1.1.13, 1.1.1.16, 1.1.2.7, 1.1.2.9, 1.1.3.3, 1.1.3.4, 1.1.3.5, 1.1.3.6, 1.1.3.7, 1.1.3.11, 1.1.3.12, 1.1.3.14, 1.1.4.2, 1.2.1.2, 1.2.1.3, 1.2.1.6, 1.2.1.7, 1.2.1.8, 1.2.1.9, 1.2.1.10, 1.2.1.13, 1.2.2.2, 1.2.2.3, 1.2.2.4, 1.2.2.5, 1.2.2.6, 1.2.2.8, 1.2.2.9, 1.2.2.12, 1.2.2.16, 1.2.2.17, 1.2.2.18, 1.2.3.1, 1.2.3.2, 1.2.3.3, 1.2.3.4, 1.2.3.7, 1.2.3.8, 1.2.3.9, 1.2.3.10, 1.2.3.11, 1.2.4.2, 1.2.4.5, 1.3.1.1, 1.3.2.1, 1.3.3.1, 1.3.3.4, 1.3.3.5, 1.3.3.7, 1.3.3.8, 1.3.3.10, 1.3.3.11, 1.4.1.5, 1.4.2.2, 1.4.3.1, 1.4.3.2, 1.4.3.4, 1.4.3.5, 1.4.3.6, 1.4.3.7, 1.4.3.8, 1.4.4.1, 1.4.4.2
Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
1.1.1.3, 1.1.2.10, 1.2.3.5
1.1.2.13
1.1.1.1, 1.1.2.1, 1.1.2.2, 1.1.2.3, 1.1.2.4, 1.1.2.5, 1.1.2.6, 1.1.2.8
1.1.3.1, 1.1.3.2, 1.2.1.1, 1.2.1.4, 1.2.1.5, 1.2.1.16, 1.2.1.18, 1.2.2.1, 1.2.3.6, 1.2.4.3, 1.2.4.4, 1.3.3.6, 1.3.3.9, 1.4.1.2, 1.4.2.1, 1.4.4.3,
1.2.2.7
1.1.1.6, 1.4.3.3
Stephen J. Williams Ph.D. and Aviva Lev-Ari, PhD, RN
1.1.3.10
Guest Authors
Gil Press, MBA
1.2.1.11, 1.2.1.12
1.2.2.11
Kelly Pearlman, BSc, MSc(c)
1.2.4.1
Part 2: CRISPR for Gene Editing and DNA Repair
Introduction to Part 2: CRISPR – Voice of Professor Williams
2.1 The Science
2.2 Technologies and Methodologies
2.3 Clinical Aspects
2.4 Business and Legal
Summary to Part 2: CRISPR – Voice of Professor Williams
Larry H. Bernstein, MD, FCAP
2.1.1.6, 2.1.1.11, 2.1.1.12, 2.1.1.15, 2.1.1.16, 2.1.1.17, 2.1.1.18, 2.1.1.19, 2.1.1.20, 2.1.1.21, 2.1.2.2, 2.1.2.3, 2.1.2.6, 2.1.2.7, 2.1.2.8, 2.1.3.8, 2.1.3.9, 2.1.3.10, 2.1.3.11, 2.1.3.12, 2.1.3.13, 2.1.4.5, 2.1.4.6, 2.1.5.21, 2.1.5.22, 2.1.5.27, 2.2.5, 2.2.13, 2.2.14, 2.2.15, 2.2.16, 2.2.17, 2.2.18, 2.2.19, 2.2.20, 2.2.25, 2.3.6, 2.3.8
Aviva Lev-Ari, PhD, RN
2.1.1.1, 2.1.1.2, 2.1.1.3, 2.1.1.4, 2.1.1.5, 2.1.1.8, 2.1.1.22, 2.1.2.4, 2.1.2.5, 2.1.3.3, 2.1.3.4, 2.1.3.5, 2.1.3.6, 2.1.3.7, 2.1.3.14, 2.1.3.15, 2.1.4.1, 2.1.4.2, 2.1.4.3, 2.1.4.4, 2.1.5.1, 2.1.5.2, 2.1.5.3, 2.1.5.4, 2.1.5.6, 2.1.5.7, 2.1.5.8, 2.1.5.9, 2.1.5.10, 2.1.5.11, 2.1.5.12, 2.1.5.13, 2.1.5.14, 2.1.5.15, 2.1.5.16, 2.1.5.17, 2.1.5.18, 2.1.5.19, 2.1.5.20, 2.1.5.23, 2.1.5.24, 2.1.5.25, 2.2.1, 2.2.2, 2.2.3, 2.2.6, 2.2.7, 2.2.11, 2.2.12, 2.2.21, 2.2.22, 2.2.23, 2.2.24, 2.2.26, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.3.7, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.4.5, 2.4.6, 2.4.7, 2.4.8, 2.4.9, 2.4.10, 2.4.11, 2.4.12, 2.4.13, 2.4.14, 2.4.16, 2.4.17, 2.4.18, 2.4.19, 2.4.20
David Orchard-Webb, PhD and Aviva Lev-Ari, PhD, RN
2.1.1.10
Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
2.1.1.9, 2.2.4, 2.2.9, 2.2.10
Larry H Bernstein, MD, FCAP and Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN
2.1.3.1
Dr. Sudipta Saha, Ph.D.
2.1.1.7, 2.1.5.5
Stephen J. Williams, Ph.D.
Introduction Part 2, 2.1.1.13, 2.1.2.1, 2.1.5.26, 2.1.5.28, 2.2.8, 2.2.27, 2.3.5, 2.4.15, Summary Part 2
Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN
2.1.1.14, 2.1.3.2
Irina Robu, PhD
2.3.9
Part 3: AI in Medicine
Introduction to Part 3: AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams
3.1 The Science
3.2 Technologies and Methodologies
3.3 Clinical Aspects
3.4 Business and Legal
3.5 Latest in Machine Learning (ML) Algorithms harnessed for Medical Diagnosis: Pattern Recognition & Prediction of Disease Onset
Summary to Part 3: AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams
Larry H. Bernstein, MD, FCAP
3.2.3, 3.2.4, 3.2.5, 3.2.6, 3.3.11, 3.3.12, 3.3.16, 3.4.8, 3.4.10, 3.4.12
Aviva Lev-Ari, PhD, RN
3.1.1, 3.1.2, 3.1.3, 3.1.4, 3.1.5, 3.1.6, 3.1.7, 3.1.8, 3.2.1, 3.2.8, 3.3.5, 3.3.6, 3.3.9, 3.3.23, 3.4.1, 3.4.2, 3.4.4, 3.4.5, 3.4.6, 3.4.9, 3.4.12, 3.4.15, 3.5.1.1, 3.5.2.1, 3.5.2.4, 4.1.3, 4.1.6, 4.1.7, 4.2.2, 4.2.3, 4.2.4, 4.3.2
Stephen J Williams, PhD
3.2.2, 3.3.3, 3.3.4, 3.3.8, 3.3.20, 3.3.21, 3.4.7, 3.4.16
3.2.7, 3.3.22, 3.4.3, 3.5 Introduction, 3.5.2.2, 3.5.2.3, 3.5.2.5, 3.5.2.6
Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
3.3.2
Dr. Sudipta Saha, Ph.D.
3.3.7, 3.3.17, 3.3.18, 4.1.8
Irina Robu, PhD
3.3.10, 3.3.13, 3.3.14, 3.3.15, 3.3.19
Gail S. Thornton, MA
3.4.13, 3.4.14
Part 4: Single Cell Genomics
Introduction to Part 4: Single Cell Genomics – Voice of Professor Williams
4.1 The Science
4.2 Technologies and Methodologies
4.3 Clinical Aspects
4.4 Business and Legal
Summary to Part 4: Single Cell Genomics – Voice of Professor Williams
Aviva Lev-Ari, PhD, RN
4.1.3, 4.1.6, 4.1.7, 4.2.2, 4.2.3, 4.2.4, 4.3.2
Dr. Sudipta Saha, Ph.D.
4.1.8, 4.2.5, 4.3.6
Stephen J Williams, PhD
4.2.6, 4.3.7
Part 5: Evolution Biology Genomics Modeling @Feldman Lab, Stanford University
Written and Curated by Prof. Marc Feldman
Introduction to Part 5: Genomics Modeling in Evolution – Voice of Professor Feldman
5.1 Human Genomic Variation, Population Diversity, and Genome-Wide Associations
Author: Professor Feldman
Summary to Part 5: Genomics Modeling in Evolution – Voice of Professor Feldman
Part 6: Simulation Modeling in Genomics
Introduction to Part 6: Simulation Modeling – Voice of Professor Williams
ALL CONTENTS IN PART 6 represent a Bibliography of articles on TEN topics – The Bibliography was curated by Aviva Lev-Ari, PhD, RN – All contributors and Literature References – cited from the original publications
6.1 Mutation Analysis – Gene Encoding
6.2 Mitochondrial Variations
6.3 Variant Analysis
6.4 Variant Detection in Hereditary Cancer Genes
6.5 Immuno-Informatics
6.6 RNA Sequencing
6.7 Complex Insertions and Deletions
6.8 Evolutionary Biology
6.9 Simulation Programs
6.10 A comparison of tools for the simulation of genomic next-generation sequencing data
Summary to Part 6: Simulation Modeling – Voice of Professor Williams
Part 7: Applications of Genomics:
Genotypes, Phenotypes and Complex Diseases
Introduction to Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases – Voice of Professor Williams
7.1 Genome-wide associations with complex diseases (GWAS)
7.2 Non-coding DNA and phenotypes—including diseases like cancer
7.3 Transcriptomic and ‘omic associations with phenotypes including cancer and rare variant diseases
7.4 Applications of Bioinformatic Analysis of ‘Omic Data
7.5 Population-level genomics and the meaning of group differences
7.6 Targeting drugs for complex diseases
Summary to Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases – Voice of Professor Williams
Stephen J Williams, PhD
7.1.2, 7.3, 7.3.2, 7.3.3, 7.4.1, 7.4.2, 7.4.3, 7.5.5, 7.6.2
Sudipta Saha, PhD
7.3.1, 7.5.3
Aviva Lev-Ari, PhD, RN
7.1.3, 7.3.4, 7.5.4, 7.6.1
Larry H. Bernstein, MD, FCAP
7.2.1, 7.2.2, 7.2.3, 7.5.2
Marcus W. Feldman, PhD
7.5.1
Kelly Perlman
7.1.1
Part 8: Epigenomics and Genomic Regulation
Introduction to Part 8: Epigenomics and Genomic Regulation – Voice of Dr. Williams
8.1 Genomic controls on epigenomics
8.2 The ENCODE project and gene regulation
8.3 Small interfering RNAs and gene expression
8.4 Epigenomics in cancer
8.5 Environmental epigenomics
Summary to Part 8: Epigenomics and Genomic Regulation – Voice of Dr. Williams
Aviva Lev-Ari, PhD, RN
8.2.2, 8.2.6, 8.3.4, 8.3.5, 8.4.3, 8.5.4
Stephen J Williams, PhD
8.2.1, 8.3.1, 8.5.1, 8.5.2, 8.5.3
Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
8.1.1, 8.3.2, 8.4.1, 8.4.2, 8.4.4
Dr. Sudipta Saha, Ph.D.
8.2.5, 8.3.3, 8.5.4
Marcus W. Feldman, PhD
8.5.4
Guest Authors
8.2.3
8.2.4
Summary to Volume 2 – Voice of Aviva Lev-Ari and Stephen Williams
Epilogue – Voice of Aviva Lev-Ari and Stephen Williams
Articles and Events of Note on NGS @pharmaceuticalintelligence.com
Curator: Larry H. Bernstein, MD, FCAP
Curator: Larry H. Bernstein, MD, FCAP
- CRISPR-Cas9 and gene editing: Medical Interpretation of Gene Editing Technology for New Therapeutics
Authors and Curators: Larry H Bernstein, MD, FCAP and Stephen J Williams, PhD and Curator: Aviva Lev-Ari, PhD, RN
Reporter: Aviva Lev-Ari, PhD, RN
Curator: Aviva Lev-Ari, PhD, RN
Reporter and writer: Larry H Bernstein, MD, FCAP
Reporter: Aviva Lev-Ari, PhD, RN
Reporter: Aviva Lev-Ari, PhD, RN
Reporter: Aviva Lev-Ari, PhD, RN
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
Reporter/Curator: Stephen J. Williams, Ph.D.
Reporter: Stephen J. Williams, PhD @StephenJWillia2
Real Time Press Coverage: Aviva Lev-Ari, PhD, RN
Leading Genomics Platforms and Diagnostics Genomics-based Applications
The future is created by you. Experience NextSeq 2000.
The NextSeq 1000 and NextSeq 2000 Sequencing Systems are groundbreaking benchtop sequencers that allow you to explore new science across a variety of current and emerging applications, with higher efficiency and fewer restraints.
https://www.illumina.com/systems/sequencing-platforms/nextseq-1000-2000.html
http://www.illumina.com/technology/next-generation-sequencing.html
iSeq 100 System |
MiniSeq System |
MiSeq Series |
NextSeq Series |
|
|---|---|---|---|---|
|
Popular Applications & Methods |
Key Application | Key Application | Key Application | Key Application |
| Large Whole-Genome Sequencing (human, plant, animal) | ||||
| Small Whole-Genome Sequencing (microbe, virus) | ||||
| Exome Sequencing | ||||
| Targeted Gene Sequencing (amplicon, gene panel) | ||||
| Whole-Transcriptome Sequencing | ||||
| Gene Expression Profiling with mRNA-Seq | ||||
| Targeted Gene Expression Profiling | ||||
| Long-Range Amplicon Sequencing* | ||||
| miRNA & Small RNA Analysis | ||||
| DNA-Protein Interaction Analysis | ||||
| Methylation Sequencing | ||||
| 16S Metagenomic Sequencing |
SOURCE
https://www.illumina.com/systems/sequencing-platforms.html
http://www.veladx.com/solutions/next-generation-sequencing.html
http://www.genewiz.com/public/NGS_seq.aspx?gclid=CIa2183iz8sCFdcagQodYhsHOg
Part 1: Next Generation Sequencing (NGS)
Introduction to Part 1: NGS – Voice of Professor Williams
1.1 The NGS Science
1.1.1 BioIT Aspects
1.1.1.1 International Award for Human Genome Project
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
https://pharmaceuticalintelligence.com/2018/02/09/international-award-for-human-genome-project/
1.1.1.2 Cracking the Genome – Inside the Race to Unlock Human DNA – quotes in newspapers
Reporter: Aviva Lev-Ari, PhD, RN
1.1.1.3 mRNA Data Survival Analysis
Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2016/06/18/mrna-data-survival-analysis/
1.1.1.4 Novel Discoveries in Molecular Biology and Biomedical Science
Curator: Larry H. Bernstein, MD, FCAP
1.1.1.5 Switching on genes
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/05/19/switching-on-genes/
1.1.1.6 The Role of Big Data in Medicine
Author: Gail S. Thornton, M.A.
https://pharmaceuticalintelligence.com/2016/05/16/the-role-of-big-data-in-medicine/
1.1.1.7 Disease related changes in proteomics, protein folding, protein-protein interaction
Curator: Larry H. Bernstein, MD, FCAP
1.1.1.8 Bio-IT World 2016 – Reception with Dr. Howard Jacob – Aviva Lev-Ari, PhD, RN will attend
Reporter: Aviva Lev-Ari, PhD, RN
1.1.1.9 How do we address medical diagnostic errors?
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/03/26/how-do-we-address-medical-diagnostic-errors/
1.1.1.10 DNA and Origami
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/03/17/dna-and-origami/
1.1.1.11 Phenotypic Screening must evolve to ensure successful Drug Development
Reporter: Aviva Lev-Ari, PhD, RN
1.1.1.12 3-D visualization of cancer cells
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/02/28/3-d-visualization-of-cancer-cells/
1.1.1.13 Leadership in Genomics: VarElect – Variants in Disease and UCSC Genome Technology Center
Reporter: Aviva Lev-Ari, PhD, RN
1.1.1.14 Signaling of Immune Response in Colon Cancer
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/01/09/signaling-of-immune-response-in-colon-cancer/
1.1.1.15 Periodic table of protein complexes
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/01/09/periodic-table-of-protein-complexes/
1.1.1.16 AGENDA for Oligonucleotide Therapeutics and Delivery, April 4-5, 2016, HYATT Hotel, Cambridge, MA
Reporter: Aviva Lev-Ari, PhD, RN
1.1.2 BioInformatics-NGS
1.1.2.1 Extracellular RNA and their carriers in disease diagnosis and therapy
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
1.1.2.2 Gender affects the prevalence of the cancer type
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
https://pharmaceuticalintelligence.com/2019/04/02/gender-affects-the-prevalence-of-the-cancer-type/
1.1.2.3 Pancreatic cancer survival is determined by ratio of two enzymes
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
1.1.2.4 Immuno-editing can be a constant defense in the cancer landscape
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
1.1.2.5 Immunotherapy may help in glioblastoma survival
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
https://pharmaceuticalintelligence.com/2019/03/16/immunotherapy-may-help-in-glioblastoma-survival/
1.1.2.6 Knowing the genetic vulnerability of bladder cancer for therapeutic intervention
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
1.1.2.7 SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke
Reporter: Aviva Lev-Ari, PhD, RN
1.1.2.8 Sperm Analysis by Smart Phone
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
https://pharmaceuticalintelligence.com/2017/03/29/sperm-analysis-by-smart-phone/
1.1.2.9 Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology
Reporter: Aviva Lev-Ari, PhD, RN
1.1.2.10 Etiologies of Cardiovascular Diseases: Epigenetics, Genetics and Genomics: Request for Book Review Writing on Amazon.com
Editors: Larry H. Bernstein and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2016/09/11/request-for-book-review-writing-on-amazon-com/
1.1.2.11 Cancer detection and therapeutics
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/05/02/cancer-detection-and-therapeutics/
1.1.2.12 Pull at Cancer’s Levers
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/02/pull-at-cancers-levers/
1.1.2.13 The world’s most innovative intersection
Reported by: Irina Robu
https://pharmaceuticalintelligence.com/2016/01/02/the-worlds-most-innovative-intersection/
1.1.2.14 Complex Cancer Genetics Testing
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/10/complex-cancer-genetics-testing/
1.1.2.15 The Need for an Informatics Solution in Translational Medicine
Curator: Larry H. Bernstein, MD, FCAP
1.1.2.16 Human Genetics and Childhood Diseases
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/03/human-genetics-and-childhood-diseases/
1.1.2.17 GEN Tech Focus: Rethinking Gene Expression Analysis
Curator: Larry H. Bernstein, MD, FCAP
1.1.3 Computation Biology
1.1.3.1 BioInformatic Resources at the Environmental Protection Agency: Tools and Webinars on Toxicity Prediction
Curator: Stephen J. Williams Ph.D.
