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Archive for the ‘Genome Biology’ Category


2,000 human brains yield clues to how genes raise risk for mental illnesses

Reporter: Irina Robu, PhD

It’s one thing to detect sites in the genome associated with mental disorders; it’s quite another to discover the biological mechanisms by which these changes in DNA work in the human brain to boost risk. In their first concerted effort to tackle the problem, 15 collaborating research teams of the National Institutes of Health-funded PsychENCODE Consortium evaluated data of 2000 human brains which might yield clues to how genes raise risk for mental illnesses.
Applying newly uncovered secrets of the brain’s molecular architecture, they established an artificial intelligence model that is six times better than preceding ones at predicting risk for mental disorders. They also identified several hundred previously unknown risk genes for mental illnesses and linked many known risk variants to specific genes. In the brain tissue and single cells, the researchers identified patterns of gene expression, marks in gene regulation as well as genetic variants that can be linked to mental illnesses.
Dr. Nenad Sestan of Yale University explained that “ the consortium’s integrative genomic analyses elucidate the mechanisms by which cellular diversity and patterns of gene expression change throughout development and reveal how neuropsychiatric risk genes are concentrated into distinct co-expression modules and cell types”. The implicated variants are typically small-effect genetic variations that fall within regions of the genome that don’t code for proteins, but instead are thought to regulate gene expression and other aspects of gene function.
In addition to the 2000 postmortem human brains, researchers examined brain tissue from prenatal development as well as people with schizophrenia, bipolar disorder,  and typical development compared findings with parallel data from non-human primates.

Their findings indicate that gene variants linked to mental illnesses exert more effects when they jointly form “modules”, communicating genes with related functions and at specific developmental time points that seem to coincide with the course of illness. Variability in risk gene expression and cell types increases during formative stages in early prenatal development and again during the teen years. However, in postmortem brains of people with a mental illness, thousands of RNAs were found to have anomalies.

According to NIMH, Geetha Senthil the multi-omic data resource caused by the PsychENCODE collaboration will pave a path for building molecular models of disease and developmental processes and may offer a platform for target identification for pharmaceutical research.

Source
https://www.nih.gov/news-events/news-releases/2000-human-brains-yield-clues-how-genes-raise-risk-mental-illnesses

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The Future of Precision Cancer Medicine, Inaugural Symposium, MIT Center for Precision Cancer Medicine, December 13, 2018, 8AM-6PM, 50 Memorial Drive, Cambridge, MA

Reporter: Aviva Lev-Ari, PhD, RN

#CPCM2018 @AVIVA1950 @pharma_BI

 

 

Aviva Lev-Ari, PhD, RN, Editor-in-Chief, will attend and cover this event in REAL TIME for

 http://pharmaceuticalintelligence.com 


Over the past decade, there have been major advancements in the field of precision medicine, leading to exciting new treatments for some cancer patients. Much attention has been focused on genomic profiling of tumors to identify genomic alterations that might guide selection of specific therapies for individual patients. Beyond genomics, however, there is a variety of other precision approaches that can identify and exploit cancer-specific biological mechanisms including proteomics, metabolomics, and computational modeling, resulting in the more effective use of existing cancer medicines. On Thursday, December 13, 2018, the MIT Center for Precision Cancer Medicine will hold its inaugural annual symposium in the Samberg Conference Center at MIT. This full-day event will feature leading researchers and clinicians, who will highlight recent advances in precision cancer medicine and share perspectives on the future. An industry panel will also discuss the barriers to instituting precision medicine into current and future clinical trials.

 


Keynote Address

Charles Sawyers

Charles Sawyers, MD

Chair, Human Oncology and Pathogenesis Program
Memorial Sloan Kettering Cancer Center

Speakers

Andrea Califano

Andrea Califano, PhD

Clyde and Helen Wu Professor of Chemical Systems Biology, Columbia University
Chair, Department of Systems Biology, Columbia University
Director, JP Sulzberger Columbia Genome Center
Associate Director, Herbert Irving Comprehensive Cancer Center

Chris Love

J. Christopher Love, PhD

Professor of Chemical Engineering, MIT
Associate Member, Ragon Institute of MGH, MIT and Harvard
Member, Koch Institute, MIT

Richard Marais

Richard Marais, PhD

Professor of Molecular Oncology
Director, CRUK Manchester Institute
The University of Manchester

Kenna Mills Shaw

Kenna Mills Shaw, PhD

Executive Director
Sheikh Khalifa Bin Zayed al Nahyan Institute for Personalized Cancer Therapy
MD Anderson Cancer Center

Alice Shaw

Alice Shaw, MD, PhD

Professor, Harvard Medical School
Director, Thoracic Cancer Program, Massachusetts General Hospital

Matt Vander Heiden

Matthew Vander Heiden, MD, PhD

Associate Professor of Biology, MIT
Associate Director, Koch Institute
Member, MIT Center for Precision Cancer Medicine

Mike Yaffe

Michael B. Yaffe, MD, PhD

David H. Koch Professor of Science, MIT
Professor of Biology and Biological Engineering, MIT
Director, MIT Center for Precision Cancer Medicine
Director, Koch Institute Clinical Investigator Program

Jean Zhao

Jean Zhao, PhD

Professor of Biological Chemistry and Molecular Pharmacology
Harvard Medical School and Dana-Farber Cancer Institute


Panelists: Barriers to Instituting Precision Medicine in Clinical Trials

Hammerman

Peter Hammerman, MD, PhD

Global Head, Translational Research
Oncology Disease Area
Novartis Institutes for BioMedical Research

Ho

Steffan N. Ho, MD, PhD

Vice President, Head of Translational Oncology
Pfizer Global Product Development

Shiva Malek

Shiva Malek, PhD

Director and Principal Scientist
Department of Discovery Oncology
Genentech Inc

Marks

Kevin Marks, PhD

VP of Biology
Agios Pharmaceuticals

Michael Rothenberg

S. Michael Rothenberg, MD, PhD

Vice-President, Research and Development
Loxo Oncology, Inc.

