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Bioinformatics Tool Review: Genome Variant Analysis Tools

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer Informatics, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Genome Biology, Personalized and Precision Medicine & Genomic Research, Uncategorized, tagged , , , , , , , , , , , , , on October 23, 2018| Leave a Comment »

Bioinformatics Tool Review: Genome Variant Analysis Tools, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Bioinformatics Tool Review: Genome Variant Analysis Tools

Curator: Stephen J. Williams, Ph.D.

Updated 02/07/2021

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.

Nature. 2015 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 LL1, Terczyńska-Dyla E1, Mørk N2, Scavenius C1, Enghild JJ1, Höning K3, Hornung V3,4, Christiansen M5,6, Mogensen TH2,6, Hartmann 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.

Updated 02/07/2021

InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution
Jun-Ichi Takeda , Kentaro Nanatsue , Ryosuke Yamagishi , Mikako Ito , Nobuhiko Haga 2, Hiromi Hirata , Tomoo Ogi , Kinji Ohno in NAR Genomics and  Bioinformatics. 2020 May 26;2(2):lqaa038.doi: 10.1093/nargab/lqaa038. eCollection 2020 Jun.

Abstract

In predicting the pathogenicity of a nonsynonymous single-nucleotide variant (nsSNV), a radical change in amino acid properties is prone to be classified as being pathogenic. However, not all such nsSNVs are associated with human diseases. We generated random forest (RF) models individually for each amino acid substitution to differentiate pathogenic nsSNVs in the Human Gene Mutation Database and common nsSNVs in dbSNP. We named a set of our models ‘Individual Meta RF’ (InMeRF). Ten-fold cross-validation of InMeRF showed that the areas under the curves (AUCs) of receiver operating characteristic (ROC) and precision-recall curves were on average 0.941 and 0.957, respectively. To compare InMeRF with seven other tools, the eight tools were generated using the same training dataset, and were compared using the same three testing datasets. ROC-AUCs of InMeRF were ranked first in the eight tools. We applied InMeRF to 155 pathogenic and 125 common nsSNVs in seven major genes causing congenital myasthenic syndromes, as well as in VANGL1 causing spina bifida, and found that the sensitivity and specificity of InMeRF were 0.942 and 0.848, respectively. We made the InMeRF web service, and also made genome-wide InMeRF scores available online (https://www.med.nagoya-u.ac.jp/neurogenetics/InMeRF/).

Source: https://pubmed.ncbi.nlm.nih.gov/33543123/

ADDRESS: A database of disease-associated human variants incorporating protein structure and folding stabilities
Jaie Woodard , Chengxin Zhang , Yang Zhang in J Mol Biol. 2021 Feb 1;166840. doi: 10.1016/j.jmb.2021.166840.

Abstract

Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS.

Source: https://pubmed.ncbi.nlm.nih.gov/33539887/

PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes
Sebastian Niehus , Hákon Jónsson , Janina Schönberger  , Eythór Björnsson, Doruk Beyter , Hannes P Eggertsson , Patrick Sulem  Kári Stefánsson , Bjarni V Halldórsson Birte Kehr 
in Nature Communications. 2021 Feb 1;12(1):730.doi: 10.1038/s41467-020-20850-5.

Abstract

Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

Source: https://pubmed.ncbi.nlm.nih.gov/33526789/

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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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

Posted in Cancer Informatics, Computational Biology/Systems and Bioinformatics, Gene Regulation, Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, Genomic Testing: Methodology for Diagnosis, mRNA Therapeutics, Mutagenesis, Single Cell Genomics, Single-cell sequencing, Variation in human protein-coding regions on September 3, 2018| Leave a Comment »

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

4.1.3

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, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics

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, PhD, the 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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    Nature 551, 658
    Rights & permissions

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    The Human Cell Atlas: from vision to reality

    • …body gets officially under way, Aviv Regev, Sarah Teichmann and colleagues…
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Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Posted in Alzheimer's Disease, Big Data, Big Data & Analytics, BioBanking, Biomarkers & Medical Diagnostics, Cancer and Current Therapeutics, Cancer Informatics, Cancer Screening, Diagnostics and Lab Tests, Meta-analysis of transcriptome data, Pharmaceutical Industry Competitive Intelligence, Pharmacotherapy and Cell Activity, Scientific & Biotech Conferences: Press Coverage, Transcriptomics, Uncategorized, tagged , , , , , , , , on July 11, 2018| Leave a Comment »

Live Conference Coverage @MedCity news Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

5:00 – 5:45 PM Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Reporter: Stephen J. Williams, Ph.D.

