Archived Content

Cambridge Healthtech Institute’s Third Annual
Genomic Data Analysis and Interpretation
Turning Data into Knowledge
March 5-7, 2012| Hilton San Diego Resort | San Diego, California 

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TUESDAY, MARCH 6

7:30 am Breakfast Presentation (Sponsorship Opportunity Available) or Morning Coffee

8:15 Java and Jive Discussion Groups

Grab a cup of coffee and join one of the facitilitated discussion groups. These are moderated discussions with brainstorming and interactive problem solving, allowing conference participants from diverse areas to exchange ideas, experiences, and develop future collaborations around a focused topic. Click here to submit a discussion topic 

View Java and Jive Discussion Groups 

 

The Long and the Short of IT and Non-Coding RNA 

9:30 Chairperson's Remarks

Stuart Tugendreich, Ph.D., Director, Product Management, Ingenuity Systems, Inc.

9:35 EteRNA - Solving the RNA Design Problem with 30,000 People

Jeehyung Lee, Research Scientist in Treuille Lab, Computer Science and Robotics, Carnegie Mellon University

We introduce EteRNA, an Internet-based RNA design competition where players design RNA sequence to match given target shapes, and receive information-rich wet-lab feedback from high-throughput RNA synthesis and chemical mapping. We show that players were able to uncover rules for robust RNA design from continuous wet-lab feedback.

10:10 Transcriptome Sequencing Identifies Unannotated Long Non-Coding RNAs Implicated in Cancer Progression

Matthew Iyer, Research Scientist in Chinnaiyan Lab, Center for Computational Medicine and Bioinformatics, University of Michigan

New non-coding RNAs can be discovered by assembling transcripts from RNA-seq data. We apply this approach across cancer samples to find RNAs that distinguish localized tumors from benign forms of the disease.

10:45 Networking Coffee Break

11:00 Analysis of Genomic Variation in Non-Coding Elements Using Population-Scale Sequencing Data from the 1000 Genomes Project

Xinmeng Jasmine Mu, Research Scientist in Gerstein Lab, Computational Biology and Bioinformatics, Yale University

To study selective pressure on non-coding elements, we investigated a full spectrum of genomic variations from next-generation sequencing data in the 1000 Genomes Project. We developed a framework for combining these variation data with non-coding elements, calculating various population-based metrics to compare classes and subclasses of elements, and developing element-aware aggregation procedures to probe the internal structure of an element. These analyses provided novel insights into the functional and structural roles of non-coding RNAs and other non-coding elements.

11:35 Sponsored Software Drive Presentation (Opportunity Available)

12:10 pm RNA Structure Characterization from High-Throughput Chemical Mapping Experiments

Sharon Aviran, Ph.D., Research Scientist, Center for Computational Biology, University of California, Berkeley

We have recently developed a high-throughput structure characterization technique, called SHAPE-Seq, which simultaneously measures quantitative, single nucleotide-resolution, secondary and tertiary structural information for hundreds of RNA molecules of arbitrary sequence. SHAPE-Seq combines selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE) chemical mapping with multiplexed paired-end deep sequencing of primer extension products. This generates millions of sequencing reads, which are then analyzed using a fully automated data analysis pipeline. Previous bioinformatics methods, in contrast, are laborious, heuristic, and expert-based, and thus prohibit high-throughput chemical mapping. Here, we describe the SHAPE-Seq technique and focus on its automated data analysis method, which relies on a novel probabilistic model of a SHAPE-Seq experiment, adjoined by a rigorous maximum likelihood estimation framework.