Angela Koehler

Moderator:

Angela Koehler, PhD

Goldblith Career Development Professor in Applied Biology, MIT
Member, Koch Institute for Integrative Cancer Research
Member, MIT Center for Precision Cancer Medicine

 

Speakers:

Panelists:

  • Peter Hammerman, Novartis Institutes for BioMedical Research
  • Steffan Ho, Pfizer
  • Shiva Malek, Genentech, Inc
  • Kevin Marks, Agios Pharmaceuticals
  • S. Michael Rothenberg, Loxo Oncology, Inc

Moderated by Angela Koehler, MIT’s Koch Institute

Agenda:

8:00 am Registration and continental breakfast

8:45 am Opening remarks by Michael Yaffe (MIT’s Koch Institute)

  • Season of great expectation, tumor genetics is just the beginning, beyond: science, engineering, medicine: beyond genomics: immunology, cell biology, early detection, new drug development for the undrugable, system biology, RNAi
  • Jack Tyler was the initiator to find a donor for CPCM

9:00 am Keynote Address by Charles L. Sawyers (Memorial Sloan Kettering Cancer Center)

  • developed a drug for prostate cancer
  • Clinical trained oncologist/genomics
  • Lineage Plasticity:
  1. luminal cells in histology of origin and basal cells and require androgen receptor AR) function
  2. deprive lunimal cells fro growth factor
  3. Hormonal therapy Leuprolite, degarelix [castration methastatic]
  4. after relapse 2nd generation anti-androgens abirateron
  5. PING MU ENZALUTAMIDE RESISTANCE P53/RB! DELETION CONFER
  6. TRANSCRIPTION CHANGE: ANTIADROGEN RESISTANCE
  7. Lineage shift Sox2 level goes up – prevent drug resistance, in vivo and in vitro
  8. SOX2 promotes lineage placticity and antiadrogen resistance in TP53 and RBI-deficient prostate cancer
  9. Evolution of Lineage plasticity over time
  10. AR Pathway inhibition accelerates lineage plasticity: synaptophysin-positive disease in-vivo
  11. scRNA-seq time course – modeled by diffusion map displayed in luminal and basal cells
  12. Emergence of EMT phenotype, with retention of epithelial features
  13. Use CRISPR to perturb luminal plasticity by phyeno type
  14. Genomic landscape of Primary Prostate Cancer: ERG gain drives luminal layer
  15. Different classes of FOXA1 mutations in Prostate organoid Cancer – Missense, inframe, truncated
  16. FOXA1 key in hormone receptor signaling
  17. Hypermorphic peaks – ATAC-seq neomorphic FOXA1 pioneering activity
  18. Common Prostate Cancer Genes:differentiation phenotypes: TP53 Loss, RB1 – Loss,
  19. work of Matan Hofree – four subtypes of luminal cells
  20. involution and regeneration of single cell RNAseq
  21. Transcriptional shifts in response to castration/androgen addback
  22. androgen addback: 50% of luminal cells are proliferation in 48 hours
  23. cell responsible for organ regeneration

 

9:45 am Alice T. Shaw (Massachusetts General Hospital)

  • evolution of drug resistance in Lung Cancer
  • oncogenic drivers in lung adenocarcenoma –
  1. EGFR – sensitizing 19.4% of all patients
  2. KRAS
  3. ALK
  4. ROS1
  5. CMET
  6. BRAF
  7. NTRK1
  8. RET

Delay and prevention of drug resistance: liquid biopsy of pleural fluids and serial blood collections

  • Crizotinib patient with ROS1 + nsclc
  • acquired mutation in ROS1 G2032R – resistance to Crizotinib – Michael Lawrence, MGH – analysis of mutation and resistance
  • Repotrectinib – for ROS1 – Resistance mediated by this mutation
  • If patient fails three antiinhibitor drugs: secondary ALK mutations mediate Crizotinib Resistance
  • 2nd generation of  ALK inhibitors are structurally Distinct molecules
  • Lorlatinib – 3rd generation –>> back to 1st generation Crizotinib
  • Clonal evolution of resistance in ALK in NSCLC
  • compound mutations in ALK mutations – Lorlatinib Resistance
  • Sequential TKI therapy foster the development of compound mutation refractory to all generations og ALK TKIs – compound mutation can’t be overcome
  • Intratumoral Heterogeneity revealed by multiregion sequencing of renal cell carcinoma and resected NSCLC
  • somatic mutations: Pre-treatment to Lorlatinib resistance
  • Clonal Analysis: Multiple Drivers of resistance underlie clinical relapse
  • genomic instability – eradicate residual disease to eliminate drug resistance and tolerance persistance

 

10:25 am Networking Break

10:45 am Richard Marais (Cancer Research UK, Manchester Institute)

  • Melanoma – Precision Medicin
  • Request – NOT TO PUBLISH on the INTERNET, some of the work presented is not PUBLISHED.
  • Request is honored

11:25 am Matthew Vander Heiden (MIT’s Koch Institute)

  • Targeting Metabolism is altered in cancer
  • Metabolism is glucose carbohydrates, lipids – conversion of nutrients into biomass: ATP, Protein, Nucleic acid,
  • Not -proliferating cells vs proliferating cells
  • genetic mutations, tissue of origin, lineage of cells — metabolism takes place: combination of these three facto
  • environment consists the metabolic network definers.d by cell intrinsic network
  • Assessment of nutrient levels in tumor microenvironment
  • Metabolite analysis: ion suppression vs nutrients
  • nutrients are available to cells in tumors
  • depletion of glucose vs enrichment
  • metabolite most different: Gluthamine, needed for cancer to grow
  • Lineage can contribute – tryptophane and argenine
  • gluthamine – Cyctine affect gluthamine sensitivity to gluthamine inhibitors
  • what you eat, where is the tumor locate, tissue environment — more important
  • therapeutic window: metabolism processes – cell proliferation
  • ability to make aspartate – given to mice pancreatic  — tumor grow faster
  • cellular oxidation state correlate with pyruvate oxidation — PDH Activator suppress oxidation
  • Aspartate vs NAD+/NADH – lactate TCA – form more carbon
  • PDH activation reduces Redux
  • Serine availability can limit proliferation even in cells with increase
  • Serine vs NAD regeneration
  • which cancer falls into which group : Serine pathway – increase serine synthesis: Melanoma vs Breast cancer
  • growth of breast cancer: Serine availability dependent – accelerate of inhibit growth by level of serine
  • Model for how nutrient limitation affect tumor growth, tumor size depends of serine levels

 

12:05 pm Box lunch

12:30 pm Industry panel: Barriers to instituting precision medicine into clinical trials

  • Long term benefits of Precision Medicine
  • What phynotype are now looked for?