 

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Speakers:
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

Bonnie Anderson and Veracyte produces genomic tests for thyroid and other cancer diagnosis.  Kevin Hrusovksy and Precision Health uses peer reviewed evidence based medicine to affect precision medicine decision.

Bonnie: aim to get a truth of diagnosis.  Getting tumor tissue is paramount as well as properly preserved tissue.  They use deep RNA sequencing  and machine learning  in their clinically approved tests.

Kevin: Serial biospace entrepreneur.  Two diseases, cancer and neurologic, have been diseases which have been hardest to get reproducible and validated biomarkers of early disease.  He concentrates on protein biomarkers.

Heather:  FDA has recently approved drugs for early disease intervention.  However the use of biomarkers can go beyond patient stratification in clinical trials.

Kevin: 15 approved drugs for MS but the markers are scans looking for brain atrophy which is too late of an endpoint.  So we need biomarkers of early disease progression.  We can use those early biomarkers of disease progression so pharma can target those early biomarkers and or use those early biomarkers of disease progression  for endpoint

Bonnie: exciting time in the early diagnostics field. She prefers transcriptomics to DNA based methods such as WES or WGS (whole exome or whole genome sequencing).  It was critical to show data on the cost savings imparted by their transcriptomic based thryoid cancer diagnostic test for payers to consider this test eligible for reimbursement.

Kevin: There has been 20 million  CAT scans for  cancer but it is estimated 90% of these scans led to misdiagnosis. Biomarker  development  has revolutionized diagnostics in this disease area.  They have developed a breakthrough panel of ten protein biomarkers in serum which he estimates may replace 5 million mammograms.

All panelists agreed on the importance of regulatory compliance and the focus of new research should be on early detection.  In addition they believe that Dr. Gotlieb’s appointment to the FDA is a positive for the biomarker development field, as Dr. Gotlieb understands the potential and importance of early detection and prevention of disease.  Kevin also felt Dr. Gotlieb understands the importance of incorporating biomarkers as endpoints in clinical trials.  Over 750 phase 1,2, and 3 clinical trials use biomarker endpoints but the pharma companies still need to prove the biomarkers clinical relevance to the FDA.They also agreed it would be helpful to involve advocacy groups in putting more pressure on the healthcare providers and policy makers on this importance of diagnostics as a preventative measure.

In addition, the discovery and use of biomarkers as disease endpoints has led to a resurgence of Alzheimer’s disease drug development by companies which have previously given up on these type of neurodegenerative diseases.

Kevin feels proteomics offers great advantages over DNA-based diagnostics, especially in cancer such as ovarian cancer, where a high degree of specificity for a diagnostic test is required to ascertain if a woman should undergo prophylactic oophorectomy.  He suggests that a new blood-based protein biomarker panel is being developed for early detection of some forms of ovarian cancer.

Please follow on Twitter using the following #hash tags and @pharma_BI

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

 

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

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

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Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

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Cracking the Genome – Inside the Race to Unlock Human DNA – quotes in newspapers

Posted in BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Computational Biology/Systems and Bioinformatics, Genome Biology on November 20, 2017| Leave a Comment »

Cracking the Genome – Inside the Race to Unlock Human DNA – quotes in newspapers, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Cracking the Genome – Inside the Race to Unlock Human DNA – quotes in newspapers

Reporter: Aviva Lev-Ari, PhD, RN

 

Cracking the Genome

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Cracking the Genome

Inside the Race to Unlock Human DNA

Kevin Davies

In 1953, James Watson and Francis Crick unveiled the double helix structure of DNA. The discovery was a profound moment in the history of science, but solving the structure of the genetic material did not reveal what the human genome sequence actually was, or what it says about who we are. Cracking the code of life would take another half a century.