1.1.3.2 Live Conference Coverage @MedCity news Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing
Reporter: Stephen J. Williams, Ph.D.
1.1.3.3 DISCUSSION – Genomics-driven personalized medicine for Pancreatic Cancer
Reporter: Aviva Lev-Ari, PhD, RN
1.1.3.4 CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to anyone on Earth
Reporter: Aviva Lev-Ari, PhD, RN
1.1.3.5 Innovative Gene Families for exploring patterns of Genetic Families applied by Craig Venter’s Team in Deeply Sequencing 10,500 Genomes: an average of 8,579 novel variants found per person –Intolerant sites, might be essential for life or health.
Reporter: Aviva Lev-Ari, PhD, RN
1.1.3.6 First challenge to make use of the new NCI Cloud Pilots – Somatic Mutation Challenge – RNA: Best algorithms for detecting all of the abnormal RNA molecules in a cancer cell
Reporter: Aviva Lev-Ari, PhD, RN
1.1.3.7 Somatic Mutation Theory – Why it’s Wrong for Most Cancers
Reporter: Aviva Lev-Ari, PhD, RN
1.1.3.8 Genomics and epigenetics link to DNA structure
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/11/genomics-and-epigenetics-link-to-dna-structure
1.1.3.9 3-D molecular structures
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/02/28/3-d-molecular-structures/
1.1.3.10 Correspondence on Leadership in Genomics and other Gene Curations: Dr. Williams with Dr. Lev-Ari
Authors: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN
1.1.3.11 Leadership in Genomics: VarElect – Variants in Disease and UCSC Genome Technology Center
Reporter: Aviva Lev-Ari, PhD, RN
1.1.3.12 Gene Editing for Exon 51: Why CRISPR Snipping might be better than Exon Skipping for DMD
Reporter: Aviva Lev-Ari, PhD, RN
1.1.3.13 Periodic Table of Protein Complexes
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/01/09/periodic-table-of-protein-complexes/
1.1.3.14 Genomics’ Proprietary Statistical Analysis Tools and Integrated Multi-Phenotype Database to be used to Support Research and Development at Vertex Pharmaceuticals
Reporter: Aviva Lev-Ari, PhD, RN
1.1.3.15 N3xt generation carbon nanotubes
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/12/14/n3xt-generation-carbon-nanotubes/
1.1.3.16 Biochemistry and Dysmetabolism of Aging and Serious Illness
Curator: Larry H. Bernstein, MD, FCAP
1.1.3.17 Identifying Cancers and Resistance
Curator: Larry H/ Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/18/identifying-cancers-and-resistance/
1.1.3.18 Complexity of Protein-Protein Interactions
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/12/protein-protein-interactions/
1.1.3.19 Variability of Gene Expression and Drug Resistance
Curator: Larry H. Bernstein, MD, FCAP
1.1.3.20 Sequence the Human Genome
Curator: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/11/sequence-the-human-genome/
1.1.3.21 Single Nucleotide Repair and Tunable DNA-directed Assembly of Nanomaterials
Curator: Larry H. Bernstein, MD, FCAP
1.1.4 NGS – Discoveries in Medicine derived from Advanced Sequencing
1.1.4.1 Genomic relationship between autism and bipolar disorder
Curator: Larry H. Bernstein, MD, FCAP
1.1.4.2 Sequencing yourself! and Learn more on Genome Sequencing on Tuesday, November 17, 2015 from 8am-5pm in the Joseph B. Martin Conference Center of the Harvard New Research Building at Harvard Medical School
Reporter: Aviva Lev-Ari, PhD, RN
1.2 Technologies and Methodologies
1.2.1 BioIT
1.2.1.1 A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information
Reporter: Stephen J. Williams, Ph.D.
1.2.1.2 18th Annual 2019 BioIT, Conference & Expo, April 16-18, 2019, Boston, Seaport World Trade Center, Track 5 Next-Gen Sequencing Informatics – Advances in Large-Scale Computing
Real Time Coverage with Social Media: Aviva Lev-Ari, PhD, RN
1.2.1.3 2019 Koch Institute Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA
Real Time Coverage with Social Media: Aviva Lev-Ari, PhD, RN
1.2.1.4 Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare
Curator: Stephen J. Williams, Ph.D.
1.2.1.5 Bioinformatics Tool Review: Genome Variant Analysis Tools
Curator: Stephen J. Williams, Ph.D.
1.2.1.6 2018 CHI’s BioIT World conference THURSDAY, MAY 17 | 8:00 – 9:45 AM – Awards and Keynote
Real Time Coverage with Social Media: Aviva Lev-Ari, PhD, RN
1.2.1.7 Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)
Reporter: Aviva Lev-Ari, PhD, RN
1.2.1.8 Synopsis Track 7: NGS in Real Time @pharma_BI 2018 CHI’s BioIT World conference & Expo, May 15 – 17, 2018, Boston, MA – Seaport World Trade Center
Reporter: Aviva Lev-Ari, PhD, RN
1.2.1.9 2017 Agenda – BioInformatics: Track 6: BioIT World Conference & Expo ’17, May 23-35, 2017, Seaport World Trade Center, Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
1.2.1.10 The 16th annual EmTech MIT – A Place of Inspiration, October 18-20, 2016, Cambridge, MA
Real Time coverage with Social Media: Aviva Lev-Ari, PhD, RN
1.2.1.11 10 Most Successful Big Data Technologies
Guest Author: Gil Press
https://pharmaceuticalintelligence.com/2016/04/25/10-most-successful-big-data-technologies/
1.2.1.12 Big Data Self-Delusion
Guest Author: Gil Press
https://pharmaceuticalintelligence.com/2016/04/02/big-data-self-delusion/
1.2.1.13 Top 100 Big Data Experts to Follow
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2016/01/20/top-100-big-data-experts-to-follow/
1.2.1.14 Crystal Resolution in Raman Spetctoscopy for Pharmaceutical Analysis
Curator: Larry H. Bernstein, MD, FCAP
1.2.1.15 Imaging of Cancer Cells
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/20/imaging-of-cancer-cells/
1.2.1.16 Bioinformatic Tools for Cancer Mutational Analysis: COSMIC and Beyond
Curator: Stephen J. Williams, Ph.D.
1.2.1.17 Laboratory Automation Today
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/23/laboratory-automation-today/
1.2.1.18 Bioinformatics Tool Review: Genome Variant Analysis Tools
Curator: Stephen J. Williams, Ph.D.
1.2.2 BioInformatics – NGS
1.2.2.1 A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information
Reporter: Stephen J. Williams, Ph.D.
1.2.2.2 18th Annual 2019 BioIT, Conference & Expo, April 16-18, 2019, Boston, Seaport World Trade Center, Track 5 Next-Gen Sequencing Informatics – Advances in Large-Scale Computing
Reporter: Aviva Lev-Ari, PhD, RN
1.2.2.3 Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL
Author and Curator: Aviva Lev-Ari, PhD, RN
1.2.2.4 Synopsis Track 7: NGS in Real Time @pharma_BI 2018 CHI’s BioIT World conference & Expo, May 15 – 17, 2018, Boston, MA – Seaport World Trade Center
Real Time Reporter: Aviva Lev-Ari, PhD, RN
1.2.2.5 The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics
Reporter: Aviva Lev-Ari, PhD, RN
1.2.2.6 2017 Agenda – BioInformatics: Track 6: BioIT World Conference & Expo ’17, May 23-35, 2017, Seaport World Trade Center, Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
1.2.2.7 A novel 5-gene pancreatic adenocarcinoma classifier: Meta-analysis of transcriptome data – Clinical Genomics Research @BIDMC
Curator: Tilda Barliya, PhD
1.2.2.8 Recap of Bio-IT World 2016 by Sanjay Joshi CTO, Healthcare & Life Sciences, EMC Emerging Technologies Division
Reporter: Aviva Lev-Ari, PhD, RN
1.2.2.9 Genome Analysis Toolkit (GATK) the Industry Standard will govern the New Tools in Biomedical Research by the Collaboration of Broad Institute and Intel
Curator: Aviva Lev-Ari, PhD, RN
1.2.2.10 Curbing Cancer Cell Growth & Metastasis-on-a-Chip’ Models Cancer’s Spread
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/03/19/curbing-cancer-cell-growth/
1.2.2.11 Simulation of DNA Sequencing through Graphene Nanopore
Reporter: Danut Dragoi, PhD
1.2.2.12 2016 BioIT World: Track 5 – April 5 – 7, 2016 Bioinformatics Computational Resources and Tools to Turn Big Data into Smart Data
Reporter: Aviva Lev-Ari, PhD, RN
1.2.2.13 Biomarker Development
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/16/biomarker-development/
1.2.2.14 Genetically Engineered Algae
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/15/genetically-engineered-algae/
1.2.2.15 Sequence the Human Genome
Curator: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/11/sequence-the-human-genome/
1.2.2.16 Atul Butte TALK on YouTube on Big Data, Open Data and Clinical Trials
Reporter: Aviva Lev-Ari, PhD
1.2.2.17 How to identify Genes associated with Genetic Diseases and Cancer: A Phylogenetic Profiling Evolutionary Approach @ HUJI
Reporter: Aviva Lev-Ari, PhD, RN
1.2.2.18 Tweets by @pharma_BI at 2015 BioIT, Boston, 4/21/2015- 4/23/2015
Live Press Coverage: Aviva Lev-Ari, PhD, RN
1.2.3 Computation Biology
1.2.3.1 A New Computational Method illuminates the Heterogeneity and Evolutionary Histories of cells within a Tumor
Reporter: Aviva Lev-Ari, PhD, RN
1.2.3.2 Through Data Science: Stanford Medicine and Google will transform Patient Care and Medical Research
Reporter: Aviva Lev-Ari, PhD, RN
1.2.3.3 A New Potential Target for Pancreatic Cancer Treatment: Rapid Screening Technique finds Gene Defending Tumors from DNA Damage @M. D. Anderson Cancer Center
Reporter: Aviva Lev-Ari, PhD, RN
1.2.3.4 Deep Learning for In-silico Drug Discovery and Drug Repurposing: Artificial Intelligence to search for molecules boosting response rates in Cancer Immunotherapy: Insilico Medicine @John Hopkins University
Reporter: Aviva Lev-Ari, PhD, RN
1.2.3.5 Gene Editing: The Role of Oligonucleotide Chips
Curators: Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2016/01/07/gene-editing-the-role-of-oligonucleotide-chips/
1.2.3.6 How Will FDA’s new precision FDA Science 2.0 Collaboration Platform Protect Data?
Reporter: Stephen J. Williams, Ph.D.
How Will FDA’s new precisionFDA Science2.0 Collaboration Platform Protect Data
1.2.3.7 Computer Aided Design
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/17/computer-aided-design/
1.2.3.8 Genomic Pathogen Typing
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/16/genomic-pathogen-typing/
1.2.3.9 Best Big Data?
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/16/best-big-data/
1.2.3.10 Better Bioinformatics
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/09/better-bioinformatics/
1.2.3.11 Healthcare conglomeration to access Big Data and lower costs
Curator: Larry H. Bernstein, MD, FCAP
1.2.4 NGS – Discoveries Driven by Advanced Sequencing
1.2.4.1 Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants
Reporter: Kelly Perlman, Life Sciences Student and Research Assistant, McGill University
1.2.4.2 Using Online Mendelian Inheritance in Man (OMIM) database and the Human Genome Mutation Database (HGMD) Pro 2015.2 for Quantification of the growth in gene-disease and variant-disease associations
Reporter: Aviva Lev-Ari, PhD, RN
1.2.4.3 Roche is developing a high-throughput low cost sequencer for NGS, How NGS Will Revolutionize Reproductive Diagnostics: November Meeting, Boston MA
Reporter: Stephen J. Williams, PhD
1.2.4.4 How NGS Will Revolutionize Reproductive Diagnostics: November Meeting, Boston MA
Reporter: Stephen J. Williams, PhD
1.2.4.5 LIVE Plenary Session 2015 BioIT, April 21, 2015, 4:00 – 5:00PM – Cambridge HealthTech Institute’s 14th Annual Meeting BioIT World – Conference & Expo ’15, April 21 – 23, 2015 @Seaport World Trade Center, Boston, MA
Real Time Reporter: Dr. Aviva Lev-Ari will be in attendance on April 21, 22, 23
1.3 NGS – Clinical Aspects
1.3.1 BioInformatics – NGS
1.3.1.1 Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between the National Institute of Standards & Technology (NIST) and Stanford University.
Reporter: Aviva Lev-Ari, PhD, RN
1.3.1.2 Transparency in Clinical Trials
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/09/transparency-in-clinical-trials/
1.3.2 Computation Biology & NGS
1.3.2.1 Topical Solution for Combination Oncology Drug Therapy: Patch that delivers Drug, Gene, and Light-based Therapy to Tumor
Reporter: Aviva Lev-Ari, PhD, RN
1.3.3 NGS – Clinical Aspects
1.3.3.1 New NGS Guidances for Laboratory Developed Tests (LDT): FDA’s Liz Mansfield on Audio Podcast
Reporter: Aviva Lev-Ari, PhD, RN
1.3.3.2 Next Generation Sequencing in Clinical Laboratory
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/03/22/next-generation-sequencing-in-clinical-laboratory/
1.3.3.3 First Cost-Effectiveness Study of Multi-Gene Panel Sequencing in Advanced Non-Small Cell Lung Cancer Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap
Guest Author: Press Release by Personalized Medicine Coalition
1.3.3.4 2019 Warren Alpert Foundation Award goes to Four Scientists for Seminal Discoveries in OptoGenetics – Illuminating the Human Brain
Reporter: Aviva Lev-Ari, PhD, RN
1.3.3.5 Broad@15 – In 2004, the Broad Institute of MIT and Harvard launched with a mission to improve human health
Reporter: Aviva Lev-Ari, PhD, RN
1.3.3.6 New Mutant KRAS Inhibitors Are Showing Promise in Cancer Clinical Trials: Hope For the Once ‘Undruggable’ Target
Curator: Stephen J. Williams, Ph.D.
1.3.3.7 eProceedings 15th Annual Personalized Medicine Conference at Harvard Medical School – THE PARADIGM EVOLVES, November 13 – 14, 2019 • Harvard Medical School, Boston, MA
Real Time Press Coverage: Aviva Lev-Ari, PhD, RN
1.3.3.8 Tweets and Retweets by @AVIVA1950 and by @pharma_BI for 15th Annual Personalized Medicine Conference at Harvard Medical School – THE PARADIGM EVOLVES, November 13 – 14, 2019 • Harvard Medical School, Boston, MA
Real Time Press Coverage: Aviva Lev-Ari, PhD, RN
1.3.3.9 Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line
Reporter: Stephen J. Williams, PhD
1.3.3.10 eProceedings – Day 1: Charles River Laboratories – 3rd World Congress, Delivering Therapies to the Clinic Faster, September 23 – 24, 2019, 25 Edwin H. Land Boulevard, Cambridge, MA
Reporter: Aviva Lev-Ari, PhD, RN
1.3.3.11 Genetic Testing in CVD and Precision Medicine
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2019/12/16/genetic-testing-in-cvd-and-precision-medicine/
1.4 Business and Legal
1.4.1 BioIT
1.4.1.1 Japan’s National Cancer Center Adopts Qiagen Bioinformatics Platform for Precision Med Program
1.4.1.2 37th Annual J.P. Morgan HEALTHCARE CONFERENCE: #JPM2019 for Jan. 8, 2019; Opening Videos, Novartis expands Cell Therapies, January 7 – 10, 2019, Westin St. Francis Hotel | San Francisco, California
Reporter: Stephen J. Williams, PhD
1.4.1.3 Healthcare conglomeration to access Big Data and lower costs
Curator: Larry H. Bernstein, MD, FCAP
1.4.1.4 Future of Big Data for Societal Transformation
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/12/14/future-of-big-data-for-societal-transformation/
1.4.1.5 Avvinity will have exclusive rights in oncology to use Alphamer therapeutic platform, invented by a Nobel Laureate and developed by Centauri: A Case of a Joint Venture Model
Reporter: Aviva Lev-Ari, PhD, RN
1.4.2 Bioinformatics – NGS
1.4.2.1 Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?
Reporter: Stephen J. Williams, Ph.D.
1.4.2.2 TSUNAMI in HealthCare under the New Name Verily.com
Curator: Aviva Lev-Ari, PhD, RN
1.4.3 Computation Biology
1.4.3.1 Convergence of Biology, Medicine, and Computing: Biomedical Informatics Entrepreneurs Salon (BIES), HMS, 2/7/19, 4:30 – 6:30PM
Real Time Reporter: Aviva Lev-Ari, PhD, RN
1.4.3.2 On its way for an IPO: mRNA platform, Moderna, Immune Oncology is recruiting 100 new Life Scientists in Cambridge, MA
Curator: Aviva Lev-Ari, PhD, RN
1.4.3.3 #JPM19 Conference: Lilly Announces Agreement To Acquire Loxo Oncology
Reporter: Gail S. Thornton
1.4.3.4 JP Morgan Healthcare Day Two: Thermo Fisher; Qiagen; Danaher; Counsyl; Human Longevity; Adaptive Bio, 10X Genomics and Pacific Biosciences
Reporter: Aviva Lev-Ari, PhD, RN
1.4.3.5 Day One at #JPM16: Breakout sessions of 23andMe, Myriad Genetics, Genomic Health, and Alere
Reporter: Aviva Lev-Ari, PhD, RN
1.4.3.6 #JPM16: Illumina’s CEO on new genotyping array called Infinium XT and Bio-Rad Partnership for single-cell sequencing workflow
Reporter: Aviva Lev-Ari, PhD, RN
1.4.3.7 Juno Acquires AbVitro for $125M: high-throughput and single-cell sequencing capabilities for Immune-Oncology Drug Discovery
Reporter: Aviva Lev-Ari, PhD, RN
1.4.3.8 #JPM16: Illumina’s CEO on new genotyping array called Infinium XT and Bio-Rad Partnership for single-cell sequencing workflow
Reporter: Aviva Lev-Ari, PhD, RN
1.4.4 NGS
1.4.4.1 QIAGEN – International Leader in NGS and RNA Sequencing
Reporter: Aviva Lev-Ari, PhD, RN
1.4.4.2 Four patents and one patent application on Nanopore Sequencing and methods of trapping a molecule in a nanopore assigned to Genia, is been claimed in a Law Suit by The Regents of the University of California, should be assigned to UCSC
Reporter: Aviva Lev-Ari, PhD, RN
1.4.4.3 Invivoscribe, Thermo Fisher Ink Cancer Dx Development Deal
Reporter: Stephen J. Williams, PhD
Part 1: Summary on NGS – Voice of Professor Williams
Part 2: CRISPR for Gene Editing and DNA Repair
Introduction to Part 2: CRISPR – Voice of Professor Williams
CRISPR Jumps in New Direction – Two recent papers describe RNA-guided DNA integration using CRISPR associated transposases, Julianna LeMieux, PhD, June 12, 2019
- a new paradigm of RNA-guided, DNA targeting and inserting
- repurposed CRISPR–Cas systems for RNA-guided DNA insertion
Two papers, one published today in Nature and the other last week in Science, describe a new mechanism in which bacterial transposons associate with CRISPR-Cas systems to allow for RNA-guided DNA insertion. Both papers show that a stolen CRISPR-Cas system can use its own guide RNA (gRNA) to recognize protospacers and, instead of cutting them by making a double-strand break, transpose adjacent to them.