Michael Rothenberg

  1. short term, identify mutations
  2. more testing is needed
  3. sequencing the therapies
  4. challenge getting tissue, doing experiments in house
  5. Industry needs Academia collaboration for accelerated innovations
  6. AI may lower the cost of drug discovery

KEVIN MARX:

  1. MECHANISM OF RESISTANCE – COMBINATORIAL DRUG DISCOVERY
  2. phynotyping, tissue acquisition immune phenotype, what drive therapeutic response?
  3. genetic drivers
  4. HR seeks Scientistist that worked in TEAMS, collaborative science

STEPHAN HO

  1. long term benefits are very important
  2. Stage III disease – technology advances
  3. advanced in the regulatory space
  4. smaller cohort size to approve a drug
  5. biologic complexity, driver oncogenes, precision to imprecision
  6. cost of risk in investment in innovations
  7. check point inhibitor – known biology and immuno-modulation, data hypothesis and moving forward
  8. Organizational culture, interaction in teams, functional behavior
  9. commit to deliverable, perfect timing contingent on work of others.

Peter Hammerman

  1. single cell tumor immunity in combination drug therapy
  2. Tumor monitoring over time
  3. Novartis is interested to collaborate with innovators in Academia and in other institutions
  4. critical thinking on DATA and on negative data
  5. Combination drug therapy: orthogonal mechanism of actions and drug classed – toxicity is an issue

Shiva Malek

  1. How to drug mutations on DATA
  2. Acquired and intrinsic mutations
  3. exposure and patient safety
  4. UCSF’s Ashkenazi’s Team and Genetech – basic biology area selection
  5. Failure are not talked about
  6. Round table for problem solvers, how you approach a problem
  7. translational work require skills beyond technical expertise
  8. learning the navigation inside an organization
  9. leadership in R&D, expected to demonstrate leadership, the Scientist needs to have command of the field and of desirable directions of research

 

2:00 pm J. Christopher Love (MIT’s Koch Institute)

Acceleration of the PROCESS to develop Precision Medicine products

  • design, build, test – PROCESS
  • New drugs and vaccines – the process is iterative
  • measurements, with use of smallest number of samples
  • deliver precision medical: small f patients or large population or
  • clinical samples provide rich source of information: Blood or tissue sample
  • Tissue – extract RNA, component cells, single-cell RNA sequencing,
  • Challenges of enabling scRNA-seq in clinical labs
  • Probability, scale, capture efficiencies, temporal uniformity
  • single-cell sequencing
  • Seq-Well: method for scRNA-Seq
  • New Chemistries for T-cell
  • Blood: cell, cfDNA, Exosomes
  • map cancer genome from blood
  • Tissue:
  • Single circulating Tumor cells:
  • yield genomic landscape of cancer
  • cell free DNA, vells, proteins, metabolite, Tumor is existence, draw blood
  • cfDNA Tumor Fraction is prognostic of survival in mTNBC
  • automate to 13 cancer types
  • Rs is now possible
  • reduce sample requirement
  • cost is low digital information from clinical samples
  • Keytruda – is a molecular Signature
  • low volume product, advanced preparation (mo-years) __>>> agile solutions (days to years)
  • bentchtop, on-demand manufacturing system: Production, Purification, Formulation
  • hand-free production of formulated G-CSF: comparable to licensed products.
  • Plug and play manufacturing using  InSeq
  • Novel MAbs from patients
  • Many molecules to many products

 

2:40 pm Andrea Califano (Columbia University, System Biology)

Mechanistic Framework for the systematic pharmacological targeting of Non-Oncogene Dependencies – Precise Precision Oncology

  • systematic elucidation od critical cancer cell dependencies
  • drug MOA
  • Tumor dependencies to Drug MOA
  • Tumor heterogeneity
  • ARACNe – regulatory targets of regulatory proteins
  • Combinational Therapy: HER@ inhibitor and JAK1/JAK2 inhibitor
  • Driver Mutations
  • ARACNe; MINDy DIGGIT; Expression VIPER: MetaVIPER
  • Aberrantly activated protein for Prioritizing treatment in patients
  • Checkpoint activity reversal – prioritize drugs based on
  • Tumor model selection: GIST
  • 260 patients, 14 untreatable cancers — N of 1 Study
  • Single cell Studies – active proteins in stem-like progenitor cells
  • Ivermectin Treatment vs Control (7d vs 14d)

 

3:20 pm Networking Break

3:40 pm Jean Zhao (Dana Farber Cancer Institute)

Immunotherapy and Targeted Therapy in Cancer Therapy

  • Targeting cancer with CDK4/6 inhibitors
  • CDK4/6 inhibitors causes tumor regression in breast cancer and regression of CT-26 colorectal cancer
  • CDK4/6DNMT1 inducing viral mimicry
  • PARP inhibitors  changing treatment in ovarian cancer
  • FDA approved three drugs for ovarian cancer
  • p53-null; BRCA-null; myc high – model testing

 

4:20 pm Kenna Mills Shaw (MD Anderson Cancer Center)