In 2001, two rival teams of scientists shared the acclaim for sequencing the human genome. Kevin Davies, founding editor of Nature Genetics, has relentlessly followed the story as it unfolded week by week since the dawn of the Human Genome Project in 1990. Here, in rich human and scientific detail, is the compelling story of one of the greatest scientific feats ever accomplished: the sequencing of the human genome.

In brilliant, accessible prose, Davies captures the drama of this momentous achievement, drawing on his own genetics expertise and on interviews with the key scientists. Davies details the fraught rivalry between the public consortium, chaperoned by Francis Collins, and Celera Genomics, directed by sequencer J. Craig Venter. And in this newly updated edition, Davies sheds light on the secrets of the sequence, highlighting the myriad ways in which genomics will impact human health for the generations to come.

Cracking the Genome is the definitive, balanced account of how the code that holds the answer to the origin of life, the evolution of humanity, and the future of medicine was finally broken.

Kevin Davies is the founding editor of Nature Genetics and is currently editor-in-chief of Bio•IT World. He graduated from Oxford University and holds a Ph.D. in genetics from the University of London.

“For an up-to-the-minute account of one of the most dramatic periods in present-day science, Cracking the Genome is an essential read.”

— Sunday Times

“A superb job… A tantalizing glimpse of the ethical perils and technological possibilities awaiting humanity.”

— Los Angeles Times

“A rollicking good tale about an enduring intellectual monument.”

— American Scientist

“The race is over, and Davies was there, all along, providing the running commentary—and there, too, at the finish line. In Cracking the Genome, he hands out the prizes.”

— The Independent

“Davies has tracked one of the most important stories ever to unfold. Davies helps readers understand how the deciphering of our genetic code will revolutionize our lives while posing serious ethical dilemmas.”

— Science News

“An impressive job of contextualizing the science within a political, economic, and social framework, creating a lively tale as accessible to non—specialists as it is to scientists.”

— Publishers Weekly

“Investors and others looking for a quick primer on the science and business of biotechnology will find this a useful guide.”

— Business Week

“In Davies’ prose, this story of molecular biology and the Human Genome Project is as compelling as any Arthurian legend. In a fast-moving approachable style, Davies captures the uncovering of biology’s Holy Grail, relying on his own expertise in genetics and interviews with key players such as Collins and Venter.”

— Margaret R. McLean – History and Philosophy of the Life Sciences
SOURCE

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SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke

Posted in Atherogenic Processes & Pathology, BioBanking, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cardiac and Cardiovascular Surgical Procedures, Computational Biology/Systems and Bioinformatics, Diabetes Mellitus, Disease Biology, Epigenetics and Cardiovascular Risks, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, Meta-analysis of transcriptome data, Origins of Cardiovascular Disease, Vascular Diseases on July 10, 2017| Leave a Comment »

SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

SNP-based Study on high BMI exposure confirms CVD and DM Risks – no associations with Stroke

Reporter: Aviva Lev-Ari, PhD, RN

Genes Affirm: High BMI Carries Weighty Heart, Diabetes Risk – Mendelian randomization study adds to ‘burgeoning evidence’

by Crystal Phend, Senior Associate Editor, MedPage Today, July 05, 2017

 

The “genetically instrumented” measure of high BMI exposure — calculated based on 93 single-nucleotide polymorphisms associated with BMI in prior genome-wide association studies — was associated with the following risks (odds ratios given per standard deviation higher BMI):

  • Hypertension (OR 1.64, 95% CI 1.48-1.83)
  • Coronary heart disease (CHD; OR 1.35, 95% CI 1.09-1.69)
  • Type 2 diabetes (OR 2.53, 95% CI 2.04-3.13)
  • Systolic blood pressure (β 1.65 mm Hg, 95% CI 0.78-2.52 mm Hg)
  • Diastolic blood pressure (β 1.37 mm Hg, 95% CI 0.88-1.85 mm Hg)

However, there were no associations with stroke, Donald Lyall, PhD, of the University of Glasgow, and colleagues reported online in JAMA Cardiology.