The Two Papers
- “Transposon-encoded CRISPR-Cas systems direct RNA-guided DNA integration,” is from a team of graduate students in the lab of Samuel Sternberg, PhD, an assistant professor at Columbia University in the department of biochemistry and molecular biophysics.
- “RNA-guided DNA insertion with CRISPR-associated transposases,” from the group of Feng Zhang, PhD, at MIT and the Broad Institute – This study is reported in this chapter in this book.
Groundwork set in 2017
- Both paper prove a hypothesis laid out two years ago by Joe Peters, PhD, professor in the department of microbiology at Cornell University and Eugene Koonin, PhD, senior investigator in the evolutionary genomics research group at the National Center for Biotechnology Information (NCBI). In their 2017 PNAS paper, “Recruitment of CRISPR-Cas systems by Tn7-like transposons Joe Peters and Eugene Koonin were the first to connect the dots: Bacterial Transposons and CRISPR are linked.
Commentary
- Gaétan Burgio, MD, PhD, group leader in the department of immunology and infectious disease at the Australian National University: “They investigated the biology of how the RNA-guided DNA targeting Cascade complex (Type I CRISPR) interacts with the transposition system (a protein called TniQ) in integrating the DNA, coupling the DNA targeting and inserting pathways, and harnessed this mechanism as a gene editing tool,”
https://www.genengnews.com/featured/crispr-jumps-in-new-direction/?utm_medium=newsletter
FIVE Forthcoming Books on CRISPR in 2019-2020: Flooded market or CRISPR-fatigued readers – Not to Worry!!!!!
Author: Aviva Lev-Ari, PhD, RN
Tutorial to CRISPR
CRISPR+101CRISPR+101
PDF download is slow, it is coming and the reading is important.
2.1 CRISPR – Aspects of the Science
2.1.1 Basic Biochemical Issues
2.1.1.1 Breakthrough in Gene Editing CRISPR–Cas systems: First example of a fully programmable, RNA-guided integrase and lays the foundation for genomic manipulations that obviate the requirements for double-strand breaks and homology-directed repair.
Reporter: Aviva Lev-Ari, PhD, RN
2.1.1.2 Alternative to CRISPR/Cas9 – CAST (CRISPR-associated transposase) – A New Gene-editing Approach for Insertion of Large DNA Sequences into a Genome developed @BroadInstitute @MIT @Harvard
Reporter: Aviva Lev-Ari, PhD, RN
2.1.1.3 Innovations on the CRISPR System for Gene Editing: (1) Cryo-electron microscopy-based visualization of Cas3 Enzyme Cleavage (2) New tool testing an entire genome against a CRISPR molecule to predict potential errors and interactions
Curator and Reporter: Aviva Lev-Ari, PhD, RN
2.1.1.4 Researchers at Dana-Farber/Boston Children’s: Differences in wiring of “exhausted” and effective T cells indicate possible gene-editing targets
Reporter: Aviva Lev-Ari, PhD, RN
2.1.1.5 “CRISPR-Cas9, bring me a gene”, Encoding for a specific protein: Three words: CRISPR. A Capella
Reporter: Aviva Lev-Ari, PhD, RN
2.1.1.6 Novel Discoveries in Molecular Biology and Biomedical Science
Curator: Larry H. Bernstein, MD, FCAP
2.1.1.7 A Genetic Switch to Control Female Sexual Behavior
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
2.1.1.8 Preliminary Agenda Available and Exclusive Discount to attend Understanding CRISPR: Mechanisms to Applications Symposium in Boston (September 19, 2016)
Reporter: Aviva Lev-Ari, PhD, RN
2.1.1.9 Gene Editing with CRISPR gets Crisper
Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2016/05/03/gene-editing-with-crispr-gets-crisper/
2.1.1.10 CRISPR-Cas9 Screening by Horizon Discovery, Cambridge, UK – HDx™ Reference Standards
Reporters: David Orchard-Webb, PhD and Aviva Lev-Ari, PhD, RN
2.1.1.11 Recent Progress in Gene Editing Error Reduction
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/21/recent-progress-in-gene-editing-error-reduction/
2.1.1.12 CRISPR/Cas9 and HIV1
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/16/crisprcas9-and-hiv1/
2.1.1.13 Rice University researches develop new CRISPR-CAS9 strategy to reduce off-target gene editing effects
Reporter: Stephen J. Williams
2.1.1.14 @MIT: New delivery method boosts efficiency of CRISPR genome-editing system
Reporters: Aviva Lev-Ari, PhD, RN and Stephen J Williams, PhD
2.1.1.15 Alternative CRISPR discovered @MIT
Reporter: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/02/15/alternative-crispr-discovered/
2.1.1.16 Shortened Time for Cell Renewal
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/12/15/shortened-time-for-cell-renewal/
2.1.1.17 Turning CRISPR/Cas9 On or Off
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/21/turning-crisprcas9-on-or-off/
2.1.1.18 Cell Death Pathway Insights
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/09/cell-death-pathway-insights/
2.1.1.19 Gene Silencing
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/05/gene-silencing/
2.1.1.20 New CRISPR-non Cas9 proteins
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/10/27/new-crispr-non-cas9-proteins/
2.1.1.21 Regulatory DNA Engineered
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/02/11/regulatory-dna-engineered/
2.1.1.22 At Technical University of Munich (TUM) Successful Genetical modification of a patient’s own immune cells, T cell receptors, using CRISPR-Cas9 gene editing tool. The engineered T cells are very similar to the physiological immune cells.
Reporter: Aviva Lev-Ari, PhD, RN
2.1.2 Drug Discovery
2.1.2.1 CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development
Curator: Stephen J. Williams, Ph.D.
2.1.2.2 Delineating a Role for CRISPR-Cas9 in Pharmaceutical Targeting
Author & Curator: Larry H. Bernstein, MD, FCAP, Chief Scientific Officer, Leaders in Pharmaceutical Intelligence (LPBI) Group, Boston, MA
2.1.2.3 Where is the most promising avenue to success in Pharmaceuticals with CRISPR-Cas9?
Author: Larry H. Bernstein, MD, FCAP
2.1.2.4 Use of CRISPR & RNAi for Drug Discovery, CHI’s World PreClinical Congress – Europe, November 14-15, 2016, Lisbon, Portugal
Reporter: Aviva Lev-Ari, PhD, RN
2.1.2.5 2nd Annual Translational Gene Editing: Exploiting CRISPR/Cas9 for Building Tools for Drug Discovery & Development: June 16, 2016, Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
2.1.2.6 Intestinal Inflammatory Pharmaceutics
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/02/11/intestinal-inflammatory-pharmaceutics/
2.1.2.7 Disease Disablers
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/23/disease-disablers/
2.1.2.8 Gene-Silencing and Gene-Disabling in Pharmaceutical Development
Curator: Larry H. Bernstein, MD, FCAP
2.1.3 CRISPR as a Therapeutics Modality
2.1.3.1 UPDATED – Medical Interpretation of the Genomics Frontier – CRISPR – Cas9: Gene Editing Technology for New Therapeutics
Authors and Curators: Larry H Bernstein, MD, FCAP and Stephen J Williams, PhD and Curator: Aviva Lev-Ari, PhD, RN
2.1.3.2 Advances in Gene Editing Technology: New Gene Therapy Options in Personalized Medicine
Curators: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN
- Recent Advances in Gene Editing Technology Adds New Therapeutic Potential for the Genomic Era
Author and Curator: Stephen J Williams, PhD
2.1.3.3 People with two copies of the Δ32 mutation died at rates 21 percent higher than those with one or no copies – application of CRISPR @Berkeley
Reporter: Aviva Lev-Ari, PhD, RN
2.1.3.4 TWEETS by @pharma_BI and @AVIVA1950 at #IESYMPOSIUM – @kochinstitute 2019 #Immune #Engineering #Symposium, 1/28/2019 – 1/29/2019
Real Time Press Coverage: Aviva Lev-Ari, PhD, RN
2.1.3.5 Jennifer Doudna and NPR science correspondent Joe Palca, several interviews
Reporter: Aviva Lev-Ari, PhD, RN
2.1.3.6 Original Tweets Re-Tweets and Likes by @pharma_BI and @AVIVA1950 at #kisymposium for 17th annual Summer Symposium: Breakthrough Cancer Nanotechnologies: Koch Institute, MIT Kresge Auditorium, June 15, 2018, 9AM-4PM
Real Time Press Coverage: Aviva Lev-Ari, PhD, RN
2.1.3.7 Lysyl Oxidase (LOX) gene missense mutation causes Thoracic Aortic Aneurysm and Dissection (TAAD) in Humans because of inadequate cross-linking of collagen and elastin in the aortic wall – Mutation carriers may be predisposed to vascular diseases because of weakened vessel walls under stress conditions.
Reporter: Aviva Lev-Ari, PhD, RN
2.1.3.8 AACR2016 – Cancer immunotherapy
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/05/19/aacr2016-cancer-immunotherapy/
2.1.3.9 CRISPR/Cas9, Familial Amyloid Polyneuropathy (FAP) and Neurodegenerative Disease
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/17/crisprcas9-fap-and-neurodegenerative-disease/
2.1.3.10 Can CRISPR/Cas9 Target Multiple Targets?
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/22/can-crisprcas9-target-multiple-targets/
2.1.3.11 Genomic Pathogen Typing
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/16/genomic-pathogen-typing/
2.1.3.12 Breaking News about Genomic Engineering, T2DM and Cancer Treatments – 9/28/2015
Curator: Larry H Bernstein, MD, FCAP
2.1.3.13 Disease related changes in proteomics, protein folding, protein-protein interaction
Curator: Larry H. Bernstein, MD, FCAP
2.1.3.14 @BroadInstitute a shift from Permanently editing DNA to Temporarily revising RNA – An approach with promise for addressing the risk of developing Alzheimer’s by deactivating APOE4 – RESCUE: RNA Editing for Specific C to U Exchange, the platform builds on REPAIR: RNA Editing for Programmable A to I
Reporter: Aviva Lev-Ari, PhD, RN
2.1.3.15 At Technical University of Munich (TUM) Successful Genetical modification of a patient’s own immune cells, T cell receptors, using CRISPR-Cas9 gene editing tool. The engineered T cells are very similar to the physiological immune cells.
Reporter: Aviva Lev-Ari, PhD, RN
2.1.4 Ethics Issues related to CRISPR Technology
2.1.4.1 Level of Comfort with Making Changes to the DNA of an Organism
Curator: Aviva Lev-Ari, PhD, RN
2.1.4.2 Opportunities and Ethics of Editing Genomes: A CRISPR-Inspired Conversation, Prof. Jennifer Doudna’s Lecture at Stanford University, JANUARY 24, 2019 – 7:00PM TO 8:30PM, CEMEX AUDITORIUM, GRADUATE SCHOOL OF BUSINESS
Reporter: Aviva Lev-Ari, PhD, RN
2.1.4.3 Gene-editing Second International Summit in Hong Kong: George Church, “Let’s be quantitative before we start being accusatory”
Reporter: Aviva Lev-Ari, PhD, RN
2.1.4.4 GENE EDITING: Promises and Challenges: HSPH and NBC News Digital, Friday, May 19, 2017 Live webcast: 12:30-1:30pm ET
Reporter: Aviva Lev-Ari, PhD, RN
2.1.4.5 CRISPR and Human Embryo
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/18/crispr-and-human-embryo/
2.1.4.6 Unchecked Spread of Engineered Genes
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/18/unchecked-spread-of-engineered-genes/
2.1.5 Other topics related to the advent of CRISPR as a Gene Editing Method
2.1.5.1 A. Richard Newton Distinguished Innovator Lecture Series – Dr. Jennifer Doudna, April 23, 2019, UC, Berkeley
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.2 Top 10 CRISPR Podcasts Every Scientist (& Non-Scientist) by Synthego.com
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.3 National Academy of Sciences for work in chemical sciences: Jennifer Doudna, University of California, Berkeley
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.4 CRISPR Based Research Awarded NHGRI Grants, The University of California, Berkeley’s Doudna will receive $2.1 million and The Broad Institute’s Zhang will receive $1.1 million
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.5 Promising research for a male birth control pill
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
https://pharmaceuticalintelligence.com/2017/03/23/promising-research-for-a-male-birth-control-pill/
2.1.5.6 Top 50 Women in CRISPR : Women in CRISPR, Legal Status of Inventions and Declaration of the Heroes in CRISPR
Curator: Aviva Lev-Ari, PhD, RN
2.1.5.7 We Celebrate 5,000 Scientific Articles @pharmaceuticalintelligence.com – 2016 was a GREAT Year – Record Articles on CRISPR !!!!!
Curator and Open Access Journal Editor-in-Chief: Aviva Lev-Ari, PhD, RN
2.1.5.8 LIVE – Day 1, OCTOBER 18 @The 16th annual EmTech MIT – A Place of Inspiration, October 18-20, 2016, Cambridge, MA
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.9 More Awards to Jennifer Doudna: 2016 Warren Alpert Foundation Prize and The 2015 Pfizer Lecture
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.10 Federation of European Biochemical Societies FEBS Journal Special Issue on CRISPR/Cas9 Gene Editing by news.wiley.com – State of CRISPR/Cas9 Science on 9/2016
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.11 Real Time Coverage and eProceedings of Presentations on 9/19-9/21 @CHI’s 14th Discovery On Target, 9/19 – 9/22/2016, Westin Boston Waterfront, Boston
Curator: Aviva Lev-Ari, PhD, RN
2.1.5.12 Genomics Orientations for Personalized Medicine: Request for Book Review Writing on Amazon.com
http://www.amazon.com/dp/B018DHBUO6
The last chapter of this volume presents the science of CRISPR till the date of book’s publication on 11/23/2015. Below is an abbreviated electronic Table of Contents of this chapter – Live links
Chapter 21 in http://www.amazon.com/dp/B018DHBUO6
Recent Advances in Gene Editing Technology Adds New Therapeutic Potential for the Genomic Era: Medical Interpretation of the Genomics Frontier – CRISPR – Cas9
Introduction
21.1 Introducing CRISPR/Cas9 Gene Editing Technology – Works by Jennifer A. Doudna
21.1.1 Ribozymes and RNA Machines – Work of Jennifer A. Doudna
21.2 CRISPR in Other Labs
21.2.1 CRISPR @MIT – Genome Surgery
21.2.2 The CRISPR-Cas9 System: A Powerful Tool for Genome Engineering and Regulation
Yongmin Yan and Department of Gastroenterology, Hepatology & Nutrition, University of Texas M.D. Anderson Cancer, Houston, USA Daoyan Wei*
21.2.5 CRISPR & MAGE @ George Church’s Lab @ Harvard
21.3 Patents Awarded and Pending for CRISPR
21.3.1 Litigation on the Way: Broad Institute Gets Patent on Revolutionary Gene-Editing Method
21.3.2 The Patents for CRISPR, the DNA editing technology as the Biggest Biotech Discovery of the Century
2.4 CRISPR/Cas9 Applications
21.4.2 CRISPR: Applications for Autoimmune Diseases @UCSF
21.4.3 In vivo validated mRNAs
21.4.4 Delineating a Role for CRISPR-Cas9 in Pharmaceutical Targeting
21.4.5 Where is the most promising avenue to success in Pharmaceuticals with CRISPR-Cas9?
21.4.6 Level of Comfort with Making Changes to the DNA of an Organism
21.4.8 CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development
Summary
Editor: Aviva Lev-Ari, PhD, RN
2.1.5.13 The Roles of Graduate Students and Postdocs in the Emergence of Gene Editing: CRISPR Science and Technology
Curator: Aviva Lev-Ari, PhD, RN
2.1.5.14 A Conversation with Jennifer Doudna, Interviewer: Jan Witkowski, Executive Director, Banbury Center at Cold Spring Harbor Laboratory
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.15 Women Leaders in Cell and Gene Therapy
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2016/07/11/women-leaders-in-cell-and-gene-therapy/
2.1.5.16 John Holdren tells Nature about the Highs and Lows of nearly eight years in the White House, Holdren is the longest-serving presidential Science Adviser in US history.