  • PM nor a Silver bullet nor a Dream Illusion
  • 2013: not all mutations are equally actionable
  • Context of Biomarkers
  • co-mutations in lung cancer identity – therapeutic vulnerability
  • NGS cost decrease leads to increases in Data generation
  • there are only 125 genes ACTIONABLE IN THE CLINIC
  • finding biomarkers beyond direct targets
  • clinical actionability:80K mutation – 32%
  • patients: No standard treatment available
  • Enrollment inGenotype Matched TRIALS
  • MUTATIONS SCREENED: LACK OF ENROLLMENT NOT DUE TO LACK OF MATCHING PROCESS
  • 69% GOT NEW REGIMEN, 17% did not come back — no one called them
  • 58% enrolled on genotrype-matched trials
  • Beyond NGS:

www.personalizedcancertherapy.org

  • DECISION SUPPORT IN REAL TIME IMPROVES “MATCHING” TO RIGHT DRUG.
  • MULTIFACTORS: CO-MOEBIDITIES, MICROBIOME, IMMUNE PHYNOTYPING, GENOMICS, MICROBIOME, ZIP CODE, INFECTION

5:00 pm Michael Yaffe (MIT)

  • AUGMENTED SYNTHETIC LETHALITY
  • CANCER CELLS ARE UNDER CONSTANT STRESS
  • inflammation
  • Therpeutics-targeted Synthetic Lethality
  • BRCA mutation seen in 10%-20% of patients
  • p53 mutations DNA demage – leads to apoptosis p38 MK2 as a pathway is taking over repair DNA and no apotosis occurs.
  • doxorubicin
  • Nanoparticle targeting of siRNAs to established tumors
  • The Concept of augmented Synthetic Lethality   —- enhance a prevosly known synthetic interaction by targeting additional pathways
  • combination of repair pathway  and checkpoint activation – lead to better therapeutic results
  • MK2 – targets hnRNP A0 (an RNA binding protein)  – Cleaved Caspase 3 – is synthetic lethal with p53 mutuant tumors, not just p53 null alleles
  • MK2 links Inflammation and Cancer – IBD –>> polyps and Colon Cancer
  • myeloid cell recruitment to inflammatory tumors in
  • MK2 KO mice: IL-4 –M2 magrophage – tumor progression; regulate the tumor microenvironment
  • IFNgamma –>M1 macrophages – tumor suppression

 

 

 

SOURCE

https://ki.mit.edu/news/events/cpcmsymposium-2018

https://www.eventbrite.com/e/mit-center-for-precision-cancer-medicine-inaugural-symposium-tickets-50424019600?utm_campaign=event_reminder&utm_medium=email&utm_source=eb_email&utm_term=eventname

https://www.eventbrite.com/e/mit-center-for-precision-cancer-medicine-inaugural-symposium-tickets-50424019600

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Individuals without angiographic CAD but with hiPRS remain at significantly elevated risk of mortality after cardiac catheterization

Reporter: Aviva Lev-Ari, PhD, RN

 

A genome-wide Polygenic risk scores (PRS) improves risk stratification when added to traditional risk factors and coronary angiography. Individuals without angiographic CAD but with hiPRS remain at significantly elevated risk of mortality.

 

Background:

Coronary artery disease (CAD) is influenced by genetic variation and traditional risk factors. Polygenic risk scores (PRS), which can be ascertained before the development of traditional risk factors, have been shown to identify individuals at elevated risk of CAD. Here, we demonstrate that a genome-wide PRS for CAD predicts all-cause mortality after accounting for not only traditional cardiovascular risk factors but also angiographic CAD itself.

Methods:

Individuals who underwent coronary angiography and were enrolled in an institutional biobank were included; those with prior myocardial infarction or heart transplant were excluded. Using a pruning-and-thresholding approach, a genome-wide PRS comprised of 139 239 variants was calculated for 1503 participants who underwent coronary angiography and genotyping. Individuals were categorized into high PRS (hiPRS) and low-PRS control groups using the maximally selected rank statistic. Stratified analysis based on angiographic findings was also performed. The primary outcome was all-cause mortality following the index coronary angiogram.

Results:

Individuals with hiPRS were younger than controls (66 years versus 69 years; P=2.1×10-5) but did not differ by sex, body mass index, or traditional risk-factor profiles. Individuals with hiPRS were at significantly increased risk of all-cause mortality after cardiac catheterization, adjusting for traditional risk factors and angiographic extent of CAD (hazard ratio, 1.6; 95% CI, 1.2–2.2; P=0.004). The strongest increase in risk of all-cause mortality conferred by hiPRS was seen among individuals without angiographic CAD (hazard ratio, 2.4; 95% CI, 1.1–5.5; P=0.04). In the overall cohort, adding hiPRS to traditional risk assessment improved prediction of 5-year all-cause mortality (area under the receiver-operating curve 0.70; 95% CI, 0.66–0.75 versus 0.66; 95% CI, 0.61–0.70; P=0.001).

Conclusions:

A genome-wide PRS improves risk stratification when added to traditional risk factors and coronary angiography. Individuals without angiographic CAD but with hiPRS remain at significantly elevated risk of mortality.

Footnotes

https://www.ahajournals.org/journal/circgen

*A list of all Regeneron Genetics Center members is given in the Data Supplement.

Guest Editor for this article was Christopher Semsarian, MBBS, PhD, MPH.

The Data Supplement is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCGEN.118.002352.

Scott M. Damrauer, MD, Department of Surgery, Hospital of the University of Pennsylvania, 3400 Spruce St, Silverstein 4, Philadelphia, PA 19104. Email 
SOURCE

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The HFE H63D variant confers an increased risk for hypertension, no increased risk for adverse cardiovascular events or substantial left ventricular remodeling

Reporter: Aviva Lev-Ari, PhD, RN

Conclusion:

The HFE H63D variant confers an increased risk for hypertension per allele and, given its frequency, accounts for a significant number of cases of hypertension. However, there was no increased risk for adverse cardiovascular events or substantial left ventricular remodeling.