The associations independent of age, sex, Townsend deprivation scores, alcohol intake, and smoking history were found in baseline data from 119,859 participants in the population-based U.K. Biobank who had complete medical, sociodemographic, and genetic data.

“The main advantage of an MR approach is that certain types of study bias can be minimized,” the team noted. “Because DNA is stable and randomly inherited, which helps to mitigate errors from reverse causality and confounding, genetic variation can be used as a proxy for lifetime BMI to overcome limitations such as reverse causality and confounding, a process that hampers observational analyses of obesity and its consequences.”

 

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

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

Posted in Analytical Instruments Industry, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cell Biology, Cell Biology, Signaling & Cell Circuits, Computational Biology/Systems and Bioinformatics, DNA repair, Drug Discovery Chemistry, Gene Regulation and Evolution, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Meta-analysis of transcriptome data, Microfuidics, Transcriptomics, tagged , , , , , on July 4, 2017| Leave a Comment »

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

Curator: Aviva Lev-Ari, PhD, RN

4.2.3

4.2.3   Genomic Diagnostics: Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single Molecule DNA Sequencing, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics

This article presents Three Techniques to Perform Single Cell Gene Expression and Genome Sequencing Single molecule DNA sequencing

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The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biomedical Measurement Science, Computational Biology/Systems and Bioinformatics, HealthCare IT, Intelligent Information Systems, Meta-analysis of transcriptome data, tagged on June 21, 2017| Leave a Comment »

The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Reporter: Aviva Lev-Ari, PhD, RN

 

 

The BioPharma Industry’s Unrealized Wealth of Data

by Ben Szekely, Vice President of Solutions and Pre-sales, Cambridge Semantics

 

Solving the great medical challenges of our time reside within patient data. Clinical trial data, real-world evidence, patient feedback, genetic data, wearables data and adverse event reports contain signals to target medicines at the right patient populations, improve overall safety, and uncover the next blockbuster therapy for unmet medical needs.

However, data sources are large, diverse, multi-structured, messy and highly regulated presenting numerous challenges. As result, extracting value from data are slow to come and require manual work or long-poll dependencies on IT and Data Science teams.

Fortunately, there are new ways being adopted to take better advantage of the ever-growing volumes of patient data.  Called ‘Smart’ Patient Data Lakes (SPDL), these tools create an Enterprise Knowledge Graph built upon foundational and open Semantic Web technology standards, providing rich descriptions of data and flexibility end-to-end.  With the SPDL, biopharma researchers can:

  • Quickly on-board new data without requiring up-front modeling or mapping, ingesting data from any source versus months or weeks of preparation
  • Dynamically map and prepare data at analytics time
  • Horizontally scale in cloud or on-prem infrastructure to 100’s of nodes – allowing billions of facts to be analyzed, queried and explored in real-time   

The world’s BioPharma and research institutions are sitting on a wealth of highly differentiating and life-saving data and should begin to realize its value via Smart Patient Data Lakes (SPDL).

 

 

CONTACT: Nadia Haidar

Global Results Communications ∙ 949-278-7328 ∙ nhaidar@globalresultspr.com

 

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Sperm Analysis by Smart Phone

Posted in Affordable Care Act, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cell Biology, Computational Biology/Systems and Bioinformatics, Health Economics and Outcomes Research, HealthCare IT, Image Processing/Computing, Medical Devices R&D and Inventions, Medical Imaging Technology, Reproductive Biology & Bio Instrumentation, tagged , , , , , on March 29, 2017| Leave a Comment »

Sperm Analysis by Smart Phone, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Sperm Analysis by Smart Phone

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

 

Low sperm count and motility are markers for male infertility, a condition that is actually a neglected health issue worldwide, according to the World Health Organization. Researchers at Harvard Medical School have developed a very low cost device that can attach to a cell phone and provides a quick and easy semen analysis. The device is still under development, but a study of the machine’s capabilities concludes that it is just as accurate as the elaborate high cost computer-assisted semen analysis machines costing tens of thousands of dollars in measuring sperm concentration, sperm motility, total sperm count and total motile cells.