12:45 Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own

 

ngs data analysis and interpretation 

2:00 Chairperson's Remarks

Ali Torkamani, Ph.D., Director of Drug Discovery and Assistant Professor, Scripps Translational Science Institute
 

2:05 GenPlay, a Multipurpose Genome Analyzer and Browser

Eric Bouhassira, Ph.D., Principal Investigator, Medicine & Hematology, Albert Einstein College of Medicine

GenPlay is a tool for rapid analysis and data processing of genomic data. It is written in Java and runs on all major operating systems. GenPlay recognizes a wide variety of common genomic data formats from microarray- or sequencing-based platforms and offers a library of operations (normalization, binning, smoothing) to process raw data into visualizable tracks. GenPlay displays tracks adapted to summarize gene structure, gene expression, repeat families, CPG islands, etc. as well as custom tracks to show the results of RNA-Seq, ChIP-Seq, TimEX-Seq and single nucleotide polymorphism (SNP) analysis. GenPlay can be used to perform various operation between tracks and to generate statistics (minimum, maximum, SD, correlation, etc.). The tools provided include Gaussian filter, peak finders, signal saturation, island finders. The software also offers graphical features such as scatter plots and bar charts to depict signal repartition. The library of operations is continuously growing based on the emerging needs.

2:40 Sequence Based Association Studies through Variant, Gene, and Pathway Annotations

Ali Torkamani, Ph.D., Director of Drug Discovery and Assistant Professor, Scripps Translational Science Institute

As high-throughput sequencing costs and efficiency continue to improve at a blistering pace, our ability to generate variant data has quickly outpaced our ability to interpret variant data. Standard univariate approaches to genome analysis cannot address the locus and allelic heterogeneity that almost certainly underlies most common disease predisposition. We present a potential solution involving gene and pathway annotation of variant data, in order to put into perspective the overabundance of a particular variant or set of variants in diseased individuals.

Sponsored by
Genomatix Software 
3:15 NGS Towards Clinical Genomics: The Need for an Integrated Data Interpretation Environment

Martin Seifert, Ph.D., CEO, Genomatix GmbHWe will show some examples how the combination of efficient tools and comprehensive biological background data within the data interpretation environment of the Genomatix NGS analysis solutions can lead to understandable yet concise results for scientific publications or actionable biomedical insights alike; in hours instead of weeks or month.


3:30 Networking Refreshment Break in the Exhibit Hall with Poster Viewing

 

Explaining Epigenetics 

4:00 Quantitative Proteomic Analysis of Histone Exchange and Chromatin Dynamics

Alan Tackett, Ph.D., Associate Professor, Director UAMS Proteomics Facility, University of Arkansas for Medical Sciences

Genome-wide studies use techniques like ChIP so histone posttranslational modifications (PTMs) can be analyzed for their roles in modulating gene transcription. A balanced level of chemical cross-linking is required to preserve the native chromatin state during immunopurification, while still allowing for solubility and interaction with affinity reagents. Bioinformatic analyses revealed that histones containing transcription activating histone PTMs exchange more rapidly than those correlated to gene silencing. We used an isotopic labeling technique combining affinity purification and mass spectrometry called transient isotopic differentiation of interactions as random or targeted (transient I-DIRT) to identify the amounts of chemical cross-linking required to prevent histone exchange during chromatin purification.

4:35 Accurate Profiling of Methylation Epialleles

Alexander Dobrovic, Ph.D., Head, Molecular Pathology Research & Development Department, Peter McCallum Cancer Centre

Methylation information is inherently complex because of the heterogeneity of methylation patterns. Each unique pattern of DNA methylation for a given genomic sequence, including fully methylated and unmethylated, would comprise one of the possible epialleles that can exist in a sample. Accordingly, methylation in a given region can only be accurately quantified in terms of epialleles using digital methodologies like next-generation sequencing. Unbiased amplification of epialleles also needs to be ensured.

5:10 Using Microdroplet PCR and Deep Sequencing to Study the Epigenetic Regulation of Human Immunity by CpG Methylation

Daniel Salomon, M.D., Associate Professor, Department of Molecular and Experimental Medicine, The Scripps Research Institute

We will describe our ongoing efforts to understand how CpG methylation regulates the activation of human CD4 T cells. First, we are interested in understanding the differences between naïve and memory helper T cells, both at the initial resting state and after activation. Next, what is the basic element of CpG level DNA methylation-mediated regulation? Is it the CpG islands or can targeted methylation of more isolated and individual CpG motifs be equally important as our current data reveals? Approaches to answering these questions for our work will be instructive for anyone planning work in the epigenetics of DNA methylation.

5:45 Welcome Reception in the Exhibit Hall with Poster Viewing

6:45 Close of Day



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