 

HFE H63D Polymorphism and the Risk for Systemic Hypertension, Myocardial Remodeling, and Adverse Cardiovascular Events in the ARIC Study

Originally publishedHypertension. 2018;0:HYPERTENSIONAHA.118.11730

H63D has been identified as a novel locus associated with the development of hypertension. The quantitative risks for hypertension, cardiac remodeling, and adverse events are not well studied. We analyzed white participants from the ARIC study (Atherosclerosis Risk in Communities) with H63D genotyping (N=10 902). We related genotype status to prevalence of hypertension at each of 5 study visits and risk for adverse cardiovascular events. Among visit 5 participants (N=4507), we related genotype status to echocardiographic features. Frequencies of wild type (WT)/WT, H63D/WT, and H63D/H63D were 73%, 24.6%, and 2.4%. The average age at baseline was 54.9±5.7 years and 47% were men. Participants carrying the H63D variant had higher systolic blood pressure (P=0.004), diastolic blood pressure (0.012), and more frequently had hypertension (P<0.001). Compared with WT/WT, H63D/WT and H63D/H63D participants had a 2% to 4% and 4% to 7% absolute increase in hypertension risk at each visit, respectively. The population attributable risk of H63D for hypertension among individuals aged 45 to 64 was 3.2% (95% CI, 1.3–5.1%) and 1.3% (95% CI, 0.0–2.4%) among individuals >65 years. After 25 years of follow-up, there was no relationship between genotype status and any outcome (P>0.05). H63D/WT and H63D/H63D genotypes were associated with small differences in cardiac remodeling. In conclusion, the HFE H63D variant confers an increased risk for hypertension per allele and, given its frequency, accounts for a significant number of cases of hypertension. However, there was no increased risk for adverse cardiovascular events or substantial left ventricular remodeling.

Footnotes

The online-only Data Supplement is available with this article at https://www.ahajournals.org/doi/suppl/10.1161/HYPERTENSIONAHA.118.11730.

Correspondence to Scott D. Solomon, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115. Email 

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Bioinformatics Tool Review: Genome Variant Analysis Tools

Curator: Stephen J. Williams, Ph.D.

Updated 11/15/2018

The following post will be an ongoing curation of reviews of gene variant bioinformatic software.

 

The Ensembl Variant Effect Predictor.

McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F.

Genome Biol. 2016 Jun 6;17(1):122. doi: 10.1186/s13059-016-0974-4.

Author information

1

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. wm2@ebi.ac.uk.

2

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

3

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. fiona@ebi.ac.uk.

Abstract

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

 

Rare diseases can be difficult to diagnose due to low incidence and incomplete penetrance of implicated alleles however variant analysis of whole genome sequencing can identify underlying genetic events responsible for the disease (Nature, 2015).  However, a large cohort is required for many WGS association studies in order to produce enough statistical power for interpretation (see post and here).  To this effect major sequencing projects have been initiated worldwide including:

A more thorough curation of sequencing projects can be seen in the following post:

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

 

And although sequencing costs have dramatically been reduced over the years, the costs to determine the functional consequences of such variants remains high, as thorough basic research studies must be conducted to validate the interpretation of variant data with respect to the underlying disease, as only a small fraction of variants from a genome sequencing project will encode for a functional protein.  Correct annotation of sequences and variants, identification of correct corresponding reference genes or transcripts in GENCODE or RefSeq respectively offer compelling challenges to the proper identification of sequenced variants as potential functional variants.

To this effect, the authors developed the Ensembl Variant Effect Predictor (VEP), which is a software suite that performs annotations and analysis of most types of genomic variation in coding and non-coding regions of the genome.

Summary of Features

  • Annotation: VEP can annotate two broad categories of genomic variants
    • Sequence variants with specific and defined changes: indels, base substitutions, SNVs, tandem repeats
    • Larger structural variants > 50 nucleotides
  • Species and assembly/genomic database support: VEP can analyze data from any species with assembled genome sequence and annotated gene set. VEP supports chromosome assemblies such as the latest GRCh38, FASTA, as well as transcripts from RefSeq as well as user-derived sequences
  • Transcript Annotation: VEP includes a wide variety of gene and transcript related information including NCBI Gene ID, Gene Symbol, Transcript ID, NCBI RefSeq ID, exon/intron information, and cross reference to other databases such as UniProt
  • Protein Annotation: Protein-related fields include Protein ID, RefSeq ID, SwissProt, UniParc ID, reference codons and amino acids, SIFT pathogenicity score, protein domains
  • Noncoding Annotation: VEP reports variants in noncoding regions including genomic regulatory regions, intronic regions, transcription binding motifs. Data from ENCODE, BLUEPRINT, and NIH Epigenetics RoadMap are used for primary annotation.  Plugins to the Perl coding are also available to link other databases which annotate noncoding sequence features.
  • Frequency, phenotype, and citation annotation: VEP searches Ensembl databases containing a large amount of germline variant information and checks variants against the dbSNP single nucleotide polymorphism database. VEP integrates with mutational databases such as COSMIC, the Human Gene Mutation Database, and structural and copy number variants from Database of Genomic Variants.  Allele Frequencies are reported from 1000 Genomes and NHLBI and integrates with PubMed for literature annotation.  Phenotype information is from OMIM, Orphanet, GWAS and clinical information of variants from ClinVar.
  • Flexible Input and Output Formats: VEP supports input data format called “variant call format” or VCP, a standard in next-gen sequencing. VEP has the ability to process variant identifiers from other database formats.  Output formats are tab deliminated and give the user choices in presentation of results (HTML or text based)
  • Choice of user interface
    • Online tool (VEP Web): simple point and click; incorporates Instant VEP Functionality and copy and paste features. Results can be stored online in cloud storage on Ensembl.
    • VEP script: VEP is available as a downloadable PERL script (see below for link) and can process large amounts of data rapidly. This interface is powerfully flexible with the ability to integrate multiple plugins available from Ensembl and GitHub.  The ability to alter the PERL code and add plugins and code functions allows the flexibility to modify any feature of VEP.
    • VEP REST API: provides robust computational access to any programming language and returns basic variant annotation. Can make use of external plugins.