 

The Harvard team isn’t the first to develop an at-home fertility test for men, but they are the first to be able to determine sperm concentration as well as motility. The scientists compared the smart phone sperm tracker to current lab equipment by analyzing the same semen samples side by side. They analyzed over 350 semen samples of both infertile and fertile men. The smart phone system was able to identify abnormal sperm samples with 98 percent accuracy. The results of the study were published in the journal named Science Translational Medicine.

 

The device uses an optical attachment for magnification and a disposable microchip for handling the semen sample. With two lenses that require no manual focusing and an inexpensive battery, it slides onto the smart phone’s camera. Total cost for manufacturing the equipment: $4.45, including $3.59 for the optical attachment and 86 cents for the disposable micro-fluidic chip that contains the semen sample.

 

The software of the app is designed with a simple interface that guides the user through the test with onscreen prompts. After the sample is inserted, the app can photograph it, create a video and report the results in less than five seconds. The test results are stored on the phone so that semen quality can be monitored over time. The device is under consideration for approval from the Food and Drug Administration within the next two years.

 

With this device at home, a man can avoid the embarrassment and stress of providing a sample in a doctor’s clinic. The device could also be useful for men who get vasectomies, who are supposed to return to the urologist for semen analysis twice in the six months after the procedure. Compliance is typically poor, but with this device, a man could perform his own semen analysis at home and email the result to the urologist. This will make sperm analysis available in the privacy of our home and as easy as a home pregnancy test or blood sugar test.

 

The device costs about $5 to make in the lab and can be made available in the market at lower than $50 initially. This low cost could help provide much-needed infertility care in developing or underdeveloped nations, which often lack the resources for currently available diagnostics.

 

References:

 

https://www.nytimes.com/2017/03/22/well/live/sperm-counts-via-your-cellphone.html?em_pos=small&emc=edit_hh_20170324&nl=well&nl_art=7&nlid=65713389&ref=headline&te=1&_r=1

 

http://www.npr.org/sections/health-shots/2017/03/22/520837557/a-smartphone-can-accurately-test-sperm-count

 

https://www.ncbi.nlm.nih.gov/pubmed/28330865

 

http://www.sciencealert.com/new-smartphone-microscope-lets-men-check-the-health-of-their-own-sperm

 

https://www.newscientist.com/article/2097618-are-your-sperm-up-to-scratch-phone-microscope-lets-you-check/

 

https://www.dezeen.com/2017/01/19/yo-fertility-kit-men-test-sperm-count-smartphone-design-technology-apps/

 

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Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Computational Biology/Systems and Bioinformatics, CRISPR/Cas9 & Gene Editing, Genome Biology, HealthCare IT, Intelligent Information Systems, mRNA Therapeutics on January 4, 2017| Leave a Comment »

Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Reporter: Aviva Lev-Ari, PhD, RN

 

Synthego Raises $41 Million From Investors, Including a Top Biochemist

By MICHAEL J. de la MERCED JAN. 4, 2017

Jennifer Doudna, a professor at the University of California, Berkeley who helped develop advanced gene-editing techniques, is among investors in Synthego.

Synthego also drew in Dr. Doudna, who had crossed paths with the company’s head of synthetic biology at various industry conferences. According to Mr. Dabrowski, the money from her trust represents the single-biggest check from a non-institutional investor that the start-up has raised.

Synthego’s new funds will help the company take its products to a more global customer base, as well as broaden its offerings. The longer-term goal, Mr. Dabrowski said, is to help fully automate biotech research and take care of much of the laboratory work that scientists currently handle themselves.