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.17 Glassman Lecture “From Bacterial Adaptive Immunity to the Future of Genome Engineering” Jennifer A. Doudna, University of California, Berkeley; Howard Hughes Medical Institute
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.18 Genome Engineering: The CRISPR-Cas Revolution, August 17 – 20, 2016, Cold Spring Harbor Laboratory
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.19 The 16th annual EmTech MIT – A Place of Inspiration, October 18-20, 2016, Cambridge, MA
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.20 CRISPR: Genome Editing and Cancer was ranked 7th on the List of Disruptive Dozen Technologies @2016 World Medical Innovation Forum
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.21 Best in Precision Medicine: RNA May Surpass DNA in Precision Medicine
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/03/23/best-in-precision-medicine/
2.1.5.22 Jennifer Doudna, Woman of Science Award
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/03/18/jennifer-doudna-woman-of-science-award/
2.1.5.23 CRISPR: A Podcast from Nature.com on Gene Editing
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2016/03/17/crispr-a-podcast-from-nature-com-on-gene-editing/
2.1.5.24 Lab Management: About The Doudna Lab, RNA Biology at UC Berkeley, HHMI
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.25 International Summit on Human Gene Editing: A Global Discussion, National Academy of Sciences, WashDC, December 1-3, 2015
Reporter: Aviva Lev-Ari, PhD, RN
2.1.5.26 Cambridge Healthtech Institute’s Second Annual New Frontiers in Gene Editing, SF, 3/10-3/11, 2016
Reporter: Stephen J. Williams, PhD
2.1.5.27 CRISPR/Cas-mediated Genome Engineering
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/09/08/crisprcas-mediated-genome-engineering/
2.1.5.28 Advances in Gene Editing and Gene Silencing | September 20-21, 2016 | Boston, MA
Author and Curator: Stephen J Williams, PhD
2.2 Technologies and Methodologies
2.2.1 Alter the Code of Life – Technologies for Gene Editing from MammothBiosciences, San Francisco, CA
Reporter: Aviva Lev-Ari, PhD, RN
2.2.2 CRISPR on TED Ideas worth spreading – Ellen Jorgensen
Reporter: Aviva Lev-Ari, PhD, RN
2.2.3 CRISPR snips a strand of DNA – Visualization of the Process
Reporter: Aviva Lev-Ari, PhD, RN
2.2.4 Pancreatic Cancer Modeling using Retrograde Viral Vector Delivery and IN-Vivo CRISPR/Cas9-mediated Somatic Genome Editing
Curators: Larry H. Benstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
2.2.5 Bacterial immune system may be utilized as a tool harboring an impressive recording capacity
Curator: Larry H. Bernstein, MD, FCAP
2.2.6 CHI’s Inaugural Oligonucleotide Therapeutics & Delivery | April 4-5, 2016 | Hyatt Regency | Cambridge, Massachusetts
Reporter: Aviva Lev-Ari, PhD, RN
2.2.7 Innovative Genomics Initiative (IGI) 2016 CRISPR WORKSHOP: PRACTICAL ASPECTS OF PRECISION BIOLOGY, UC, Berkeley, July 11-15, 2016
Reporter: Aviva Lev-Ari, PhD, RN
2.2.8 RNA-Based Drugs Turn CRISPR/Cas9 On and Off
Reporter: Stephen J. Williams
https://pharmaceuticalintelligence.com/2016/02/17/rna-based-drugs-turn-crisprcas9-on-and-off/
2.2.9 Reengineering Therapeutics
Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/02/06/reengineering-therapeutics/
2.2.10 Deciphering the Epigenome
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/01/28/deciphering-the-epigenome/
2.2.11 Gene Editing for Exon 51: Why CRISPR Snipping might be better than Exon Skipping for DMD
Reporter: Aviva Lev-Ari, PhD, RN
2.2.12 Genome Engineering: Genome Editing with CRISPR-Cas9
Reporter: Aviva Lev-Ari, PhD, RN
2.2.13 Enhanced Cas9 for more precise editing
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/12/11/enhanced-cas9-for-more-precise-editing/
2.2.14 Genetically Engineered Algae
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/15/genetically-engineered-algae/
2.2.15 Cas9 Proofreads
Curator: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/13/cas9-proofreads/
2.2.16 RNAi, CRISPR and Gene Expression
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/02/rnai-crispr-and-gene-expression/
2.2.17 Obesity Variant Circuitry
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/10/31/obesity-variant-circuitry/
2.2.18 CRISPR-Cas9 and Regenerative Medicine
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/10/31/crispr-cas9-crispr-cas9-and-regenerative-medicine/
2.2.19 Gene Editing by Creation of a Complement without Transcription Error
Curator: Larry H. Bernstein, MD, FCAP
2.2.20 Principles of Gene Editing
Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/10/30/principles-of-gene-editing/
2.2.21 Top 10 Medical Innovations for 2016 by Cleveland Clinic
Reporter: Aviva Lev-Ari, PhD, RN
2.2.22 NIH to Award Up to $12M to Fund DNA, RNA Sequencing Research: single-cell genomics, sample preparation, transcriptomics and epigenomics, and genome-wide functional analysis.
Reporter: Aviva Lev-Ari, PhD, RN
2.2.23 CRISPR/Cas9 genome editing tool for Staphylococcus aureus Cas9 complex (SaCas9) @ MIT’s Broad Institute
Reporter: Aviva Lev-Ari, PhD, RN
2.2.24 RNAi, CRISPR, and Gene Editing: Discussions on How To’s and Best Practices @14th Annual World Preclinical Congress June 10-12, 2015 | Westin Boston Waterfront | Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
2.2.25 CRISPR/Cas9: Contributions on Endoribonuclease Structure and Function, Role in Immunity and Applications in Genome Engineering
Writer and Curator:Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/03/27/crisprcas9/
2.2.26 GUIDE-seq: First genome-wide method of detecting off-target DNA breaks induced by CRISPR-Cas nucleases
Reporter: Aviva Lev-Ari, PhD, RN
2.2.27 2nd Annual Translational Gene Editing: Exploiting CRISPR/Cas9 for Building Tools for Drug Discovery & Development: June 16, 2016 @Boston, MA
Reporter: Stephen J. Williams, PhD
2.3 CRISPR – The Clinical Aspects
2.3.1 Sickle Cell and Beta Thalassemia chosen for first human trial of the gene editing technology, CRISPR by sponsoring companies CRISPR Therapeutics and Vertex Pharmaceuticals, trial at a single site in Germany
Reporter: Aviva Lev-Ari, PhD, RN
2.3.2 Updated: First-in-Man: CRISPR, the Genome Editing Technology is Nearing Human Trials: Human T cells will soon be Modified using the CRISPR Technique in a Clinical Trial to attack Cancer Cells
Curator: Aviva Lev-Ari, PhD, RN
2.3.3 The Promise of Gene Editing for Slowing Progression of Disease: Translational Application toward Cure of Disease
Reporters: Gerard Loiseau, ESQ and Aviva Lev-Ari, PhD, RN
2.3.4 Translational Gene Editing – June 16-17, 2016 in Boston, MA by CHI, Westin Boston Waterfront, Boston, MA
Reporter: Aviva Lev-Ari, PhD, RN
2.3.5 FDA Cellular & Gene Therapy Guidances: Implications for CRSPR/Cas9 Trials
Reporter: Stephen J. Williams, PhD
2.3.6 CRISPR Gene Editing Trial
Curator: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/10/crispr-gene-editing-trial/
2.3.7 UNESCO Calls for More Regulations on Genome Editing, DTC Genetic Testing
Reporter: Aviva Lev-Ari, PhD, RN
2.3.8 CRISPR/Cas9, Familial Amyloid Polyneuropathy (FAP) and Neurodegenerative Disease
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/17/crisprcas9-fap-and-neurodegenerative-disease/
2.3.9 CRISPR cuts turn gels into biological watchdogs
Reporter: Irina Robu, PhD
https://pharmaceuticalintelligence.com/2019/08/30/crispr-cuts-turn-gels-into-biological-watchdogs/
2.4 CRISPR – Business and Legal
2.4.1 CRISPR – The Business and Legal Aspects of IP Development
Patent on Methods and compositions for RNA-directed target DNA modification and for RNA-directed modulation of transcription was awarded to UC, Berkeley on October 30, 2018
- site-specific modification of a target DNA and/or a polypeptide associated with the target DNA, a DNA-targeting RNA
- genetically modified cells that produce Cas9 and Cas9 transgenic non-human multicellular organisms.
Reporter: Aviva Lev-Ari, PhD, RN
2.4.2 Will the Supreme Court accept a UC Berkeley Appeal of the Sep. 10th, US Court of Appeals for the Federal Circuit decision to uphold the patent filed by the Broad Institute on CRISPR/Cas9 gene editing?
Reporter: Aviva Lev-Ari, PhD, RN
2.4.3 On June 12, 2018 – Berkeley was granted a patent on using CRISPR/Cas9 to edit single-stranded RNA. On June 19, 2018 – Berkeley was granted a second patent, covering the use of CRISPR-Cas9 gene editing with formats that will be particularly useful in developing human therapeutics and improvements in food security.
Reporter and Curator: Aviva Lev-Ari, PhD, RN
2.4.4 Developments in CRISPR Patent Dispute: EPO Revokes Broad’s CRISPR Patent
Curator: Aviva Lev-Ari, PhD, RN
2.4.5 Appellate Brief Seeking Reversal of U.S. Patent Board Decision on CRISPR/Cas9 Gene Editing
Reporter: Aviva Lev-Ari, PhD, RN
2.4.6 Doudna and Charpentier and their teams to receive wide-ranging patents in many countries: European Patent Office (EPO) and UK Intellectual Property Office – broad patent for CRISPR-Cas9 gene-editing technology to the University of California and the University of Vienna
Reporter: Aviva Lev-Ari, PhD, RN
2.4.7 UPDATED – Gene Editing Consortium of Biotech Companies: CRISPR Therapeutics $CRSP, Intellia Therapeutics $NTLA, Caribou Biosciences, ERS Genomics, UC, Berkeley (Doudna’s IP) and University of Vienna (Charpentier’s IP), is appealing the decision ruled that there was no interference between the two sides, to the U.S. Court of Appeals for the Federal Circuit, targeting patents from The Broad Institute.
Curator: Aviva Lev-Ari, PhD, RN
2.4.8 CRISPR Patent Battle Determined on 2/15/2017 – USPTO issues a verdict in legal tussle over rights to genome-editing technology
Curator: Aviva Lev-Ari, PhD, RN
2.4.9 Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology
Reporter: Aviva Lev-Ari, PhD, RN
2.4.10 Dr. Jennifer Doudna (UC Berkeley): PMWC 2017 Luminary Award, January 22, 2017 @PMWC 2017, January 23-25, Silicon Valley
Reporter: Aviva Lev-Ari, PhD, RN
2.4.11 CRISPR Therapeutics raises a $56M IPO, but patent battles, potential stock drops loom
Reporter: Aviva Lev-Ari, PhD, RN
2.4.12 Licensing Agreements for CRISPR/Cas9 Genome Editing Technology Patent
Curator: Aviva Lev-Ari, PhD, RN
2.4.13 Licensing deal with Regeneron to accelerate CRISPR biotech Intellia (Jennifer Doudna’s Start Up) for an IPO
Reporter: Aviva Lev-Ari, PhD, RN
2.4.14 Nine Parties had come forward: Opposition Procedure to the Broad Institute’s first European CRISPR–Cas9 Patent
Curator: Aviva Lev-Ari, PhD, RN
2.4.15 Use of CRISPR/CAS9 to Edit Genome of Pigs: Recominetics announces $10M Funding Round
Reporter: Stephen J. Williams
2.4.16 UPDATED – Status “Interference — Initial memorandum” – CRISPR/Cas9 – The Biotech Patent Fight of the Century: UC, Berkeley and Broad Institute @MIT
Reporter: Aviva Lev-Ari, PhD, RN
2.4.17 Editas, CEO predicts 2017 to be the Year of Human Gene Editing
Reporter: Aviva Lev-Ari, PhD, RN
2.4.18 Anatomy of a $105M Deal for Joint R&D in Genomics: CRISPR Therapeutics & Vertex Pharmaceuticals
Reporter: Aviva Lev-Ari, PhD, RN
2.4.19 CRISPR companies calling for article retraction from Nature Methods – If the same or similar sequence of letters appears elsewhere in the genome, that can result in an unintentional or off-target edit – Concerns of Harm caused by Gene Editing using CRISPR-Cas9
Reporter: Aviva Lev-Ari, PhD, RN
2.4.20 Merck KGaA-owned Sigma-Aldrich has petitioned the US Patent and Trademark Office (USPTO) to open an interference proceeding between its own pending CRISPR-Cas9 patents and patents awarded to the University of California, Berkeley (UC Berkeley).
Reporter: Aviva Lev-Ari, PhD, RN
Part 2: Summary – CRISPR – Voice of Professor Williams
Real Time Coverage @BIOConvention #BIO2019: Can Genome Editing Fulfill Its Promise and Meet Unmet Medical Needs? Philadelphia PA
Reporter: Stephen J. Williams, PhD
Part 3: Artificial Intelligence in Medicine
Introduction to Part 3: AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams
There is a current consensus that of all specialties in Medicine, Artificial Intelligence technologies will benefit the most the specialty of Radiology.
What AI can do
Of course, there is still a lot AI can do for radiologists. Soonmee Cha, MD, neuroradiologist, has served as a program director at the University of California San Francisco since 2012 and currently oversees 100 radiology trainees, said at RSNA 2019 in Chicago
“we can see a future where AI is improving image quality, decreasing acquisition times, eliminating artifacts, improving patient communication and even decreasing radiation dose.
“If AI can detect when machines are being set up incorrectly and alert us, it’s a win for us and for patients,” she said.
Radiology societies team up for new statement on ethics of AI
Numerous imaging societies, including the American College of Radiology (ACR) and RSNA, have published a new statement on the ethical use of AI in radiology.
The European Society of Radiology, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics (EuSoMII), Canadian Association of Radiologists and American Association of Physicists in Medicine all also co-authored the statement which is focused on three key areas of AI development: data, algorithms and practice. A condensed summary was shared in the Journal of the American College of Radiology, Radiology, Insights into Imaging and the Canadian Association of Radiologists Journal.
“Radiologists remain ultimately responsible for patient care and will need to acquire new skills to do their best for patients in the new AI ecosystem,” J. Raymond Geis, MD, ACR Data Science Institute senior scientist and one of the document’s leading contributors, said in a prepared statement. “The radiology community needs an ethical framework to help steer technological development, influence how different stakeholders respond to and use AI, and implement these tools to make the best decisions for—and increasingly with—patients.”
“The application of AI tools in radiological practice lies in the hand of the radiologists, which also means that they have to be well-informed not only about the advantages they can offer to improve their services to patients, but also about the potential risks and pitfalls that might occur when implementing them,” Erik R. Ranschaert, MD, PhD, president of EuSoMII. “This paper is therefore an excellent basis to improve their awareness about the potential issues that might arise, and should stimulate them in thinking proactively on how to answer the existing questions.”
Back in September, the Royal Australian and New Zealand College of Radiologists (RANZCR) published its own guidelines on the ethical application of AI in healthcare. The document, “Ethical Principles for Artificial Intelligence in Medicine,” is available on the RANZCR website.
https://www.radiologybusiness.com/topics/artificial-intelligence/radiology-societies-ethics-ai
Selective examples of applications of AI in the specialty of Radiology include the following:
- RSNA 2019, the world’s largest radiology conference, kicks off at Chicago’s McCormick Place on Sunday, Dec. 1, 2019, and promises to include more AI content than ever before. There will be an expanded AI Showcase this year, giving attendees access to more than 100 vendors in one location.
- “Artificial Intelligence and Precision Education: How AI Can Revolutionize Training in Radiology” | Monday, Dec. 2 | 8:30 – 10 a.m. | Room: E450A
- “Learning AI from the Experts: Becoming an AI Leader in Global Radiology (Without Needing a Computer Science Degree)” | Tuesday, Dec. 3 | 4:30-6 p.m. | Room: S406B
- “Deep Learning in Radiology: How Do We Do It?” | Wednesday, Dec. 4 | 8:30-10 a.m. | Room: S406B
- Interview with George Shih, MD, a radiologist at Weill Cornell Medicine and NewYork-Presbyterian and the co-founder of the healthcare startup MD.ai
An academic gold rush, where people are working to apply the latest AI techniques to both existing problems and brand new problems, and it’s all been really great for the field of radiology.
We’re also holding another machine learning competition this year hosted on Kaggle. In previous years, we’ve annotated existing public data that was used for our competition, but this year, we were actually able to acquire high-quality data—more than 25,000 CT examinations that nobody has used or seen before—from four different institutions. The top 10 winning algorithms will also be made public to anyone in the world, which is an amazing way to advance the use of AI in radiology. I think that’s one of the biggest contributions RSNA is making to the academic community this year.
The other exciting part is that our new and improved AI Showcase will include more vendors—more than 100—than any previous year, which shows just how much the market continues to focus on these technologies.
- AI model could help radiologists diagnose lung cancer
Michael Walter | November 27, 2019 | Medical Imaging
- AI a hot topic for radiology researchers in 2019
Michael Walter | November 26, 2019 | Medical Imaging
- GE Healthcare launches new program to simplify AI development, implementation
Michael Walter | November 26, 2019 | Business Intelligence
- How teleradiologists are helping underserved regions all over the world
Michael Walter | Medical Imaging Review
Sponsored by vRad, a MEDNAX Company
AI in Healthcare 2020 Leadership Survey Report: 7 Key Findings
Artificial and augmented intelligence are already helping healthcare improve clinically, operationally and financially—and there is extraordinary room for growth. Success starts with leadership, vision and investment and leaders tell us they have all of the above. Here are the top 7 survey findings.
01 C-level healthcare leaders are leading the charge to AI. AI has earned the attention of the C-suite, with 40% of survey respondents saying their strategy is coming from the top down. Chief information officers are most often managing AI across the healthcare enterprise (27%).
02 AI has moved into the mainstream. The future is now. It’s here. Health systems are hiring data scientists and spending on AI and infrastructure. Some 40% of respondents are using AI, with 50% using between one and 10 apps.
03 Health systems are committed to investing in AI. 93% of respondents agree AI is absolutely essential, very important or important to their strategy. There is great willingness to take advantage of intelligent technology and leverage machine intelligence to enhance human intelligence. Administration holds financial responsibility for AI at 43% of facilities, with IT paying the bill at 26% of sites.
04 Fortifying infrastructure is top of mind. 93% of respondents agree AI is absolutely essential, very important or important to their strategy. There is great willingness to take advantage of intelligent technology and leverage machine
intelligence to enhance human intelligence. Administration holds financial responsibility for AI at 43% of facilities, with IT paying the bill at 26% of sites.05 Improving care is AI’s greatest benefit. Improving accuracy, efficiency and workflow are the top benefits leaders see coming from AI. AI helps to highlight key findings from the depths of the EMR, identify declines in patient conditions earlier and improve chronic disease management. Cancer, heart disease and stroke are the disease states survey respondents see AI holding the greatest promise—the 2nd, 1st and 5th leading killer of Americans.
06 Health systems are both buying and developing AI apps. Some 50% of respondents tell us they are both buying and developing AI apps. About 38% are exclusively opting to purchase commercially developed apps while 13% are developing everything in-house.
07 Radiology is blazing the AI trail. AI apps for imaging outnumber all other categories of FDA-approved apps to date. It’s no surprise then that respondents tell us that rad apps top the list of tools they’re using to enhance breast, chest and cardiovascular imaging.
SOURCE
WATCH VIDEO
https://www.dropbox.com/s/xayeu7ss7f7cahp/AI%20Launch%20v2.mp4?dl=0
Like in the past, Dr. Eric Topol is a Tour de Force, again
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again 1st Edition
by Eric Topol (Author)
5.0 out of 5 starsCrystal Ball for the Next Era of Healthcare
March 13, 2019
Format: HardcoverVerified Purchase
Dr. Topol’s new book, Deep Medicine – How Artificial Intelligence Can Make Healthcare Human Again, is an encyclopedia of the emerging Fourth Industrial Age; a crystal ball in what is about happen in the next era of healthcare. I’m impressed by the detailed references and touching personal and family stories.