 

 

Watch Video on VES Instructional Webinar: https://youtu.be/7Fs7MHfXjWk

Watch Video on VES Web Version training on How to Analyze Your Sequence in VEP

 

 

Availability of data and materials

The dataset supporting the conclusions of this article is available from Illumina’s Platinum Genomes [93] and using the Ensembl release 75 gene set. Pre-built data sets are available for all Ensembl and Ensembl Genomes species [94]. They can also be downloaded automatically during set up whilst installing the VEP.

 

References

Large-scale discovery of novel genetic causes of developmental disorders.

Deciphering Developmental Disorders Study.

Nature2015 Mar 12;519(7542):223-8. doi: 10.1038/nature14135. PMID:25533962

Updated 11/15/2018

 

Research Points to Caution in Use of Variant Effect Prediction Bioinformatic Tools

Although we have the ability to use high throughput sequencing to identify allelic variants occurring in rare disease, correlation of these variants with the underlying disease is often difficult due to a few concerns:

  • For rare sporadic diseases, classical gene/variant association studies have proven difficult to perform (Meyts et al. 2016)
  • As Whole Exome Sequencing (WES) returns a considerable number of variants, how to differentiate the normal allelic variation found in the human population from disease-causing pathogenic alleles
  • For rare diseases, pathogenic allele frequencies are generally low

Therefore, for these rare pathogenic alleles, the use of bioinformatics tools in order to predict the resulting changes in gene function may provide insight into disease etiology when validation of these allelic changes might be experimentally difficult.

In a 2017 Genes & Immunity paper, Line Lykke Andersen and Rune Hartmann tested the reliability of various bioinformatic software to predict the functional consequence of variants of six different genes involved in interferon induction and sixteen allelic variants of the IFNLR1 gene.  These variants were found in cohorts of patients presenting with herpes simplex encephalitis (HSE). Most of the adult population is seropositive for Herpes Simplex Virus (HSV) however a minor fraction (1 in 250,000 individuals per year) of HSV infected individuals will develop HSE (Hjalmarsson et al., 2007).  It has been suggested that HSE occurs in individuals with rare primary immunodeficiencies caused by gene defects affecting innate immunity through reduced production of interferons (IFN) (Zhang et al., Lim et al.).

 

References

Meyts I, Bosch B, Bolze A, Boisson B, Itan Y, Belkadi A, et al. Exome and genome sequencing for inborn errors of immunity. J Allergy Clin Immunol. 2016;138:957–69.

Hjalmarsson A, Blomqvist P, Skoldenberg B. Herpes simplex encephalitis in Sweden, 1990-2001: incidence, morbidity, and mortality. Clin Infect Dis. 2007;45:875–80.

Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, et al. TLR3 deficiency in patients with herpes simplex encephalitis. Science. 2007;317:1522–7.

Lim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83:1888–97.

 

Genes Immun. 2017 Dec 4. doi: 10.1038/s41435-017-0002-z.

Frequently used bioinformatics tools overestimate the damaging effect of allelic variants.

Andersen LL1Terczyńska-Dyla E1Mørk N2Scavenius C1Enghild JJ1Höning K3Hornung V3,4Christiansen M5,6Mogensen TH2,6Hartmann R7.

 

Abstract

We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.

Methodology

  1. Identification of rare variants
  2. Genomes of nineteen Dutch patients with a history of HSE sequenced by WES and identification of novel HSE causing variants determined by filtering the single nucleotide polymorphisms (SNPs) that had a frequency below 1% in the NHBLI Exome Sequencing Project Exome Variant Server and the 1000 Genomes Project and were present within 204 genes involved in the immune response to HSV.
  3. Identified variants (204) manually evaluated for involvement of IFN induction based on IDBase and KEGG pathway database analysis.
  4. In-silico predictions: Variants classified by the in silico variant pathogenicity prediction programs: SIFT, Mutation Assessor, FATHMM, PROVEAN, SNAP2, PolyPhen2, PhD-SNP, SNP&GO, FATHMM-MKL, MutationTaster2, PredictSNP, Condel, MetaSNP, and CADD. Each program returned prediction scores measuring likelihood of a variant either being ‘deleterious’ or ‘neutral’. Prediction accuracy measured as

ACC = (true positive+true negative)/(true positive+true negative+false positive+false negative)

 

  1. Validation of prediction software/tools

In order to validate the predictive value of the software, HEK293T cells, deficient in IRF3, MAVS, and IKKe/TBK1, were cotransfected with the nine variants of the aforementioned genes and a luciferase reporter under control of the IFN-b promoter and luciferase activity measured as an indicator of IFN signaling function.  Western blot was performed to confirm the expression of the constructs.

 

Results

Table 2 Summary of the
bioinformatic predictions
HSE variants IFNLR1 variants Overall ACC
TN TP FN FP Total ACC TN TP FN FP Total ACC
Uniform cutoff
SIFT 4 1 0 4 9 0.56 8 1 0 7 16 0.56 0.56
Mutation assessor 6 1 0 2 9 0.78 9 1 0 6 16 0.63 0.68
FATHMM 7 1 0 1 9 0.89 0.89
PROVEAN 8 1 0 0 9 1.00 11 1 0 4 16 0.75 0.84
SNAP2 5 1 0 3 9 0.67 8 0 1 7 16 0.50 0.56
PolyPhen2 6 1 0 2 9 0.78 12 1 0 3 16 0.81 0.80
PhD-SNP 7 1 0 1 9 0.89 11 1 0 4 16 0.75 0.80
SNPs&GO 8 1 0 0 9 1.00 14 1 0 1 16 0.94 0.96
FATHMM MKL 4 1 0 4 9 0.56 13 0 1 2 16 0.81 0.72
MutationTaster2 4 0 1 4 9 0.44 14 0 1 1 16 0.88 0.72
PredictSNP 6 1 0 2 9 0.78 11 1 0 4 16 0.75 0.76
Condel 6 1 0 2 9 0.78 0.78
Meta-SNP 8 1 0 0 9 1.00 11 1 0 4 16 0.75 0.84
CADD 2 1 0 6 9 0.33 8 0 1 7 16 0.50 0.44
MSC 95% cutoff
SIFT 5 1 0 3 9 0.67 8 1 0 8 16 0.50 0.56
PolyPhen2 6 1 0 2 9 0.78 13 1 0 3 16 0.81 0.80
CADD 4 1 0 4 9 0.56 7 0 1 9 16 0.44 0.48

 

Note: TN: true negative, TP: true positive, FN: false negative, FP: false positive, ACC: accuracy

Functional testing (data obtained from reporter construct experiments) were considered as the correct outcome.