The model is cloud technology, where companies rent out powerful remote server farms to handle their computing needs rather than rely on their own hardware.

“We’ll be able to do their full research workflow,” he said. “If you look at how cloud computing developed, it used to be that every company handled their server farm. Now it’s all handled in the cloud.”

SOURCE

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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

https://pharmaceuticalintelligence.com/2016/01/06/status-interference-initial-memorandum-crisprcas9-the-biotech-patent-fight-of-the-century/

 

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A novel 5-gene pancreatic adenocarcinoma classifier: Meta-analysis of transcriptome data – Clinical Genomics Research @BIDMC

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Screening, Genomic Expression, Meta-analysis of transcriptome data, Pancreatic adenocarcinoma classifier on December 28, 2016| Leave a Comment »

A novel 5-gene pancreatic adenocarcinoma classifier: Meta-analysis of transcriptome data – Clinical Genomics Research @BIDMC, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

A novel 5-gene pancreatic adenocarcinoma classifier: Meta-analysis of transcriptome data – Clinical Genomics Research @BIDMC

Curator: Tilda Barliya, PhD

Analysis of  Bhasin et al paper and Literature search

Table 1: 5-genes classifiers as biomarkers for PDAC:

Gene symbol Gene name Subcellular localization
ECT2 Epithelial cell transforming sequence 2 oncogene Nucleus, cytoplasm
AHNAK2 AHNAKE nucleoprotein 2 Plasma membrane, cytoplasm
POSTN Periostin, osteoblast specific factor Extracellular space
TMPRSS4 Transmembrane protease, serine 4 Plasma membrane

 
SERPINB5 Serpin peptidase inhibitor, clade B (ovalbumin) member 5 Extracellular space


Introduction:

  • Bhasin et al, conducted a beautiful study using a powerful meta-analysis from different sources to identify the “important/classifier” genes associated with Pancreatic Cancer (PDAC).
  • The authors identified 5 genes that were considered as good classifiers (table 1).
  • It is important to note that the meta-analysis was performed on tissue and microdissection samples.
  • In their summary, the authors aim to validate these genes in blood/urine samples.
  • While these genes might be over expressed in tissue samples it may not be true to their existence in blood and careful examination and validation is required.
  • Liquid biopsies are emerging as the go-to use tools for disease detection, mostly aimed for early diagnosis.
  • Liquid biopsies are non-invasive biopsies of blood, urine (potentially saliva) and their “exotic” components, i.e miRNA, exosomes etc.
  • Since Liquid biopsies are non-invasive, they are painless and patients are more complied.
  • It is important to note that there is a gap between the expression of a gene or a protein in tissue section and their expression in the blood and may not necessarily correlate.
  • It will be very interesting to follow their research and future outcomes.

Additional References:

  • TMPRSS4: an emerging potential therapeutic target in cancer.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4453593/

  • The tumour trail left in blood

http://www.nature.com/nature/journal/v532/n7598/full/532269a.html

Aashir Awan, PhD, wrote on 12/28/2016

I was wondering if these same 5 genes were upregulated in the pancreatic ductal adenocarcinoma cell lines that are available out there.  In doing cell biology work, there is always a dilemma whether cancer cell lines correctly re-capitulate in vivo tumors or not.  Personally, I prefer primary cell lines to do analysis but this finding can be used to test primary vs cell line.  In addition, I’ve attached the gene network for Ect2.  If you look carefully, the two big proteins that jump out are RACGAP1 and KIF23.  I think in designing therapies, combinatorial targets can yield the best outcomes.  Drugs directed towards two or more targets would seem ideal in my opinion.

ect2

Gene Network for Ect2

Original Research
Oncotarget. 2016 Apr 26;7(17):23263-81. doi: 10.18632/oncotarget.8139.

Meta-analysis of transcriptome data identifies a novel 5-gene pancreatic adenocarcinoma classifier.

Bhasin MK1, Ndebele K2, Bucur O2,3, Yee EU2, Otu HH4, Plati J2, Bullock A5, Gu X1, Castan E1, Zhang P1, Najarian R2, Muraru MS2, Miksad R5, Khosravi-Far R2, Libermann TA1.