Centers for Medicare & Medicaid Services (CMS) policy modifications in the past 10 months reveal sweeping changes that fortify Dr. Topol’s vision: May 2018 medical students can document for attending physicians in the health record (MLN MM10412), 2019 ancillary staff members and patients can document the History/medical interview into the health record, 2021 medical providers can document based only on Medical Decision Making or Time (Federal Register Nov, 23, 2018).
Part of making healthcare human is also making it fun. The joy of practicing medicine is about to return to the healthcare delivery as computers will be used to empower humanistic traits, not overburden medical professionals with clerical tasks. For patients, you will be heard, understood and personally treated. Deep Medicine is not a vision of what will happen in 50 years as much will start to reveal within the next 5!
Bravo Dr. Topol!
Michael Warner, DO, CPC, CPCO, CPMA, AAPC Fellow
AUDIT PODCASTS
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The perspective of what it truly means to be an AI company and AI platform.
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How MaxQ AI is reinventing the diagnostic process with AI in time sensitive, life threatening environments.
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How EnvoyAI is working towards a zero-click approach for physicians to feel confident in their findings.
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Recognizing the right questions to ask when training algorithms for more accurate results.
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The value of having a powerful world-class image processing algorithm running on an extensible interoperable platform.
Join Jeff, Gene, and Kevin next time as they continue the conversation on the future of artificial intelligence in healthcare.
Academic Gallup Poll: The Artificial Intelligence Age, June 2019.
New Northeastern-Gallup poll: People in the US, UK, and Canada want to keep up in the artificial intelligence age. They say employers, educators, and governments are letting them down. – News @ Northeastern
Dense Map of Artificial Intelligence Start ups in Israel
Image Source: https://www.startuphub.ai/multinational-corporations-with-artificial-intelligence-research-and-development-centers-in-israel/
(See here for an interactive version of the infographic above).
https://hackernoon.com/israels-artificial-intelligence-landscape-2018-83cdd4f04281
3.1 The Science
VIEW VIDEO
Max Tegmark lecture on Life 3.0 – Being Human in the age of Artificial Intelligence
https://www.youtube.com/watch?v=1MqukDzhlqA
3.1.1 World Medical Innovation Forum, Partners Innovations, ARTIFICIAL INTELLIGENCE | APRIL 8–10, 2019 | Westin, BOSTON
https://worldmedicalinnovation.org/agenda/
Reporter: Aviva Lev-Ari, PhD, RN
3.1.2 LIVE Day Three – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 10, 2019
Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN
3.1.3 LIVE Day Two – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 9, 2019
Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN
3.1.4 LIVE Day One – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 8, 2019
Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN
3.1.5 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place https://worldmedicalinnovation.org/
Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN
3.1.6 Synopsis Days 1,2,3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts | Westin Copley Place
Real Time Coverage: Curator: Aviva Lev-Ari, PhD, RN
3.1.7 Interview with Systems Immunology Expert Prof. Shai Shen-Orr
Reporter: Aviva Lev-Ari, PhD, RN
Interview with Systems Immunology Expert Prof. Shai Shen-Orr
3.1.8 Unique immune-focused AI model creates largest library of inter-cellular communications at CytoReason. Used to predict 335 novel cell-cytokine interactions, new clues for drug development.
Reporter: Aviva Lev-Ari, PhD, RN
- CYTOREASON. CytoReason features in hashtag #DeepKnowledgeVentures‘s detailed Report on AI in hashtag #drugdevelopment report https://lnkd.in/dKV2BB6
https://www.eurekalert.org/pub_releases/2018-06/c-uia061818.php
3.2 Technologies and Methodologies
3.2.1 R&D for Artificial Intelligence Tools & Applications: Google’s Research Efforts in 2018
Reporter: Aviva Lev-Ari, PhD, RN
3.2.2 Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare
Curator: Stephen J. Williams, Ph.D.
3.2.3 N3xt generation carbon nanotubes
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/12/14/n3xt-generation-carbon-nanotubes/
3.2.4 Mindful Discoveries
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/01/28/mindful-discoveries/
3.2.5 Novel Discoveries in Molecular Biology and Biomedical Science
Curator: Larry H. Bernstein, MD, FCAP
3.2.6 Imaging of Cancer Cells
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/04/20/imaging-of-cancer-cells/
3.2.7 Retrospect on HistoScanning: an AI routinely used in diagnostic imaging for over a decade
Author and Curator: Dror Nir, PhD
3.2.8 Prediction of Cardiovascular Risk by Machine Learning (ML) Algorithm: Best performing algorithm by predictive capacity had area under the ROC curve (AUC) scores: 1st, quadratic discriminant analysis; 2nd, NaiveBayes and 3rd, neural networks, far exceeding the conventional risk-scaling methods in Clinical Use
Reporter: Aviva Lev-Ari, PhD, RN
3.2.9 An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
3.3 Clinical Aspects
Is AI ready for Medical Applications? – The Debate in August 2019 in Nature
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Eric Topol (@EricTopol) |
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Why I’ve been writing #AI for medicine is long on promise, short of proof nature.com/articles/s4159… @NatureMedicine status update in this schematic, among many mismatches pic.twitter.com/mpifYFwlp8 |
The “inconvenient truth” about AI in healthcare
npj Digital Medicine volume 2, Article number: 77 (2019)
However, “the inconvenient truth” is that at present the algorithms that feature prominently in research literature are in fact not, for the most part, executable at the frontlines of clinical practice. This is for two reasons: first, these AI innovations by themselves do not re-engineer the incentives that support existing ways of working.2 A complex web of ingrained political and economic factors as well as the proximal influence of medical practice norms and commercial interests determine the way healthcare is delivered. Simply adding AI applications to a fragmented system will not create sustainable change. Second, most healthcare organizations lack the data infrastructure required to collect the data needed to optimally train algorithms to (a) “fit” the local population and/or the local practice patterns, a requirement prior to deployment that is rarely highlighted by current AI publications, and (b) interrogate them for bias to guarantee that the algorithms perform consistently across patient cohorts, especially those who may not have been adequately represented in the training cohort.9 For example, an algorithm trained on mostly Caucasian patients is not expected to have the same accuracy when applied to minorities.10 In addition, such rigorous evaluation and re-calibration must continue after implementation to track and capture those patient demographics and practice patterns which inevitably change over time.11 Some of these issues can be addressed through external validation, the importance of which is not unique to AI, and it is timely that existing standards for prediction model reporting are being updated specifically to incorporate standards applicable to this end.12 In the United States, there are islands of aggregated healthcare data in the ICU,13 and in the Veterans Administration.14 These aggregated data sets have predictably catalyzed an acceleration in AI development; but without broader development of data infrastructure outside these islands it will not be possible to generalize these innovations.
3.3.1 9 AI-based initiatives catalyzing immunotherapy in 2018
By Tanima Bose
https://www.prescouter.com/2018/07/9-ai-based-initiatives-catalyzing-immunotherapy-in-2018/
3.3.2 mRNA Data Survival Analysis
Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2016/06/18/mrna-data-survival-analysis/
3.3.3 Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI
Reporter: Stephen J. Williams
3.3.4 Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address
Reporter: Stephen J. Williams, PhD
3.3.5 VIDEOS: Artificial Intelligence Applications for Cardiology
Reporter: Aviva Lev-Ari, PhD, RN
3.3.6 Artificial Intelligence in Health Care and in Medicine: Diagnosis & Therapeutics
Reporter: Aviva Lev-Ari, PhD, RN
3.3.7 Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
3.3.8 The 3rd STATONC Annual Symposium, April 25-27, 2019, Hilton Hartford, CT, 315 Trumbull St., Hartford, CT 06103
Reporter: Stephen J. Williams, Ph.D.
3.3.9 2019 Biotechnology Sector and Artificial Intelligence in Healthcare
Reporter: Aviva Lev-Ari, PhD, RN
3.3.10 Artificial intelligence can be a useful tool to predict Alzheimer
Reporter: Irina Robu, PhD
3.3.11 Unlocking the Microbiome
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/02/07/unlocking-the-microbiome/
3.3.12 Biomarker Development
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/11/16/biomarker-development/
3.3.13 AI System Used to Detect Lung Cancer
Reporter: Irina Robu, PhD
https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/
3.3.14 AI App for People with Digestive Disorders
Reporter: Irina Robu, PhD
https://pharmaceuticalintelligence.com/2019/06/24/ai-app-for-people-with-digestive-disorders/
3.3.15 Sepsis Detection using an Algorithm More Efficient than Standard Methods
Reporter: Irina Robu, PhD
3.3.16 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancer?
Author: Larry H Bernstein, MD, FCAP
3.3.17 An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
3.3.18 Artificial Intelligence and Cardiovascular Disease
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
3.3.19 Using A.I. to Detect Lung Cancer gets an A!
Reporter: Irina Robu, PhD
https://pharmaceuticalintelligence.com/2019/08/04/using-a-i-to-detect-lung-cancer-gets-an-a/
3.3.20 Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line
Reporter: Stephen J. Williams, PhD
3.3.21 Multiple Barriers Identified Which May Hamper Use of Artificial Intelligence in the Clinical Setting
Reporter: Stephen J. Williams, PhD.
3.3.22 Deep Learning–Assisted Diagnosis of Cerebral Aneurysms
Author and Curator: Dror Nir, PhD
3.3.23 Artificial Intelligence Innovations in Cardiac Imaging
Reporter: Aviva Lev-Ari, PhD, RN
3.4 Business and Legal
Image Source: https://www.linkedin.com/pulse/resources-artificial-intelligence-health-care-note-lev-ari-phd-rn/
3.4.1 McKinsey Top Ten Articles on Artificial Intelligence: 2018’s most popular articles – An executive’s guide to AI
Reporter: Aviva Lev-Ari, PhD, RN
3.4.2 HOTTEST Artificial Intelligence Hub: Israel’s High Tech Industry – Why?
Reporter: Aviva Lev-Ari, PhD, RN
3.4.3 The Regulatory challenge in adopting AI
Author and Curator: Dror Nir, PhD
https://pharmaceuticalintelligence.com/2019/04/07/the-regulatory-challenge-in-adopting-ai/
3.4.4 HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally
Reporter: Aviva Lev-Ari, PhD, RN
3.4.5 IBM’s Watson Health division – How will the Future look like?
Reporter: Aviva Lev-Ari, PhD, RN
3.4.6 HUBweek 2018, October 8-14, 2018, Greater Boston – “We The Future” – coming together, of breaking down barriers, of convening across disciplinary lines to shape our future
Reporter: Aviva Lev-Ari, PhD, RN
3.4.7 Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?
Reporter: Stephen J. Williams, Ph.D.
3.4.8 Healthcare conglomeration to access Big Data and lower costs
Curator: Larry H. Bernstein, MD, FCAP
3.4.9 Linguamatics announces the official launch of its AI self-service text-mining solution for researchers.
Reporter: Aviva Lev-Ari, PhD, RN
3.4.10 Future of Big Data for Societal Transformation
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2015/12/14/future-of-big-data-for-societal-transformation/
3.4.11 Deloitte Analysis 2019 Global Life Sciences Outlook
https://www.cioapplications.com/news/making-a-breakthrough-in-drug-discovery-with-ai-nid-3114.html
3.4.12 OpenAI: $1 Billion to Create Artificial Intelligence Without Profit Motive by Who is Who in the Silicon Valley
Reporter: Aviva Lev-Ari, PhD, RN
3.4.13 The Health Care Benefits of Combining Wearables and AI
Reporter: Gail S. Thornton, M.A.
3.4.14 These twelve artificial intelligence innovations are expected to start impacting clinical care by the end of the decade.
Reporter: Gail S. Thornton, M.A.
3.4.15 Forbes Opinion: 13 Industries Soon To Be Revolutionized By Artificial Intelligence
Reporter: Aviva Lev-Ari, PhD, RN
3.4.16 AI Acquisitions by Big Tech Firms Are Happening at a Blistering Pace: 2019 Recent Data by CBI Insights
Reporter: Stephen J. Williams, Ph.D.
3.5 Machine Learning (ML) Algorithms harnessed for Medical Diagnosis: Pattern Recognition & Prediction of Disease Onset
Introduction by Dr. Dror Nir
Icahn School of Medicine at Mount Sinai to Establish World Class Center for Artificial Intelligence – Hamilton and Amabel James Center for Artificial Intelligence and Human Health
First center in New York to seamlessly integrate artificial intelligence, data science and genomic screening to advance clinical practice and patient outcomes.
Integrative Omics and Multi-Scale Disease Modeling— Artificial intelligence and machine learning approaches developed at the Icahn Institute have been extensively used for identification of novel pathways, drug targets, and therapies for complex human diseases such as cancer, Alzheimer’s, schizophrenia, obesity, diabetes, inflammatory bowel disease, and cardiovascular disease. Researchers will combine insights in genomics—including state-of-the-art single-cell genomic data—with ‘omics,’ such as epigenomics, pharmacogenomics, and exposomics, and integrate this information with patient health records and data originating from wearable devices in order to model the molecular, cellular, and circuit networks that facilitate disease progression. “Novel data-driven predictions will be tightly integrated with high-throughput experiments to validate the therapeutic potential of each prediction,” said Adam Margolin, PhD, Professor and Chair of the Department of Genetics and Genomic Sciences and Senior Associate Dean of Precision Medicine at Mount Sinai. “Clinical experts in key disease areas will work side-by-side with data scientists to translate the most promising therapies to benefit patients. We have the potential to transform the way care givers deliver cost-effective, high quality health care to their patients, far beyond providing simple diagnoses. Mount Sinai wants to be on the frontlines of discovery.”
Precision Imaging—Researchers will use artificial intelligence to enhance the diagnostic power of imaging technologies—X-ray, MRI, CT, and PET—and molecular imaging, and accelerate the development of therapies. “We see a huge potential in using algorithms to automate the image interpretation and to acquire images much more quickly at high resolution – so that we can better detect disease and make it less burdensome for the patient,” said Zahi Fayad, PhD, Director of the Translational and Molecular Imaging Institute, and Vice Chair for Research for the Department of Radiology, at Mount Sinai. Dr. Fayad plans to broaden the scope of the Translational and Molecular Imaging Institute by recruiting more engineers and scientists who will create new methods to aid in the diagnosis and early detection of disease, treatment protocol development, drug development, and personalized medicine. Dr. Fayad added, “In addition to AI, we envision advance capabilities in two important areas: computer vision and augmented reality, and next generation medical technology enabling development of new medical devices, sensors and robotics.”
A comprehensive overview of ML algorithms applied in health care is presented in the following article:
Survey of Machine Learning Algorithms for Disease Diagnostic
https://www.scirp.org/journal/PaperInformation.aspx?PaperID=73781
3.5.1 Cases in Pathology
3.5.1.1 Deep Learning extracts Histopathological Patterns and accurately discriminates 28 Cancer and 14 Normal Tissue Types: Pan-cancer Computational Histopathology Analysis
Reporter: Aviva Lev-Ari, PhD, RN
3.5.2 Cases in Radiology
3.5.2.1 Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package
Reporter: Aviva Lev-Ari, PhD, RN
3.5.2.2 Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI
Reporter: Dror Nir, PhD
3.5.2.3 Showcase: How Deep Learning could help radiologists spend their time more efficiently
Reporter and Curator: Dror Nir, PhD
3.5.2.4 CancerBase.org – The Global HUB for Diagnoses, Genomes, Pathology Images: A Real-time Diagnosis and Therapy Mapping Service for Cancer Patients – Anonymized Medical Records accessible to anyone on Earth
Reporter: Aviva Lev-Ari, PhD, RN
3.5.2.5 Applying AI to Improve Interpretation of Medical Imaging
Author and Curator: Dror Nir, PhD
3.5.2.6 Imaging: seeing or imagining? (Part 2)
Author and Curator: Dror Nir, PhD
https://pharmaceuticalintelligence.com/2019/04/07/imaging-seeing-or-imagining-part-2-2/
3.5.3 Cases in Prediction Cancer Onset
3.5.3.1 A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction
3.5.3.2 Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction
Karin Dembrower , Yue Liu, Hossein Azizpour, Martin Eklund, Kevin Smith, Peter Lindholm, Fredrik Strand
Published Online: Dec 17 2019 https://doi.org/10.1148/radiol.2019190872
Results
A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P < .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P < .001), and the percentage density (mean, 25.6% vs 24.0%; P < .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P < .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P < .001); this difference was most pronounced for more aggressive cancers.
Conclusion
Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers.
Related articles
Radiology2019
Volume: 0Issue: 0
Radiology2019
Volume: 293Issue: 2pp. 246-259
Radiology2019
Volume: 291Issue: 3pp. 582-590
Summary of ML in Medicine by Dr. Dror Nir
See Introduction to 3.5, above
Part 3: Summary – AI in Medicine – Voice of Aviva Lev-Ari & Professor Williams
AI applications in healthcare
The potential of AI to improve the healthcare delivery system is limitless. It offers a unique opportunity to make sense out of clinical data to enable fully integrated healthcare that is more predictive and precise. Getting all aspects of AI-enabled solutions right requires extensive collaboration between clinicians, data scientists, interaction designers, and other experts. Here are four applications of artificial intelligence to transform healthcare delivery:
1. Improve operational efficiency and performance
On a departmental and enterprise level, the ability of AI to sift through large amounts of data can help hospital administrators to optimize performance, drive productivity, and improve the use of existing resources, generating time and cost savings. For example, in a radiology department, AI could make a difference in the management of referrals, patient scheduling, and exam preparations. Improvements here can help to enhance patient experience and will allow a more effective and efficient use of the facilities at examination sites.
2. Aiding clinical decision support
AI-enabled solutions can help to combine large amounts of clinical data to generate a more holistic view of patients. This supports healthcare providers in their decision making, leading to better patient outcomes and improved population health. “The need for insights and for those insights to lead to clinical operations support is tremendous,” says Dr. Smythe. “Whether that is the accuracy of interventions or the effective use of manpower – these are things that physicians struggle with. That is the imperative.”
3. Enabling population health management
Combining clinical decision support systems with patient self-management, population health management can also benefit from AI. Using predictive analytics with patient populations, healthcare providers will be able to take preventative action, reduce health risk, and save unnecessary costs.
As the population ages, so does a desire to age in place when possible, and to maximize not only disease management, but quality of life as we do so. The possibility of aggregating, analyzing and activating health data from millions of consumers will enable hospitals to see how socio-economic, behavioral, genetic and clinical factors correlate and can offer more targeted, preventative healthcare outside the four walls of the hospital.
4. Empowering consumers, improving patient care
As recently as 2015 patients reported physically carrying x-rays, test results, and other critical health data from one healthcare provider’s office to another3. The burden of multiple referrals, explaining symptoms to new physicians and finding out that their medical history has gaps in it were all too real. Patients now are demanding more personalized, sophisticated and convenient healthcare services.