Three prediction tools (PROVEAN, SNP&GO, and MetaSNP correctly predicted the effect of all nine variants tested.

 

Other articles related to Genomics and Bioinformatics on this online Open Access Journal Include:

Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

 

Large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes

 

US Personalized Cancer Genome Sequencing Market Outlook 2018 –

 

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

 

 

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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

UPDATED on 9/13/2018

 

September 13, 2018

NIH launches initiative to accelerate genetic therapies to cure sickle cell disease

“Our scientific investments have brought us to a point where we have many tools available to correct or compensate for the defective gene that causes sickle cell disease. We are now ready to use these tools to speed up our quest for a cure,” said Gary H. Gibbons, M.D., director of NIH’s National Heart, Lung, and Blood Institute (NHLBI), which is leading the effort.

SOURCE

https://www.nih.gov/news-events/news-releases/nih-launches-initiative-accelerate-genetic-therapies-cure-sickle-cell-disease

 

 

Vertex licensed CTX001, an autologous gene-edited hematopoietic stem cell therapy, from CRISPR in December. It was the first CRISPR-based treatment to come out of a four-year, $105 million deal the pair struck in 2015. At the time, Vertex paid up $75 million in cash and took a $30 million stake in CRISPR Therapeutics in exchange for the right to license up to six gene-editing programs. CTX001 is being developed for the blood disorders sickle cell disease and beta thalassemia.

Both disorders are caused by mutations in the beta-globin gene, which codes for a part of hemoglobin, the oxygen-carrying component of red blood cells. This results in missing or defective hemoglobin. CTX001 was developed on the knowledge that fetal hemoglobin—found in newborn babies but later replaced by adult hemoglobin—can be protective in adults who have blood disorders.

CTX001 uses CRISPR gene-editing ex vivo—that is, outside the body. A patient’s cells are harvested and edited to increase fetal hemoglobin levels in the patient’s blood cells. The edited cells are then infused back into the patient where they are expected to produce blood cells with fetal hemoglobin and compensate for defective adult hemoglobin.

SOURCE

https://www.fiercebiotech.com/biotech/crispr-therapeutics-vertex-start-first-company-backed-human-crispr-trial?mkt_tok=eyJpIjoiTm1FMllXTmtOMlkwWkRNdyIsInQiOiJLMUEyeGtsT0ZMTVBuM1RtbVFjRFdMQUdRcDZkXC9yVHlXTWxIQmlvc3M0XC9LVFArdlFuaVVYY0lQXC81ak9cL3h1VjFHYnprZ3dqVlNlaWFldWxcLzA3QUphdExpc0w0Vk1TSGR3WVl0YXNqQlFRVHdvZmNycVNEWE9qdWQ2QmdacklSIn0%3D&mrkid=993697

Other 339 articles on GENE EDITING were published in this Open Access Online Scientific Journal, including the following articles:

https://pharmaceuticalintelligence.com/?s=Gene+Editing

On CRISPR/Cas9, there are 141 articles in the Journal:

https://pharmaceuticalintelligence.com/?s=CRISPR%2FCas9

Gene Therapy, there are 11 articles in the Journal:

https://pharmaceuticalintelligence.com/category/genome-biology/gene-therapy-gene-editing-development/

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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

 

Dana Pe’er, PhD, now chair of computational and systems biology at the Sloan Kettering Institute at the Memorial Sloan Kettering Cancer Center and a member of the Human Cell Atlas Organizing Committee,

what really sets Regev apart is the elegance of her work. Regev, says Pe’er, “has a rare, innate ability of seeing complex biology and simplifying it and formalizing it into beautiful, abstract, describable principles.”

Dr. Aviv Regev, an MIT biology professor who is also chair of the faculty of the Broad and director of its Klarman Cell Observatory and Cell Circuits Program, was reviewing a newly published white paper detailing how the Human Cell Atlas is expected to change the way we diagnose, monitor, and treat disease at a gathering of international scientists at Israel’s Weizmann Institute of Science, 10/2017.

For Regev, the importance of the Human Cell Atlas goes beyond its promise to revolutionize biology and medicine. As she once put it, without an atlas of our cells, “we don’t really know what we’re made of.”

Regev, turned to a technique known as RNA interference (she now uses CRISPR), which allowed her to systematically shut genes down. Then she looked at which genes were expressed to determine how the cells’ response changed in each case. Her team singled out 100 different genes that were involved in regulating the response to the pathogens—some of which weren’t previously known to be involved in immune function. The study, published in Science, generated headlines.

The project, the Human Cell Atlas, aims to create a reference map that categorizes all the approximately 37 trillion cells that make up a human. The Human Cell Atlas is often compared to the Human Genome Project, the monumental scientific collaboration that gave us a complete readout of human DNA, or what might be considered the unabridged cookbook for human life. In a sense, the atlas is a continuation of that project’s work. But while the same DNA cookbook is found in every cell, each cell type reads only some of the recipes—that is, it expresses only certain genes, following their DNA instructions to produce the proteins that carry out a cell’s activities. The promise of the Human Cell Atlas is to reveal which specific genes are expressed in every cell type, and where the cells expressing those genes can be found.