Author information

Abstract

PURPOSE:

Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis. Superior early detection biomarkers are critical to improving PDAC survival and risk stratification.

EXPERIMENTAL DESIGN:

Optimized meta-analysis of PDAC transcriptome datasets identified and validated key PDAC biomarkers. PDAC-specific expression of a 5-gene biomarker panel was measured by qRT-PCR in microdissected patient-derived FFPE tissues. Cell-based assays assessed impact of two of these biomarkers, TMPRSS4 and ECT2, on PDAC cells.

RESULTS:

A 5-gene PDAC classifier (TMPRSS4, AHNAK2, POSTN, ECT2, SERPINB5) achieved on average 95% sensitivity and 89% specificity in discriminating PDAC from non-tumor samples in four training sets and similar performance (sensitivity = 94%, specificity = 89.6%) in five independent validation datasets. This classifier accurately discriminated PDAC from chronic pancreatitis (AUC = 0.83), other cancers (AUC = 0.89), and non-tumor from PDAC precursors (AUC = 0.92) in three independent datasets. Importantly, the classifier distinguished PanIN from healthy pancreas in the PDX1-Cre;LSL-KrasG12D PDAC mouse model. Discriminatory expression of the PDAC classifier genes was confirmed in microdissected FFPE samples of PDAC and matched surrounding non-tumor pancreas or pancreatitis. Notably, knock-down of TMPRSS4 and ECT2 reduced PDAC soft agar growth and cell viability and TMPRSS4 knockdown also blocked PDAC migration and invasion.

CONCLUSIONS:

This study identified and validated a highly accurate 5-gene PDAC classifier for discriminating PDAC and early precursor lesions from non-malignant tissue that may facilitate early diagnosis and risk stratification upon validation in prospective clinical trials. Cell-based experiments of two overexpressed proteins encoded by the panel, TMPRSS4 and ECT2, suggest a causal link to PDAC development and progression, confirming them as potential therapeutic targets.

KEYWORDS:

bioinformatics; biomarkers; meta-analysis; pancreatic cancer; transcriptome

PMID:
26993610
PMCID:
PMC5029625
DOI:
10.18632/oncotarget.8139

SOURCE

https://www.ncbi.nlm.nih.gov/pubmed/26993610 

Oncotarget, Vol. 7, No. 17 – Referred as PDF, above

 

BIDMC Researchers Discover Early Indicators of Pancreatic Cancer

LibermannBhasin_PancreasCancerStudy

Markers may help doctors detect pancreatic cancer before it becomes deadly

In photo: First author Manoj Bhasin, PhD, and co-senior author Towia Libermann, PhD, Co-Director and Director of BIDMC’s Genomics, Proteomics, Bioinformatics and Systems Biology Center.

SOURCE

http://www.bidmc.org/News/PRLandingPage/2016/March/Libermann-Pancreatic-Cancer-Research-2016.aspx

BOSTON – Pancreatic cancer, the fourth leading cause of cancer death in the United States, is often diagnosed at a late stage, when curative treatment is no longer possible. A team led by investigators at Beth Israel Deaconess Medical Center (BIDMC) has now identified and validated an accurate 5-gene classifier for discriminating early pancreatic cancer from non-malignant tissue. Described online in the journal Oncotarget, the finding is a promising advance in the fight against this typically fatal disease.

“Pancreatic cancer is a devastating disease with a death rate close to the incidence rate,” said co-senior author Towia Libermann, PhD, Director of the Genomics, Proteomics, Bioinformatics and Systems Biology Center at BIDMC and Associate Professor of Medicine at Harvard Medical School (HMS). “Because more than 90 percent of pancreatic cancer cases are diagnosed at the metastatic stage, when there are only limited therapeutic options, earlier diagnosis is anticipated to have a major impact on extending life expectancy for patients. There has been a lack of reliable markers, early indicators and risk factors associated with pancreatic cancer, but this new way of differentiating between healthy and malignant tissue offers hope for earlier diagnosis and treatment.”