The great motivation behind AI in healthcare is that increasingly, as patients become more engaged with their own healthcare and better understand their own needs, healthcare will have to take steps towards them and meet them where they are, providing them with health services when they need them, not just when they are ill.
SOURCE
Our Summary for AI in Medicine presents to the eReader the results of the 2020 Survey on that topic, all the live links will take the eReader to the report itself. We provided the reference, below
AI in Healthcare 2020 Leadership Survey Report: About the Survey
The AI in Healthcare team embarked on this survey to gain a deeper understanding of the current state of artificial and augmented intelligence in use and being planned across healthcare in the next few years. We polled readers of AI in Healthcare, AIin.Healthcare and sister brand HealthExec.com over 2 months. All data is presented in this report in aggregate, with individual responses remaining anonymous.
The content in this report reflects the input of 1,238 physicians, executives, IT and administrative leaders in healthcare, medical devices and IT and software development from across the globe, with 75 percent based in the United States. The report focuses on the responses of providers and professionals at the helm of healthcare systems, integrated delivery networks, academic medical centers, hospitals, imaging centers and physician groups across the U.S. For a deeper dive into survey demographics, click here.
Some respondents chose to share more specific demographics that help us better get to know our survey base. Those 165 healthcare leaders work for 38 unique health systems, hospitals, physician groups and imaging or surgery centers, across 39 states and the District of Columbia. They are large, small and mid-sized, for profit, not for profit, academic and government owned. Respondents, too, herald from all levels of leadership. Here are some of the interesting titles who chimed in—and we are thankful they did: CEO, CFO, CMO, CIO, chief innovation officer, chief data officer, chief administrative officer, medical director of quality, senior VP of quality and innovation officer, system director of transformation, VP of service line development, and plenty of physicians, directors of ICU, imaging, cath lab and surgery, nurses and technologists.
In this report we unpack current trends in AI and machine learning, drill into data from various perspectives such as the C-suite and the physician leader, and learn how healthcare systems are using and planning to use AI. Turn the page and see where we are and where we’re going.
Author: Mary C. Tierney, MS, Chief Content Officer, AI in Healthcare magazine and AIin.Healthcare
AI in Healthcare 2020 Leadership Survey Report
SOURCE
Part 4: Single Cell Genomics
Introduction to Part 4: Single Cell Genomics – Voice of Aviva Lev-Ari & Stephen Williams
Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis
Subjects:
The evolution of the discipline consists of the following research topics, see References in https://www.nature.com/articles/s41587-019-0315-8
- Analytical methods: References 5,6,11,27,35,40,44,46,47
- Computational and machine learning: 7,8,14
- Transcription-related phenomena: 9,10,17,18,19,21,.22,31,34,37,38,39,43,45,48
- Promoters: 13,15
- Binding properties: 10,12,16
- Role for sequences: 18,19,20
- Classical regulation: 23 24, 36
- Nucleosomes: 25,26,28,29,32,33
- Chromatin: 30,31
- Phenotypes: 41,42,49,50
The Scientific Frontier is presented in “Deciphering eukaryotic gene-regulatory logic with 100 million random promoters”
Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat Biotechnol (2019) doi:10.1038/s41587-019-0315-8
Abstract
How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.
The Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis is presented in the following Table, 1986 – 2019
50 Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034 e1026 (2019). 5 Muerdter, F. et al. Resolving systematic errors in widely used enhancer activity assays in human cells. Nat. Methods 15, 141–149 (2018). 6 Wang, X. et al. High-resolution genome-wide functional dissection of transcriptional regulatory regions and nucleotides in human. Nat. Commun. 9, 5380 (2018). 15 Yona, A. H., Alm, E. J. & Gore, J. Random sequences rapidly evolve into de novo promoters. Nat. Commun. 9, 1530 (2018). 4 van Arensbergen, J. et al. Genome-wide mapping of autonomous promoter activity in human cells. Nat. Biotechnol. 35, 145–153 (2017). 14 Cuperus, J. T. et al. Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences. Genome Res. 27, 2015–2024 (2017). 31 Levo, M. et al. Systematic investigation of transcription factor activity in the context of chromatin using massively parallel binding and expression assays. Mol. Cell 65, 604–617 e606 (2017). 49 Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017). 54 de Boer, C. High-efficiency S. cerevisiae lithium acetate transformation. protocols.io https://doi.org/10.17504/protocols.io.j4tcqwn (2017). 59 Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems. arXiv 1603.04467 (2016). 20 Shalem, O. et al. Systematic dissection of the sequence determinants of gene 3’ end mediated expression control. PLoS Genet. 11, e1005147 (2015). 55 Deng, C., Daley, T. & Smith, A. D. Applications of species accumulation curves in large-scale biological data analysis. Quant. Biol. 3, 135–144 (2015). 9 Hughes, T. R. & de Boer, C. G. Mapping yeast transcriptional networks. Genetics 195, 9–36 (2013). 10 Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013). 19 Kosuri, S. et al. Composability of regulatory sequences controlling transcription and translation in Escherichia coli. Proc. Natl Acad. Sci. USA 110, 14024–14029 (2013). 7 Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012). 18 de Boer, C. G. & Hughes, T. R. YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities. Nucleic Acids Res. 40, D169–D179 (2012). 56 Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012). 61 Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012). 11 Nutiu, R. et al. Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument. Nat. 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D. & Lieb, J. D. Whole-genome comparison of Leu3 binding in vitro and in vivo reveals the importance of nucleosome occupancy in target site selection. Genome Res. 16, 1517–1528 (2006). 34 Roberts, G. G. & Hudson, A. P. Transcriptome profiling of Saccharomyces cerevisiae during a transition from fermentative to glycerol-based respiratory growth reveals extensive metabolic and structural remodeling. Mol. Genet. Genomics 276, 170–186 (2006). 48 Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Gen. Res. 16, 962–972 (2006). 53 Tong, A. H. & Boone, C. Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol. Biol. 313, 171–192 (2006). 57 Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006). 62 Chua, G. et al. Identifying transcription factor functions and targets by phenotypic activation. Proc. Natl Acad. Sci. USA 103, 12045–12050 (2006). 17 Arnosti, D. N. & Kulkarni, M. M. Transcriptional enhancers: intelligent enhanceosomes or flexible billboards? J. Cell. Biochem. 94, 890–898 (2005). 21 Granek, J. A. & Clarke, N. D. Explicit equilibrium modeling of transcription-factor binding and gene regulation. Genome Biol. 6, R87 (2005). 1 Beer, M. A. & Tavazoie, S. Predicting gene expression from sequence. Cell 117, 185–198 (2004). 28 Bernstein, B. E., Liu, C. L., Humphrey, E. L., Perlstein, E. O. & Schreiber, S. L. Global nucleosome occupancy in yeast. Genome Biol. 5, R62 (2004). 44 Kim, T. S., Kim, H. Y., Yoon, J. H. & Kang, H. S. Recruitment of the Swi/Snf complex by Ste12-Tec1 promotes Flo8-Mss11-mediated activation of STA1 expression. Mol. Cell. Biol. 24, 9542–9556 (2004). 45 Harbison, C. T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004). 60 Kent, N. A., Eibert, S. M. & Mellor, J. Cbf1p is required for chromatin remodeling at promoter-proximal CACGTG motifs in yeast. J. Biol. Chem. 279, 27116–27123 (2004). 22 Kulkarni, M. M. & Arnosti, D. N. Information display by transcriptional enhancers. Development 130, 6569–6575 (2003). 24 Conlon, E. M., Liu, X. S., Lieb, J. D. & Liu, J. S. Integrating regulatory motif discovery and genome-wide expression analysis. Proc. Natl Acad. Sci. USA 100, 3339–3344 (2003). 43 Neely, K. E., Hassan, A. H., Brown, C. E., Howe, L. & Workman, J. L. Transcription activator interactions with multiple SWI/SNF subunits. Mol. Cell. Biol. 22, 1615–1625 (2002). 23 Bussemaker, H. J., Li, H. & Siggia, E. D. Regulatory element detection using correlation with expression. Nat. Genet. 27, 167–171 (2001). 37 Haurie, V. et al. The transcriptional activator Cat8p provides a major contribution to the reprogramming of carbon metabolism during the diauxic shift in Saccharomyces cerevisiae. J. Biol. Chem. 276, 76–85 (2001). 39 Grauslund, M. & Ronnow, B. Carbon source-dependent transcriptional regulation of the mitochondrial glycerol-3-phosphate dehydrogenase gene, GUT2, from Saccharomyces cerevisiae. Can. J. Microbiol. 46, 1096–1100 (2000). 42 Cullen, P. J. & Sprague, G. F. Jr. Glucose depletion causes haploid invasive growth in yeast. Proc. Natl Acad. Sci. USA 97, 13619–13624 (2000). 38 Sato, T. et al. TheE-box DNA binding protein Sgc1p suppresses the gcr2 mutation, which is involved in transcriptional activation of glycolytic genes in Saccharomyces cerevisiae. FEBS Lett. 463, 307–311 (1999). 40 Madhani, H. D. & Fink, G. R. Combinatorial control required for the specificity of yeast MAPK signaling. Science 275, 1314–1317 (1997). 41 Gavrias, V., Andrianopoulos, A., Gimeno, C. J. & Timberlake, W. E. Saccharomyces cerevisiae TEC1 is required for pseudohyphal growth. Mol. Microbiol. 19, 1255–1263 (1996). 36 Hedges, D., Proft, M. & Entian, K. D. 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Identification and characterization of HAP4: a third component of the CCAAT-bound HAP2/HAP3 heteromer. Genes Dev. 3, 1166–1178 (1989). 13 Horwitz, M. S. & Loeb, L. A. Promoters selected from random DNA sequences. Proc. Natl Acad. Sci. USA 83, 7405–7409 (1986).
To access each reference as a live link, go to the number in the first column in the Table and look it up in the List of References in the Link, below
https://www.nature.com/articles/s41587-019-0315-8
4.1 The Science
4.1.1 Single-cell biology
Special | 05 July 2017
https://www.nature.com/collections/gbljnzchgg
4.1.2 The race to map the human body — one cell at a time, A host of detailed cell atlases could revolutionize understanding of cancer and other diseases
- by Heidi Ledford 20 February 2017
https://www.nature.com/news/the-race-to-map-the-human-body-one-cell-at-a-time-1.21508
4.1.3 Single-cell Genomics: Directions in Computational and Systems Biology – Contributions of Prof. Aviv Regev @Broad Institute of MIT and Harvard, Cochair, the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute
Curator: Aviva Lev-Ari, PhD, RN
4.1.4 Cellular Genetics
https://www.sanger.ac.uk/science/programmes/cellular-genetics
4.1.5 Cellular Genomics
https://www.garvan.org.au/research/cellular-genomics
4.1.6 SINGLE CELL GENOMICS 2019 – sometimes the sum of the parts is greater than the whole, September 24-26, 2019, Djurönäset, Stockholm, Sweden http://www.weizmann.ac.il/conferences/SCG2019/single-cell-genomics-2019
Reporter: Aviva Lev-Ari, PhD, RN
4.1.7 Norwich Single-Cell Symposium 2019, Earlham Institute, single-cell genomics technologies and their application in microbial, plant, animal and human health and disease, October 16-17, 2019, 10AM-5PM
Reporter: Aviva Lev-Ari, PhD, RN
4.1.8 Newly Found Functions of B Cell
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
https://pharmaceuticalintelligence.com/2019/05/23/newly-found-functions-of-b-cell/
4.1.9 RESEARCH HIGHLIGHTS: HUMAN CELL ATLAS
https://www.broadinstitute.org/research-highlights-human-cell-atlas
4.2 Technologies and Methodologies
4.2.1 How to build a human cell atlas – Aviv Regev is a maven of hard-core biological analyses. Now she is part of an effort to map every cell in the human body.
Anna Nowogrodzki, 05 July 2017, Article tools
https://www.nature.com/news/how-to-build-a-human-cell-atlas-1.22239
4.2.2 Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab
Reporter: Aviva Lev-Ari, PhD, RN
4.2.3 Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing
Curator: Aviva Lev-Ari, PhD, RN
4.2.4 Three Technology Leaders in Single Cell Sequencing: 10X Genomics, Illumina and MissionBio
Reporter: Aviva Lev-Ari, PhD, RN
4.2.5 scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
4.2.6 Nano-guided cell networks: new methods to detect intracellular signaling and implications
Curator: Stephen J. Williams, PhD
4.3 Clinical Aspects
4.3.1 Using single cell sequencing data to model the evolutionary history of a tumor.
Kim KI, Simon R.
BMC Bioinformatics. 2014 Jan 24;15:27. doi: 10.1186/1471-2105-15-27.
PMID:
4.3.2 eProceedings 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA
Real Time Press Coverage: Aviva Lev-Ari, PhD, RN
4.3.3 The Impact of Heterogeneity on Single-Cell Sequencing
Samantha L. Goldman1,2, Matthew MacKay1,2, Ebrahim Afshinnekoo1,2,3, Ari M. Melnick4, Shuxiu Wu5,6 and Christopher E. Mason1,2,3,7*
https://www.frontiersin.org/articles/10.3389/fgene.2019.00008/full
4.3.4 Single-cell approaches to immune profiling
https://www.nature.com/articles/d41586-018-05214-w
4.3.5 Single-cell sequencing made simple. Data from thousands of single cells can be tricky to analyse, but software advances are making it easier.
https://www.nature.com/news/single-cell-sequencing-made-simple-1.22233
4.3.6 Single-cell RNA-seq helps in finding intra-tumoral heterogeneity in pancreatic cancer
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
4.3.7 Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity
Curator: Stephen J. Williams, PhD
4.4 Business and Legal
4.4.1 iBioChips integrate diagnostic assays and cellular engineering into miniaturized chips that achieve cutting-edge sensitivity and high-throughput. We have resolved traditional biotech challenges with innovative biochip approaches
4.4.2 Targeted Single-Cell Solutions for High Impact Applications – Mission Bio’s Tapestri® Platform is the only technology that provides single-cell targeted DNA sequencing at single-base resolution.
Part 4: Summary – Single Cell Genomics – Voice of Stephen Williams
Part 5: Evolution Biology Genomics Modeling
@Feldman Lab, Stanford University
Written and Curated by Prof. Marc Feldman
5.1 Human Genomic Variation, Population Diversity, and Genome-Wide Associations
Author: Marcus W. Feldman, PhD
Part 5: Introduction
This article covers the following five topics: Population Genomics, Human Migration, Heritability, Population-specific statistics and interactions and the need for more diversity and the pitfalls of insufficient diversity.
Each topic covers the studies included in the references, as follows:
- Population Genomics: refs 1-7, 9, 20, 21
- Human Migration: 4, 5 ,6
- Heritability: 8, 10-14, 16, 17, 22, 23
- Population-specific statistics and interactions: 21, 24- 27
- The need for more diversity and the pitfalls of insufficient diversity: 28-33
The genomic age has taken hold and with it a new era in the study and polemic of the relationships between genetic variation, population grouping, genotype-phenotype association, and human evolution. Prior to 2002, partitioning of genetic variation in worldwide human populations relied on blood groups and enzyme genotypes (1). The initiation of the Human Genome Diversity Project (HGDP) (2) ushered in the worldwide analysis of DNA from 1,056 human cell lines maintained at Fondation Jean Dausset in Paris.
The first HGDP study (3) analyzed 377 short tandem DNA repeats from 51 populations representing Africa, Europe, Asia, the Americas, and Oceania. Rosenberg et al. (3) showed that although the populations showed continental clustering, only about five percent of the genetic variation could be attributed to between-continent differences—what have classically been called “races”. It is worth noting that, although this study did not mention the words “race” or “phenotype” or “disease”, it was awarded The Lancet’s paper of the year in biomedical research.
The number of short tandem repeats was more than doubled in the next HGDP study in 2005 (4) in which 783 microsatellites were analyzed across 53 populations. One of the important findings in this second study was a very significant negative regression of heterozygosity on geographic distance of populations from Africa. Heterozygosity is highest in Africans and lowest in indigenous Americans. This negative regression is consistent with a serial-founder effect, a scenario in which a group of modern humans left Africa between fifty and seventy-five thousand years ago, first through the Levant then to Europe and Asia and finally to the Americas. Each group of new continental settlers was presumably a sample of the group that settled the previous continent. The basic form of the serial founder effect (5) remains as a basic model for the settlement of the world by modern humans after the initial “out-of-Africa” process (5).
Single nucleotide polymorphisms (SNPs) constituted the next data to drive the story of modern human evolution using the HGDP. Some of the original HGDP samples were removed because they appeared to be derived from related individuals, leaving 938 unrelated individuals sampled from 51 populations. With 650,000 SNPs, principal components analysis separated French samples from Tuscan and these from other Europeans. Again, the correlation between heterozygosity and distance from Addis Ababa was stronger than –0.9 (6). Recent technologies have allowed variation at millions of SNPs to be studied at finer geographic scale than the HGDP. In specific populations, variation at the ultimate level, DNA sequences, has been possible; examples are the U.K. biobank, which has some 500,000 samples of DNA from British people, and complete sequences of these are expected in 2020, and the Uganda 2000 Genomes (UG2G) project (7). Such projects can reveal population structure at a very fine level and the sequencing data can be used in conjunction with millions of SNPs sampled from thousands of people. An important lesson to be drawn from the many studies on genomic variation within and between populations is that in the search for associations between DNA variants and specific phenotypes, including diseases, the data analysis must control for cryptic substructure in the study population.
A genome-wide association study (GWA, plural GWAS) attempts to detect statistically association of genomic variation with a phenotype. Examples of phenotypes that have been subject to such association studies are height, body mass index (BMI), and schizophrenia (8–12). In most cases, the phenotype is regarded as “polygenic”; that is, no single gene or small number of genes (or SNPs) are known to produce the association with the trait. Instead, regression statistics from the potentially millions of SNPs are accumulated to test for overall significance of the association with the studied trait. The cumulative risk derived from aggregating the contributions of the many DNA variants associated with a complex trait or disease is referred to as a “polygenic risk score” (PRS) (13).
The statistical technique most widely used to estimate the extent of the association is called “GREML” (genomic-relatedness-based restricted maximum likelihood) (8), which produces an estimate related to classical heritability. This estimate is always less than that obtained from classical pedigree (or twin) studies and has been widely used in social science applications, for example, genetic association with educational attainment (14). Recently it has been suggested that the term “polygenic” used in connection with traits such as height, schizophrenia, and educational attainment should be replaced by the term “omnigenic” to indicate that thousands or hundreds of thousands of genomic elements may each make infinitesimal contributions to such complex traits (15). A recent reassessment of the relationship between classical heritability (from correlations between relatives) and SNP-based statistics can be found in Feldman and Ramachandran (16).
