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Clinical Genomic Analysis Symposium 2016

Wednesday, May 25, 2016

Organized by IBM Research – Haifa and Edmond J. Safra Center for Bioinformatics at Tel-Aviv University

You are cordially invited to participate in a one-day symposium on clinical genomic analysis, to be held Wednesday, May 25, 2016 at the IBM Research lab in Haifa.

This full-day workshop will provide a forum for the research and development communities from both academia and industry to share their work, exchange ideas, and discuss issues, problems, and works-in-progress. The forum will also address future research directions and trends in the area of personalized healthcare and the use of "omics" and Big Data technologies for optimizing the individual care.

A special focus of this year's workshop will be clinical metagenomics and, specifically, the interplay between microbial communities and the human host and its effect on our health and behavior.

The workshop will take place in the auditorium of IBM Research – Haifa on the University of Haifa campus, from 09:30 to 16:30. Lunch and light refreshments will be served. Participation is free but registration is required.

Parking is available in the IBM parking lot following registration.

Please confirm your participation by May 24, 2016, via the symposium registration page.

Local Arrangements
, IBM Research - Haifa


Table header results




Oded Cohn, VP, IBM Research lab - Haifa

Session 1


Improving health - IBM Agenda
Dario Gil, VP, Science and Solutions, IBM Research


Evolution of Bacterial Pathogens,
Prof. Roy Kishony, Technion

Abstract: Antibiotic resistance is growing as a major public health concern. In the talk, I will present our combined theoretical-experimental-clinical efforts towards understanding, predicting and manipulating the evolution of antibiotic resistance. Novel experimental devices for laboratory evolution of drug resistance now allow us to unravel the genotypic diversity of multi-step adaptive paths to antibiotic resistance. Some of the laboratory concepts can be translated into the much more complex environment of infections within humans, where whole-genome sequencing of clinical isolates can reveal pathogen spread and evolution. Finally, I will present experimental tests of drug combinations and alternating treatments that are more resilient to evolution of antibiotic resistance. My hope is that these experimental and theoretical directions will help lead towards a new paradigm for genome-based diagnostics of microbial infections and guided treatments that are more resilient to evolution of resistance.


Identification and Visualization of Splicing Bias from Single Cell RNA Sequencing,
Dr. Tal Shay, Ben Gurion University of the Negev

Abstract: Single cell RNA sequencing (scRNA-seq) has revolutionized the understanding of cell population heterogeneity in many biological fields, including immunology. While the number of available scRNA-seq datasets increases fast, the availability of analysis methods is lagging behind. We have created JingleBells, a repository of publicly available scRNA-seq datasets in a uniform annotated BAM format. That format is readily visible in IGV, a popular tool for RNA-seq visualization. This repository is accompanied by a suit of methods to identify splicing bias, where the single cell isoform usage is significantly different from the population level usage. I will describe the methods and their application to several publicly available immune datasets, and identify cases where isoforms usage is regulated at the single cell level by a non-negligible proportion of the cells.



Session 2


Towards Deciphering the Functional Genetics and Neo-antigenic Landscape in Melanoma,
Prof. Yardena Samuels, Weizmann Institute


The Genetics of MVP – Using Rapid Parallel Sequencing for Mutation Detection,
Dr. Ronen Durst, Hadassah Medical Center

Abstract: Mitral valve prolapse (MVP) is a common cardiac valve disease that affects nearly 1 in 40 individuals. It can manifest as mitral regurgitation and is the leading indication for mitral valve surgery. Despite a clear heritable component, the genetic aetiology leading to non-syndromic MVP has remained elusive for many years. Four affected individuals from a large multigenerational family segregating non-syndromic MVP underwent capture sequencing of the linked interval on chromosome 11. Using DNA enrichment of linked choromosomal sequcne and massive parallel sequencing allowed us to fine a missense mutation in the DCHS1 gene, the human homologue of the Drosophila cell polarity gene dachsous (ds), that segregates with MVP in the family. This mutatation was further validated by morpholino knockdown of the zebrafish homologue dachsous1b resulted in a cardiac atrioventricular canal defect that could be rescued by wild-type human DCHS1, but not by DCHS1 messenger RNA with the familial mutation. Further genetic studies identified two additional families in which a second deleterious DCHS1 mutation segregates with MVP. Both DCHS1 mutations reduce protein stability as demonstrated in zebrafish, cultured cells and, notably, in mitral valve interstitial cells (MVICs) obtained during mitral valve repair surgery of a proband. Dchs1(+/-) mice had prolapse of thickened mitral leaflets, which could be traced back to developmental errors in valve morphogenesis. DCHS1 deficiency in MVP patient MVICs, as well as in Dchs1(+/-) mouse MVICs, result in altered migration and cellular patterning, supporting these processes as aetiological underpinnings for the disease. This study demonstrate the power of new generation sequencing in elucidating mutation causing complex diseases.