Regev says,

The final product, will amount to nothing less than a “periodic table of our cells,” a tool that is designed not to answer one specific question but to make countless new discoveries possible.

Sequencing the RNA of the cells she’s studying can tell her only so much. To understand how the circuits change under different circumstances, Regev subjects cells to different stimuli, such as hormones or pathogens, to see how the resulting protein signals change.

“the modeling step”—creating algorithms that try to decipher the most likely sequence of molecular events following a stimulus. And just as someone might study a computer by cutting out circuits and seeing how that changes the machine’s operation, Regev tests her model by seeing if it can predict what will happen when she silences specific genes and then exposes the cells to the same stimulus.

By sequencing the RNA of individual cancer cells in recent years—“Every cell is an experiment now,” she says—she has found remarkable differences between the cells of a single tumor, even when they have the same mutations. (Last year that work led to Memorial Sloan Kettering’s Paul Marks Prize for Cancer Research.) She found that while some cancers are thought to develop resistance to therapy, a subset of melanoma cells were resistant from the start. And she discovered that two types of brain cancer, oligodendroglioma and astrocytoma, harbor the same cancer stem cells, which could have important implications for how they’re treated.

As a 2017 overview of the Human Cell Atlas by the project’s organizing committee noted, an atlas “is a map that aims to show the relationships among its elements.” Just as corresponding coastlines seen in an atlas of Earth offer visual evidence of continental drift, compiling all the data about our cells in one place could reveal relationships among cells, tissues, and organs, including some that are entirely unexpected. And just as the periodic table made it possible to predict the existence of elements yet to be observed, the Human Cell Atlas, Regev says, could help us predict the existence of cells that haven’t been found.

This year alone it will fund 85 Human Cell Atlas grants. Early results are already pouring in.

  • In March, Swedish researchers working on cells related to human development announced they had sequenced 250,000 individual cells.
  • In May, a team at the Broad made a data set of more than 500,000 immune cells available on a preview site.

The goal, Regev says, is for researchers everywhere to be able to use the open-source platform of the Human Cell Atlas to perform joint analyses.

Eric Lander, PhDthe founding director and president of the Broad Institute and a member of the Human Cell Atlas Organizing Committee, likens it to genomics.

“People thought at the beginning they might use genomics for this application or that application,” he says. “Nothing has failed to be transformed by genomics, and nothing will fail to be transformed by having a cell atlas.”

“How did we ever imagine we were going to solve a problem without single-cell resolution?”

SOURCE

https://www.technologyreview.com/s/611786/the-cartographer-of-cells/?utm_source=MIT+Technology+Review&utm_campaign=Alumni-Newsletter_Sep-Oct-2018&utm_medium=email

Other related articles published in this Open Access Online Scientific Journal include the following:

 

University of California Santa Cruz’s Genomics Institute will create a Map of Human Genetic Variations

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2015/01/13/university-of-california-santa-cruzs-genomics-institute-will-create-a-map-of-human-genetic-variations/

 

Recognitions for Contributions in Genomics by Dan David Prize Awards

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/07/31/recognitions-for-contributions-in-genomics-by-dan-david-prize-awards/

 

ENCODE (Encyclopedia of DNA Elements) program: ‘Tragic’ Sequestration Impact on NHGRI Programs

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/09/18/encode-encyclopedia-of-dna-elements-program-tragic-sequestration-impact-on-nhgri-programs/

 

Single-cell Sequencing

Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/07/04/genomic-diagnostics-three-techniques-to-perform-single-cell-gene-expression-and-genome-sequencing-single-molecule-dna-sequencing/

 

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT – See, Aviv Regev

REAL TIME PRESS COVERAGE & Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2017/03/13/16th-annual-cancer-research-symposium-koch-institute-friday-june-16-9am-5pm-kresge-auditorium-mit/

 

LIVE 11/3/2015 1:30PM @The 15th Annual EmTech MIT – MIT Media Lab: Top 10 Breakthrough Technologies & 2015 Innovators Under 35 – See, Gilead Evrony

REAL TIME PRESS COVERAGE & Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2015/11/03/live-1132015-130pm-the-15th-annual-emtech-mit-mit-media-lab-top-10-breakthrough-technologies-2015-innovators-under-35/

 

Cellular Guillotine Created for Studying Single-Cell Wound Repair

Reporter: Irina Robu, PhD

https://pharmaceuticalintelligence.com/2017/06/29/cellular-guillotine-created-for-studying-single-cell-wound-repair/

 

New subgroups of ILC immune cells discovered through single-cell RNA sequencing

Reporter: Stephen J Williams, PhD

https://pharmaceuticalintelligence.com/2016/02/17/new-subgroups-of-ilc-immune-cells-discovered-through-single-cell-rna-sequencing-from-karolinska-institute/

 

#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

https://pharmaceuticalintelligence.com/2016/01/12/jpm16-illuminas-ceo-on-new-genotyping-array-called-infinium-xt-and-bio-rad-partnership-for-single-cell-sequencing-workflow/

 

Juno Acquires AbVitro for $125M: high-throughput and single-cell sequencing capabilities for Immune-Oncology Drug Discovery

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/12/juno-acquires-abvitro-for-125m-high-throughput-and-single-cell-sequencing-capabilities-for-immune-oncology-drug-discovery/

 

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

https://pharmaceuticalintelligence.com/2015/10/27/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/

 

Genome-wide Single-Cell Analysis of Recombination Activity and De Novo Mutation Rates in Human Sperm

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

https://pharmaceuticalintelligence.com/2012/08/07/genome-wide-single-cell-analysis-of-recombination-activity-and-de-novo-mutation-rates-in-human-sperm/

REFERENCES to Original studies

In Science, 2018

Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

 See all authors and affiliations

Science  21 Apr 2017:
Vol. 356, Issue 6335, eaah4573
DOI: 10.1126/science.aah4573
Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis

See all authors and affiliations

Science  26 Apr 2018:
eaar3131
DOI: 10.1126/science.aar3131

In Nature, 2018 and 2017

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.

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