GWAS explain only a small proportion of the expected genetic component of the risk of diabetes, Crohn’s disease, height, and educational attainment compared to estimates from pedigrees. Eighteen genome-wide significant markers for type 2 diabetes, for example, explain only six percent of the expected heritability, and forty genome-wide significant loci explain only five percent of the heritability of height (17). This discrepancy between the findings of GWAS and classical methods is called the problem of “missing heritability” (16, 17).
The classical definition of heritability is the proportion of the phenotypic variance due to genetic effects, and it was historically estimated from correlations between relatives, such as twins. It is often denoted . Those SNPs that are strongly associated with a trait make up another estimate of heritability, , which is almost lower than . The use of almost all SNPs (not just the strongly associated one) gives another estimate commonly called , and as an empirical rule, . These estimates vary across the type of trait and the population studied. Differences in continental ancestry and contributions from the environment can cause large differences between populations in heritability estimates (18).
A recent study has proposed a mechanism whereby a large fraction of all genes can contribute in indirect ways; that is, the genes are not directly involved in the trait in question. This mechanism involved trans-regulatory elements, which are SNPs that have weak effects on gene expression that can diffuse and affect how core genes are expressed. In this way, trans effects due to potentially very large numbers of elements can become co-regulators of gene expression and, in fact, be the basis of omnigenic effects on phenotypes (19).
Although the fraction of genomic variance attributable to continental differences is very small, millions of SNPs differences between neighboring populations may be detectable at the statistical level, for example, within Europe (20). This emphasizes the importance of controlling for substructure even in a large population sample from one geographic area. An example is instructive. In a study of BMI and obesity risk among African Americans, being U.S.-born was associated with a substantially higher BMI and risk of obesity in both men and women, but genetic ancestry was associated with obesity only among U.S.-born women (21). Substructure among African Americans is therefore important in this group as it is in Europeans and European Americans.
Genome-wide association studies have historically been mostly done on samples of humans with European ancestry. This includes studies of medically important phenotypes, as well as others that address intelligence or educational attainment (14, 22, 23). In the first ten years of polygenic scoring studies (2008–2017 inclusive), 67 percent included exclusively people of European ancestry while only 3.8 percent were of African, Hispanic, or indigenous peoples. Nineteen percent included only people of East Asian ancestry (24). The importance of this imbalance resides in the finding that the predictive performance of PRS derived from people of European ancestry is very much worse in people of African ancestry (24). Extreme caution is necessary in generalizing from GWAS in European-ancestry samples to people of non-European ancestry.
Several recent studies have called for GWAS studies to be carried out in populations that are more representative of that in the U.S. and around the world. One reason is that disease burden varies among ancestrally diverse populations. For example, in the U.S., African Americans have the highest prevalence of hypertension and have the highest mortality rates from renal failure (25–27). On a worldwide perspective, available tools have generally restricted applicability to GWAS. For example, Illumina’s MEGA genotyping array includes African variation representative of western African, but there is poor coverage of eastern Africa. For these reasons, workers associated with the study called “PAGE” (population architecture using genomics and epidemiology) have stressed the importance of including ancestrally diverse populations in both data collection and development of methods for data analysis (28).
The arrays used in GWAS genotype a fraction of the common DNA variation. A reference panel is a set of genetic variants from a population that is used to design arrays, to catalog genetic variants and to compare genomic regions between populations. “Imputation” is a computational approach to inferring genotypes at variants that have not been genotyped on a GWAS array by comparing the observed genotypes to those in a reference sample. Thus, imputation can massively increase the number of markers than can be applied to estimating associations with phenotypes. Application of this process depends on having an appropriate reference set, and it has been stressed (29) that in order to study associations in more diverse and representative populations, the diversity of reference sets for imputation must be expanded to include more ancestrally diverse people.
The PAGE study carried out a GWAS of 26 clinical and behavioral traits in 49,839 non-Europeans (30). A framework was established for analyzing populations that can vary genomically. The study focused on ancestrally diverse American populations; HGDP analyses mentioned above suggest that the different ancestries may have contributed either different variants or produced different allele frequencies in these groups. The gene HBB encodes the adult hemoglobin chain and plays a role in sickle cell anemia. In the PAGE study, a strong association was found between a SNP in HBB and HbA1c levels (HbA1c levels are used to diagnose Type 1 and Type 2 pre-diabetes). Interestingly, the majority of this strong association came from the Hispanic/Latino group in the study (30). This demonstrates how important it is to carry out reference studies in minorities (in countries like the U.S. with large ethnic minorities) and in other continents, where little is known about genotype-phenotype associations.
It is not only GWAS studies of complex phenotypes that can produce population-specific inferences. Sirugo et al. (31) point out that the cystic fibrosis (CF) mutation , which is the most common cause of CF in Europeans, where it accounts for 70 percent of cases, accounts for only 29 percent of CF cases in the African diaspora. However, a different mutation accounts for between 15 percent and 65 percent of CF patients in South Africa. In the same way, more than 3,000 mutations in 65 genes are known to cause retinitis pigmentosa (RP) with different modes of inheritance. Many of these mutations have only been characterized in Europeans; so much more data is needed from other ethnic groups around the world. For complex traits, as well as these Mendelian diseases, there can be variation among ethnic groups in SNP effect sizes, and this variation can provide clinically relevant insights (31).
An important class of phenotype variation concerns response to drugs. For many medicines there is some genetic contribution to the patients’ response and the responses can be expected to vary with ancestral background, as we have seen with other GWAS. Pharmacogenetic variants with clinical relevance are common and vary greatly around the world (32). For example, the cytochrome P450 (CYP) 3A5*3 allele reaches a frequency of 98 percent in an Iranian population but only 11 percent in a Ngoni population from Malawi. Huddart et al. (32) present seven geographically defined groups that they suggest should be useful for analysis of worldwide pharmacogenetic variation: American, Central/South Asian, East Asian, European, Near Eastern Oceanian, and Sub-Saharan African. They also suggest two admixed groups: Afro American/Afro Caribbean and Latino. It is interesting that the seven groups suggested for genetic variation in reaction to drugs agree exactly with the HGDP clusters found using 650,000 SNPs by Li et al. (6). This suggests that studies designed to study the evolutionary genetic history of modern humans can inform research into population differences in clinically relevant traits. A major caveat, however, is that aspects of the environment, which also vary geographically or by socio-economic status, may interact with genotypes to produce the traits under study (33). So far GWAS have placed little emphasis on finding the salient environmental variables.
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- Liu, X., Y.I. Li, and J.K. Pritchard. 2019. Trans effects on gene expression can drive omnigenic inheritance. Cell 177: 1022–1034.
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- Vishnu, A., G.M. Belbin, G.L. Wojcik, E.P. Bottinger, C.R. Gignoux, E.E. Kenny, and R.J.F. Loos. 2019. The role of country of birth, and genetic and self-identified ancestry, in obesity susceptibility among African and Hispanic Americans. Am. J. Clin. Nutr. 110: 16–23.
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- Rosenberg, N.A., M.D. Edge, J.K. Pritchard, and M.W. Feldman. 2019. Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences. Evol. Med. Public Health 2019(1): 26–34.
Part 5: Summary – Genomics Modeling in Evolution – Voice of Professor Feldman
Studies designed to study the evolutionary genetic history of modern humans can inform research into population differences in clinically relevant traits. A major caveat, however, is that aspects of the environment, which also vary geographically or by socio-economic status, may interact with genotypes to produce the traits under study (33). So far GWAS have placed little emphasis on finding the salient environmental variables.
Part 6: Simulation Modeling in Genomics
Introduction to Part 6: Simulation Modeling – Voice of Professor Williams
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Simulation Tools of Genomic Next Generation Sequencing Data: Comparative Analysis & Genetic Simulation Resources
Bibliography Curator: Aviva Lev-Ari, PhD, RN
Introduction
What is next generation sequencing?
Behjati S, Tarpey PS.
Arch Dis Child Educ Pract Ed. 2013 Dec;98(6):236-8. doi: 10.1136/archdischild-2013-304340. Epub 2013 Aug 28. Review.
Computational pan-genomics: status, promises and challenges.
Computational Pan-Genomics Consortium.
Brief Bioinform. 2018 Jan 1;19(1):118-135. doi: 10.1093/bib/bbw089. Review.
Dahlö M, Scofield DG, Schaal W, Spjuth O.
Gigascience. 2018 May 1;7(5). doi: 10.1093/gigascience/giy028.
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NGS in the Clinic
[Clinical Applications of Next-Generation Sequencing].
Rebollar-Vega RG, Arriaga-Canon C, de la Rosa-Velázquez IA.
Rev Invest Clin. 2018;70(4):153-157. doi: 10.24875/RIC.18002544.
Clinical Genomics: Challenges and Opportunities.
Vijay P, McIntyre AB, Mason CE, Greenfield JP, Li S.
Crit Rev Eukaryot Gene Expr. 2016;26(2):97-113. doi: 10.1615/CritRevEukaryotGeneExpr.2016015724. Review.
Next-generation sequencing in the clinic: promises and challenges.
Xuan J, Yu Y, Qing T, Guo L, Shi L.
Cancer Lett. 2013 Nov 1;340(2):284-95. doi: 10.1016/j.canlet.2012.11.025. Epub 2012 Nov 19. Review.
The Future of Whole-Genome Sequencing for Public Health and the Clinic.
Allard MW.
J Clin Microbiol. 2016 Aug;54(8):1946-8. doi: 10.1128/JCM.01082-16. Epub 2016 Jun 15.
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Roy S, Coldren C, Karunamurthy A, Kip NS, Klee EW, Lincoln SE, Leon A, Pullambhatla M, Temple-Smolkin RL, Voelkerding KV, Wang C, Carter AB.
J Mol Diagn. 2018 Jan;20(1):4-27. doi: 10.1016/j.jmoldx.2017.11.003. Epub 2017 Nov 21. Review.
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- 29154853
6.1 Mutation Analysis – Gene Encoding
Nagy PL, Worman HJ.
Methods Mol Biol. 2018;1840:321-336. doi: 10.1007/978-1-4939-8691-0_22.
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- 30141054
Genome-wide genetic marker discovery and genotyping using next-generation sequencing.
Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML.
Nat Rev Genet. 2011 Jun 17;12(7):499-510. doi: 10.1038/nrg3012. Review.
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Best practices for evaluating mutation prediction methods.
Rogan PK, Zou GY.
Hum Mutat. 2013 Nov;34(11):1581-2. doi: 10.1002/humu.22401. Epub 2013 Sep 10. No abstract available.
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- 23955774
6.2 Mitochondrial Variations
Vellarikkal SK, Dhiman H, Joshi K, Hasija Y, Sivasubbu S, Scaria V.
Hum Mutat. 2015 Apr;36(4):419-24. doi: 10.1002/humu.22767.
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- 25677119
6.3 Variant Analysis
A survey of tools for variant analysis of next-generation genome sequencing data.
Pabinger S, Dander A, Fischer M, Snajder R, Sperk M, Efremova M, Krabichler B, Speicher MR, Zschocke J, Trajanoski Z.
Brief Bioinform. 2014 Mar;15(2):256-78. doi: 10.1093/bib/bbs086. Epub 2013 Jan 21.
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- 23341494
Variant callers for next-generation sequencing data: a comparison study.
Liu X, Han S, Wang Z, Gelernter J, Yang BZ.
PLoS One. 2013 Sep 27;8(9):e75619. doi: 10.1371/journal.pone.0075619. eCollection 2013.
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- 24086590
6.4 Variant Detection in Hereditary Cancer Genes
Lopez-Doriga A, Feliubadaló L, Menéndez M, Lopez-Doriga S, Morón-Duran FD, del Valle J, Tornero E, Montes E, Cuesta R, Campos O, Gómez C, Pineda M, González S, Moreno V, Capellá G, Lázaro C.
Hum Mutat. 2014 Mar;35(3):271-7.
Judkins T, Leclair B, Bowles K, Gutin N, Trost J, McCulloch J, Bhatnagar S, Murray A, Craft J, Wardell B, Bastian M, Mitchell J, Chen J, Tran T, Williams D, Potter J, Jammulapati S, Perry M, Morris B, Roa B, Timms K.
BMC Cancer. 2015 Apr 2;15:215. doi: 10.1186/s12885-015-1224-y.
Clinical Applications of Next-Generation Sequencing in Cancer Diagnosis.
Sabour L, Sabour M, Ghorbian S.
Pathol Oncol Res. 2017 Apr;23(2):225-234. doi: 10.1007/s12253-016-0124-z. Epub 2016 Oct 8. Review.
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Studying cancer genomics through next-generation DNA sequencing and bioinformatics.
Doyle MA, Li J, Doig K, Fellowes A, Wong SQ.
Methods Mol Biol. 2014;1168:83-98. doi: 10.1007/978-1-4939-0847-9_6. Review.
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- 24870132
6.5 Immuno-Informatics
Immunoinformatics and epitope prediction in the age of genomic medicine.
Backert L, Kohlbacher O.
Genome Med. 2015 Nov 20;7:119. doi: 10.1186/s13073-015-0245-0. Review.
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- 26589500
IgSimulator: a versatile immunosequencing simulator.
Safonova Y, Lapidus A, Lill J.
Bioinformatics. 2015 Oct 1;31(19):3213-5. doi: 10.1093/bioinformatics/btv326. Epub 2015 May 25.
Computational genomics tools for dissecting tumour-immune cell interactions.
Hackl H, Charoentong P, Finotello F, Trajanoski Z.
Nat Rev Genet. 2016 Jul 4;17(8):441-58. doi: 10.1038/nrg.2016.67. Review.
- PMID:
- 27376489
6.6 RNA Sequencing
SimBA: A methodology and tools for evaluating the performance of RNA-Seq bioinformatic pipelines.
Audoux J, Salson M, Grosset CF, Beaumeunier S, Holder JM, Commes T, Philippe N.
BMC Bioinformatics. 2017 Sep 29;18(1):428. doi: 10.1186/s12859-017-1831-5.
INDELseek: detection of complex insertions and deletions from next-generation sequencing data.
Au CH, Leung AY, Kwong A, Chan TL, Ma ES.
BMC Genomics. 2017 Jan 5;18(1):16. doi: 10.1186/s12864-016-3449-9.
The State of Software for Evolutionary Biology.
Darriba D, Flouri T, Stamatakis A.
Mol Biol Evol. 2018 May 1;35(5):1037-1046. doi: 10.1093/molbev/msy014. Review.
- PMID:
- 29385525
6.9 Simulation Programs
Published online 2016 Jun 20. doi: 10.1038/nrg.2016.57
Zhao M, Liu D, Qu H.
Brief Funct Genomics. 2017 May 1;16(3):121-128. doi: 10.1093/bfgp/elw012. Review.
6.10 A comparison of tools for the simulation of genomic next-generation sequencing data
Online Summary
There is a large number of tools for the simulation of genomic data for all currently available NGS platforms, with partially overlapped functionality. Here we review 23 of these tools, highlighting their distinct functionalities, requirements and potential applications.
The parameterization of these simulators is often complex. The user may decide between using existing sets of parameters values called profiles or re-estimating them from its own data.
Parameters than can be modulated in these simulations include the effects of the PCR amplification of the libraries, read features and quality scores, base call errors, variation of sequencing depth across the genomes and the introduction of genomic variants.
Several types of genomic variants can be introduced in the simulated reads, such as SNPs, indels, inversions, translocations, copy-number variants and short-tandem repeats.
Reads can be generated from single or multiple genomes, and with distinct ploidy levels. NGS data from metagenomic communities can be simulated given an “abundance profile” that reflects the proportion of taxa in a given sample.
Many of the simulators have not been formally described and/or tested in dedicated publications. We encourage the formal publication of these tools and the realization of comprehensive, comparative benchmarkings.
Choosing among the different genomic NGS simulators is not easy. Here we provide a guidance tree to help userschoosing a suitable tool for their specific interests.
Abstract
Computer simulation of genomic data has become increasingly popular for assessing and validating biological models or to gain understanding about specific datasets. Multiple computational tools for the simulation of next-generation sequencing (NGS) data have been developed in recent years, which could be used to compare existing and new NGS analytical pipelines. Here we review 23 of these tools, highlighting their distinct functionality, requirements and potential applications. We also provide a decision tree for the informed selection of an appropriate NGS simulation tool for the specific question at hand.
Image source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5224698/
An overview of current NGS technologies
The most popular NGS technologies on the market are Illumina’s sequencing by synthesis, which is probably the most widely used platform at present17, Roche’s 454 pyrosequencing (454), SOLiD sequencing-by-ligation (SOLiD), IonTorrent semiconductor sequencing18 (IonTorrent), Pacific Biosciences’s (PacBio) single molecule real-time sequencing19, and Oxford Nanopore Technologies (Nanopore) single-cell DNA template strand sequencing. These strategies can differ, for example, regarding the type of reads they produce or the kind of sequencing errors they introduce (Table 1). Only two of the current technologies (Illumina and SOLiD) are capable of producing all three sequencing read types —single end, paired end and mate pair. Read length is also dependent on the machine and the kit used; in platforms like Illumina, SOLiD, or IonTorrent it is possible to specify the number of desired base pairs per read. According to the sequencing run type selected it is possible to obtain reads with maximum lengths of 75 bp (SOLiD), 300 bp (Illumina) or 400bp (IonTorrent). On the other hand, in platforms like 454, Nanopore or PacBio, information is only given about the mean and maximum read length that can be obtained, with average lengths of 700 bp, 10 kb and 15 kb and maximum lengths of 1 kb, 10 kb and 15 kb, respectively. Error rates vary depending on the platform from <=1% in Illumina to ~30% in Nanopore. Further overviews and comparisons of NGS strategies can be found in 5,20–22.
Table 1
Main characteristics of current NGS technologies.
Technology Run Type Maximum Read Length Quality Scores Error Rates References Single-read Paired-end Mate-pair Illumina X X X 300 bp > Q30 0.0034 – 1% 65 SOLiD X X X 75 bp > Q30 0.01 – 1% 66 IonTorrent X X 400 bp ~ Q20 1.78% 22 454 X X ~700 bp (up to 1 Kb) > Q20 1.07 – 1.7% 59,67 Nanopore X 5.4 – 10 Kb NAY 10 – 40% 68–72 PacBio X ~15 Kb (up to 40 Kb) < Q10 5 – 10% 22,73–75 Simulation parameters
The existing sequencing platforms use distinct protocols that result in datasets with different characteristics1. Many of these attributes can be taken into account by the simulators (Fig. 2), although there is not a single tool that incorporates all possible variations. The main characteristics of the 23 simulators considered here are summarized in Tables 2 and and3.3. These tools differ in multiple aspects, such as sequencing technology, input requirements or output format, but maintain several common aspects. With some exceptions, all programs need a reference sequence, multiple parameter values indicating the characteristics of the sequencing experiment to be simulated (read length, error distribution, type of variation to be generated, if any, etc.) and/or a profile (a set of parameter values, conditions and/or data used for controlling the simulation), which can be provided by the simulator or estimated de novo from empirical data. The outcome will be aligned or unaligned reads in different standard file formats, such as FASTQ, FASTA or BAM. An overview of the NGS data simulation process is represented in Fig. 3. In the following sections we delve into the different steps involved.