The investigators used a number of publicly available gene expression datasets for pancreatic cancer and developed a strategy to reanalyze these datasets together, applying rigorous statistical criteria to compare different datasets from different laboratories and different platforms with each other. The team then selected a subset of data for developing a panel for differentiating between pancreatic cancer and healthy pancreas tissue and thereafter applied this “Pancreatic Cancer Predictor” to the remaining datasets for independent validation to confirm the accuracy of the markers.

After demonstrating and independently validating that a 5-gene pancreatic cancer predictor discriminated between cancerous and healthy tissue, the researchers applied the predictor to datasets that also included benign lesions of the pancreas, including pancreatitis and early stage cancer. The predictor accurately differentiated pancreatic cancer, benign pancreatic lesions, early stage pancreatic cancer and healthy tissue. The predictor achieved on average 95 percent sensitivity and 89 percent specificity in discriminating pancreatic cancer from non-tumor samples in four training sets and similar performance (94 percent sensitivity, 90 percent specificity) in five independent validation datasets.

“Using innovative data normalization and gene selection approaches, we combined the statistical power of multiple genomic studies and masked their variability and batch effects to identify robust early diagnostic biomarkers of pancreatic cancer,” said first author Manoj Bhasin, PhD, Co-Director of BIDMC’s Genomics, Proteomics, Bioinformatics and Systems Biology Center and Assistant Professor of Medicine at HMS.

“The identification and initial validation of a highly accurate 5-gene pancreatic cancer biomarker panel that can discriminate late and early stages of pancreatic cancer from normal pancreas and benign pancreatic lesions could facilitate early diagnosis of pancreatic cancer,” said co-senior author Roya Khosravi-Far, PhD, Associate Professor of Pathology at BIDMC. “Our findings may open a window of opportunity for earlier diagnosis and, consequently, earlier intervention and more effective treatment of this deadly cancer, leading to higher survival rates.”

The first diagnostic application of the panel may be for analyses of fine needle biopsies routinely used for diagnosing pancreatic cancer and for determining the malignant potential of mostly benign pancreatic cysts that can sometimes be precursors of pancreatic cancer. In addition to providing a new tool for diagnoses, the research may also lead to new insights into how pancreatic cancer arises.

“Because these five genes are ‘turned on’ so early in the development of pancreatic cancer, they may play roles as drivers of this disease and may be exciting targets for therapies,” said Libermann. Most of the five genes—named TMPRSS4, AHNAK2, POSTN, ECT2 and SERPINB5—have been linked to migration, invasion, adhesion, and metastasis of pancreatic or other cancers.

The scientists next plan to evaluate the precise roles of the five genes and to validate the accuracy of their diagnostic assay in a prospective clinical study. “Moving forward, we will explore the potential to convert this tissue-based diagnostic into a noninvasive blood or urine test,” Libermann said.

“To further enhance the diagnostic power of this biomarker, we plan to expand it by including non-coding RNAs, proteins, metabolites and mutations associated with pancreatic cancer. This will result in development of the first of its kind biomarker that gauges pancreatic cancer alterations from multiple genomic angles for making highly accurate diagnoses,” added Bhasin. Such an inexpensive and simple test could help transform the landscape of pancreatic cancer and help prevent many of the estimated 330,000 deaths that it causes worldwide each year.

Study coauthors include BIDMC investigators Kenneth Ndebele, Octavian Bucur, Eric Yee, Jessica Plati, Andrea Bullock, Xuesong Gu, Eduardo Castan, Peng Zhang, Robert Najarian, Maria Muraru and Rebecca Miksad, and the University of Nebraska-Lincoln’s Hasan H. Otu. The work was supported by the National Institutes of Health, National Cancer Institute and Ben and Rose Cole Charitable Pria Foundation.

SOURCE

http://www.bidmc.org/News/PRLandingPage/2016/March/Libermann-Pancreatic-Cancer-Research-2016.aspx

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