Keynote: The Danish National Biobank and health Register Information: When an Entire Country is a Cohort Study,
Prof. Mads Melbye, Director, Institut Serum Denmark



Session 3


The Growing Role of Exogenous Data in Healthcare,
Dr. Michael Fahey, Manager for Exogenous Data at IBM Watson Health


Enhancer Methylation Dynamics Contribute to Cancer Plasticity and Patient Mortality,
Dr. Carmit Levy, Tel Aviv University

Abstract: During development enhancers play pivotal roles in regulating gene expression programs; however, their involvement in cancer progression has not been fully characterized. We performed an integrative analysis of DNA methylation, RNA-seq and small RNA-seq profiles from thousands of patients, including 25 diverse primary malignances and 7 body sites of metastatic melanoma. We found that enhancers are consistently the most differentially methylated regions (DMR) as cancer progress from normal to primary tumors and then to metastases, compared to other genomic features. Remarkably, identification of enhancer DMRs (eDMRs) enabled classification of primary tumors according to physiological organ systems and in metastasis eDMRs are the most correlated with patient outcome. To further understand eDMR role in cancer progression we developed a model to predict genes and microRNAs that are regulated by enhancer and not promotor methylation, which shows high accuracy with chromatin architecture methods and was experimentally validated. Interestingly, among all metastatic melanoma eDMRs the most correlated with patient survival were eDMRs that 'switched' their methylation patterns back and forth between normal, primary and metastases and target cancer drivers, e.g.: KIT. We further demonstrated that eDMR target genes were modulated in melanoma bone metastasis microenvironment, suggesting that eDMRs respond to microenvironmental cues in metastatic niches. Our findings that aberrant methylation in cancer cells mostly affects enhancers, which contribute to tumor progression and cancer cell plasticity, will facilitate development of epigenetic anticancer approaches.


ERACoSysMed – Funding Opportunity in System Biology,
Dr. Ahmi Ben-Yehudah, Israel Ministry of Health

Abstract: ERACoSysMed ("Collaboration on systems medicine funding to promote the implementation of systems biology approaches in clinical research and medical practice") started its activities in January 2015 as the first ERA-Net on Systems Medicine under the EU framework program Horizon2020. ERACoSysMed aims to enhance the implementation of systems biology approaches in both clinical research and medical practice throughout Europe and Israel.


Integrated Genomic and Electronic Medical Record Analyses Identify Dyslipidemia as a Strong Inherited Risk Factor for Autism,
Dr. Alal Eran, Ben Gurion University of the Negev

Abstract: Recent genomic studies have revealed that the clinical heterogeneity characterizing autism spectrum disorder (ASD) is matched by extreme genetic heterogeneity. Untangling this complexity-squared is essential for improving ASD diagnosis and prognosis. Toward this goal we integrated familial whole exome sequences with neurodevelopmental expression patterns to identify clusters of neurodevelopmentally-coregulated, sexually dimorphic, ASD-segregating deleterious variants. While the function of most clusters converged on previously described ASD etiologies (including immune, synaptic, and transcriptional regulation functions), 20% of the identified clusters were enriched with lipid regulation functions. These include sets of variants affecting low density lipoprotein receptor (LDLR, P=1.93x10-7), lipoprotein lipase (LPL, P=1.55x10-6), and multiple coexpressed genes of the Reactome metabolism of lipids and lipoprotein pathway (P=2.95x10-5). Collectively, these variants are predicted to lead to high cholesterol and triglyceride levels. Therefore, we used electronic medical records to examine blood lipid profiles of children with ASD as compared to neurotypical children. ASD was associated with elevated triglycerides (OR=3.14, 95% CI=[2.44,4.03], P=1.19x10-23), LDL (OR=1.78, 95% CI=[1.40,2.26], P=6.42x10-7), and total cholesterol (OR=1.67, 95% CI=[1.35,2.06], P=5.55x10-7). We further used health claims data to compare the abundance of dyslipidemia diagnoses in individuals with ASD, unaffected siblings, and unrelated controls, and. Children with ASD had a strong enrichment of dyslipidemia diagnoses as compared to matched controls (OR=1.95, 95% CI=[1.80,2.11], P=1.94x10-65) as well as unaffected siblings (OR=1.76, 95%CI= [1.61 1.92], P=2.25x10-36). Finally, mouse models of dyslipidemia were found to exhibit striking phenotypic similarities to autism mouse models (power = 0.97). Taken together, this work suggests that dyslipidemia might be a strong inherited risk factor for a subtype of ASD, opening new avenues for screening, intervention, and prevention.


Closing Remarks,
Dr. Yaara Goldschmidt, IBM Research Labs


Demos and Reception

Program Committee

Edmond J. Safra Bioinformatics Center IBM Research - Haifa