General overview of the sequencing process and steps that can be parameterized in the simulations.
NGS simulators try to imitate the real sequencing process as closely as possible by considering all the steps that could influence the characteristics of the reads. a | NGS simulators do not take into account the effect of the different DNA extraction protocols in the resulting data. However, they can consider whether the sample we want to sequence includes one or more individuals, from the same or different organisms (e.g., pool-sequencing, metagenomics). Pools of related genomes can be simulated by replicating the reference sequence and introducing variants on the resulting genomes. Some tools can also simulate metagenomes with distinct taxa abundance. b | Simulators can try to mimic the length range of DNA fragmentation (empirically obtained by sonication or digestion protocols) or assume a fixed amplicon length. c | Library preparation involves ligating sequencing–platform dependent adaptors and/or barcodes to the selected DNA fragments (inserts). Some simulators can control the insert size, and produce reads with adaptors/barcodes. d | | Most NGS techniques include an amplification step for the preparation of libraries. Several simulators can take this step into account (for example, by introducing errors and/or chimaeras), with the possibility of specifying the number of reads per amplicons. e | Sequencing runs imply a decision about coverage, read length, read type (single-end, paired-end, mate-pair) and a given platform (with their specific errors and biases). Simulators exist for the different platforms, and they can use particular parameter profiles, often estimated from real data.
General overview of NGS simulation.
The simulation process begins with the input of a reference sequence (most cases) and simulation parameters. Some of the parameters can be given via a profile, that is estimated (by the simulator or other tools) from other reads or alignments. The outcome of this process may be reads (with or without quality information) or genome alignments in different formats.
CONCLUSIONS
NGS is having a big impact in a broad range of areas that benefit from genetic information, from medical genomics, phylogenetic and population genomics, to the reconstruction of ancient genomes, epigenomics and environmental barcoding. These applications include approaches such as de novo sequencing, resequencing, target sequencing or genome reduction methods. In all cases, caution is necessary in choosing a proper sequencing design and/or a reliable analytical approach for the specific biological question of interest. The simulation of NGS data can be extremely useful for planning experiments, testing hypotheses, benchmarking tools and evaluating particular results. Given a reference genome or dataset, for instance, one can play with an array of sequencing technologies to choose the best-suited technology and parameters for the particular goal, possibly optimizing time and costs. Yet, this is still not the standard practice and researchers often base their choices on practical considerations like technology and money availability. As shown throughout this Review, simulation of NGS data from known genomes or transcriptomes can be extremely useful when evaluating assembly, mapping, phasing or genotyping algorithms e.g. 2,7,10,13,64 exposing their advantages and drawbacks under different circumstances.
Altogether, current NGS simulators consider most, if not all, of the important features regarding the generation of NGS data. However, they are not problem-free. The different simulators are largely redundant, implementing the same or very similar procedures. In our opinion, many are poorly documented and can be difficult to use for non-experts, and some of them are no longer maintained. Most importantly, for the most part they have not been benchmarked or validated. Remarkably, among the 23 tools considered here, only 13 have been described in dedicated application notes, 3 have been mentioned as add-ons in the methods section of bigger articles, and 5 have never been referenced in a journal. Indeed, peer-reviewed publication of these tools in dedicated articles would be highly desirable. While this would not definitively guarantee quality, at least it would encourage authors to reach minimum standards in terms of validation, benchmarking, and documentation. Collaborative efforts like the Assemblathon e.g. 27 or iEvo (http://www.ievobio.org/) might be also a source of inspiration. Meanwhile, we hope that the decision tree presented in Fig. 1 helps users making appropriate choices.
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Part 6: Summary – Simulation Modeling – Voice of Professor Williams
Part 7: Applications of Genomics:
Genotypes, Phenotypes and Complex Diseases
Introduction to Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases – Voice of Stephen Williams
See
7.1 Genome-wide associations with complex diseases (GWAS)
7.1.1 Crowdsourcing Genetic Data Yields Discovery of DNA loci associated with Major Depressive Disorder (MDD) in European Descendants
Reporter: Kelly Perlman
7.1.2 Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies
Writer and Reporter: Stephen J. Williams, Ph.D.
7.1.3 23andMe Genome-Wide Association Study on Human propensity to Get up early or Sleep in the Morning
Reporter: Aviva Lev-Ari, PhD, RN
7.2 Non-coding DNA and phenotypes—including diseases like cancer
7.2.1 Novel Discoveries in Molecular Biology and Biomedical Science
Curator: Larry H. Bernstein, MD, FCAP
7.2.2 Genomic expression carried over from Neanderthal DNA
Larry H. Bernstein, MD, FCAP, Curator
7.2.3 Junk DNA and Breast Cancer
Larry H. Bernstein, MD, FCAP, Curator
https://pharmaceuticalintelligence.com/2016/02/02/junk-dna-and-breast-cancer/
7.3 Transcriptomic and ‘omic associations with phenotypes including cancer and rare variant diseases
7.3.1 scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
7.3.2 Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line
Reporter: Stephen J. Williams, PhD
7.3.3 Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing
Curator: Stephen J. Williams, PhD
7.3.4 Millions of inherited DNA differences – which ones matter: NIH Grants in Genomics to research Disease Risk
Reporter: Aviva Lev-Ari, PhD, RN
7.4 Applications of Bioinformatic Analysis of ‘Omic Data
7.4.1 A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information
Reporter: Stephen J. Williams, Ph.D.
7.4.2 Bioinformatics Tool Review: Genome Variant Analysis Tools
Curator: Stephen J. Williams, Ph.D.
7.4.3 Bioinformatic Tools for RNA-Seq Analysis
Curator: Stephen J. Williams, Ph.D
https://pharmaceuticalintelligence.com/2019/12/18/bioinformatic-tools-for-rnaseq-a-curation/
7.5 Population-level genomics and the meaning of group differences
7.5.1 Genomics and Evolution
Author: Marcus W. Feldman, PhD
https://pharmaceuticalintelligence.com/2013/02/14/genomics-and-evolution/
7.5.2 Tandem Repeats, with Application to Human Population-Divergence Time
Larry H. Bernstein, MD, FCAP, Curator
7.5.3 Gender affects the prevalence of the cancer type
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
https://pharmaceuticalintelligence.com/2019/04/02/gender-affects-the-prevalence-of-the-cancer-type/
7.5.4 Access to Precision Medicine: Genomics is failing on Diversity
Reporter: Aviva Lev- Ari, PhD, RN
7.5.5 Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies
Curator: Stephen J. Williams, PhD
7.6 Targeting drugs for complex diseases
7.6.1 Anti-tumor necrosis factor drugs (TNF inhibitors) is the treatment for otulipenia, a new inflammatory disease discovered by NIH researchers using NGS
Reporter: Aviva Lev-Ari, PhD, RN
7.6.2 New Mutant KRAS Inhibitors Are Showing Promise in Cancer Clinical Trials: Hope For the Once ‘Undruggable’ Target
Curator: Stephen J. Williams, Ph.D.
Summary to Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases –Voice of Professor Stephen Williams
Part 8: Epigenomics and Genomic Regulation
Introduction to Part 8: Epigenomics and Genomic Regulation – Voice of Professor Williams
See
8.1 Genomic controls on epigenomics
8.1.1 Series A: e-Books on Cardiovascular Diseases, Series A Content Consultant: Justin D Pearlman, MD, PhD, FACC
VOLUME THREE, Etiologies of Cardiovascular Diseases: Epigenetics, Genetics and Genomics
Larry H Bernstein, MD, FCAP, Senior Editor, Author and Curator and Aviva Lev-Ari, PhD, RN, Editor and Curator
http://www.amazon.com/dp/B018PNHJ84
8.2 The ENCODE project and gene regulation
8.2.1 Bioinformatic Tools for Cancer Mutational Analysis: COSMIC and Beyond
Curator: Stephen J. Williams, Ph.D.
8.2.2 ENCODE (Encyclopedia of DNA Elements) Program: ‘Tragic’ Sequestration Impact on NHGRI Programs
Reporter: Aviva Lev-Ari, PhD, RN
8.2.3 Reveals from ENCODE project will invite high synergistic collaborations to discover specific targets
Author and Reporter: Anamika Sarkar, Ph.D
8.2.4 ENCODE: the key to unlocking the secrets of complex genetic diseases
Author: Ritu Saxena, Ph.D.
8.2.5 Impact of evolutionary selection on functional regions: The imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
8.2.6 ENCODE Findings as Consortium
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2012/09/10/encode-findings-as-consortium/
8.3 Small interfering RNAs and gene expression
8.3.1 Moderna Therapeutics Deal with Merck: Are Personalized Vaccines here?
Curator & Reporter: Stephen J. Williams, Ph.D.
8.3.2 IsomicroRNA
Larry H. Bernstein, MD, FCAP, Curator
https://pharmaceuticalintelligence.com/2016/02/18/isomicrorna/
8.3.3 An Intelligent DNA Nanorobot to Fight Cancer by Targeting HER2 Expression
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
8.3.4 Exosomes: Natural Carriers for siRNA Delivery using extracellular vesicles through endocytic pathway.
Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2017/04/24/exosomes-natural-carriers-for-sirna-delivery/
8.3.5 Alnylam Announces First-Ever FDA Approval of an RNAi Therapeutic, ONPATTRO™ (patisiran) for the Treatment of the Polyneuropathy of Hereditary Transthyretin-Mediated Amyloidosis in Adults
Reporter: Aviva Lev-Ari, PhD, RN
8.4 Epigenomics in Cancer
8.4.1 Deciphering the Epigenome
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/01/28/deciphering-the-epigenome/
8.4.2 Methylation and cancer epigenomics
Larry H. Bernstein, MD, FCAP, Curator
https://pharmaceuticalintelligence.com/2016/02/19/methylation-and-cancer-epigenomics/
8.4.3 A New Potential Target for Pancreatic Cancer Treatment: Rapid Screening Technique finds Gene Defending Tumors from DNA Damage @M. D. Anderson Cancer Center
Reporter: Aviva Lev-Ari, PhD, RN
8.4.4 Is the Warburg effect an effect of deregulated space occupancy of methylome?
Larry H. Bernstein and Radoslav Bozov, co-curation
8.5 Environmental Epigenomics
8.5.1 BioInformatic Resources at the Environmental Protection Agency: Tools and Webinars on Toxicity Prediction
Curator Stephen J. Williams Ph.D.
8.5.2 Live 2:30-4:30 PM Mediterranean Diet and Lifestyle: A Symposium on Diet and Human Health: October 19, 2018
Reporter: Stephen J. Williams, Ph.D.
8.5.3 Live 12:00 – 1:00 P.M Mediterranean Diet and Lifestyle: A Symposium on Diet and Human Health : October 19, 2018
Reporter: Stephen J. Williams, Ph.D.
8.5.4 Decline in Sperm Count – Epigenetics, Well-being and the Significance for Population Evolution and Demography
Contributors of Co-Curation
Dr. Marc Feldman, Expert Opinion on the significance of Sperm Count Decline on the Future of Population Evolution and Demography
Dr. Sudipta Saha, Effects of Sperm Quality and Quantity on Human Reproduction
Dr. Aviva Lev-Ari, Psycho-Social Effects of Poverty, Unemployment and Epigenetics on Male Well-being, Physiological Conditions affecting Sperm Quality and Quantity
Summary to Part 8: Epigenomics and Genomic Regulation – Voice of Professor Stephen Williams
See
Volume Summary – The Voice of Aviva Lev-Ari and Stephen Williams
- This is the ONLY Book on the topics in its Title integrated together in one Volume on
Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
- This is the ONLY Book to consist of these eight parts on these topics representing the Scientific Frontier in Biological Sciences and Medicine related to the Genomics aspects of Disease onset based on
- all aspects of life: the Cell, the Organ, the Human Body and Human Populations
- all methodologies of genomic data analysis: Next Generation Sequencing, Gene Editing, AI, Single Cell Genomics, Evolution Biology Genomics, Simulation Modeling in Genomics, Genotypes and Phenotypes Modeling, measurement of Epigenomics effects on disease and pharmacogenomics
The books eight parts are the following:
Part 1: NGS
Part 2: CRISPR for Gene Editing and DNA Repair
Part 3: AI in Medicine
Part 4: Single Cell Genomics
Part 5: Evolution Biology Genomics Modeling @Feldman Lab, Stanford University – Written and Curated by Prof. Marc Feldman
Part 6: Simulation Modeling in Genomics
Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases
Part 8: Epigenomics and Genomic Regulation
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No other book covers these topics in one volume
-
No other book incorporated 74 eProceedings created in real time by the Book’s authors and Editors
-
No other book incorporated four collections of Tweets representing quotes from speakers at the most esteemed conferences on Genomics
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No other book has 13 locations where Videos and Audio Podcast are enriching the e-Reader experience
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No other book has 326 articles on the topics included in the Book’s Title
Epilogue – The Voice of Aviva Lev-Ari and Professor Williams
The Editors decided that the Epilogue to this volume will be four videos made at the
2019 15th Annual Personalized Medicine Conference,
11/13-11/14, 2019, Boston, Harvard Medical School
Thank You for Joining Us
November 13 – 14, 2019
Joseph B. Martin Conference Center
Harvard Medical School
77 Avenue Louis Pasteur
Boston, MA 02115
“If you want to go fast, go alone. If you want to go far, go together.”Steven Shak, M.D.
Co-Founder, Chief Scientific Officer, Genomic Health
Dear Colleague:
Use the link to access videos of the keynote sessions
featuring Scott Gottlieb, M.D., of the American Enterprise
Institute; Carl June, M.D., of the University of Pennsylvania;
Steven Shak, M.D., of Genomic Health; and Paul Stoffels,
M.D., of Johnson & Johnson.
As senior leaders from the GO2 Foundation for Lung Cancer, Harvard Pilgrim Health Care, the Institute for Clinical and Economic Review, M2Gen, and Novartis walked off the stage after the 15th Annual Personalized Medicine Conference at Harvard Medical School‘s final session, which was titled “Toward a Shared Value Proposition in Health Care,” Natasha Loder, Health Policy Editor, The Economist, told me it was “remarkable” to “see all of these people discuss the issues together” as she prepares to write a feature story on personalized medicine.
Her comments capture the spirit of this year’s conference and speak to PMC’s approach to advancing personalized medicine.
In the middle of a divisive debate about health care access and costs, conference participants reminded us that leaders from every sector of the health care ecosystem agree on the need for personalized medicine that targets more effective treatments to only those patients who will benefit from them.
In the context of the complex regulatory, reimbursement, and clinical adoption challenges associated with personalized tests and treatments that leverage insights about the molecular make-up of each patient to guide earlier prevention strategies and longer-lasting interventions, we heard from employers and leaders in the genetic testing industry who are collaborating to implement genetically informed wellness programs without threatening data protections, sacrificing patient privacy, or misleading patients about the significance of their results.
We also heard from payers and providers who are collaborating to facilitate patient access to pharmacogenetic and precision oncology programs that will improve patients’ lives and inform the future of the field.
And we heard from patients, industry executives, and policy experts who are at the table to discuss collaborative drug development and reimbursement solutions that can better balance business and social objectives.
As Steven Shak, M.D., Co-Founder, Chief Scientific Officer, Genomic Health, reminded us after accepting the 15th Annual Leadership in Personalized Medicine Award on the first day of the conference, all of these efforts demonstrate that “if you want to go far, go together.”
It is in this context that I thank you for attending the conference and for your ongoing interest in the Personalized Medicine Coalition’s work.
You can access videos of this year’s keynote sessions using the link provided above.
We look forward to seeing you next year.
Sincerely yours,
Christopher J. Wells
Vice President, Public Affairs
Personalized Medicine Coalition
SOURCE
From: “Christopher Wells (PMC)” <cwells@personalizedmedicinecoalition.org>
Reply-To: “Christopher Wells (PMC)” <cwells@personalizedmedicinecoalition.org>
Date: Thursday, November 21, 2019 at 2:05 PM
To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>
Subject: Thank You for Joining Us
Views of the best leaders in the field of Personalized Medicine and Genomics are presented, below
Co-Founder, Chief Scientific Officer, Genomic Health
SOURCE
https://www.youtube.com/channel/UCVOS-aFZnEd2y6-ZRY0wWqw/videos?view=0&sort=dd&shelf_id=1
Another view in this epilogue represents a customer driven business models, precision health and precision medicine to represent the Biotech landscape in 2025.
AMAZON, Alphabet, Microsoft, Alibaba, Tencent and alike conquering healthcare – Towards customer driven business models, precision health and precision medicine – A customer journey in 2025
Special attention is given below to the role that NGS, ML, AI will play in Amazon Web Services (AWS) applied to Health Care: Scenarios for Amazon.com’s Potential Point of Entry into the Pharma Supply Chain
- Partnership with existing players
- Online pharmacy
- Omni-channel pharmacy (retail+online)
- Integrated PBM/online pharmacy
- Drug distribution to pharmacy
Here are some of the key insights from the report:
- Rather than replacing pharmacies right away, Amazon might start by partnering with a pharmacy benefits manager (PBM), which acts as an intermediary between payers, like health insurers, and the rest of the health system. That would provide “access to patient data and the potential to cross-sell related products.”
- Amazon could ultimately improve price transparency for the consumer and reduce out-of-pocket drug costs. But it would likely start by speeding up the drug delivery process and facilitating at-home delivery.
- Amazon could also become an online pharmacy, retail and online pharmacy, integrated PBM and online pharmacy, or handle drug distribution to pharmacies.
- One potential — and overlooked — challenge for Amazon might be the so-called “age gap.” Amazon’s customers tend to be younger and healthier than people who typically take prescription drugs.
- Amazon could move into digital health by using the Echo in clinical settings and developing tools for telemedicine and remote patient monitoring. “Imagine seeing a virtual doctor on your Amazon app, having it prescribe you a certain medication, and then tapping a ‘buy now’ button — all without leaving your home.”
Other Sources
Microsoft lands another healthcare partnership, this time with Humana to take care of aging seniors
Microsoft, Nuance developing ambient and AI technology to tackle doctors’ documentation headaches
AI and Health Care Are Made for Each Other
Universal blood test from Microsoft and Adaptive is a Google-sized data challenge
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