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Wednesday, August 15
7:30 am Breakfast Presentation or Morning Coffee (Sponsorship Opportunity Available)
8:30 Chairperson’s Remarks
Martin Seifert, Ph.D., CEO, Genomatix
8:35 Global Analysis of RNA Alternative Splicing by Deep Sequencing
Yi Xing, Ph.D., Assistant Professor, Department of Internal Medicine; Department of Biomedical Engineering, University of Iowa
Ultra-deep RNA sequencing (RNA-Seq) has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We developed MATS (multivariate analysis of transcript splicing), a flexible statistical framework for detecting differential alternative splicing patterns from RNA-Seq data. Using RNA-Seq, we investigated the complete spectrum of alternative splicing events that are regulated by the epithelium-specific splicing regulatory proteins ESRP1 and ESRP2. Our study provides a comprehensive picture of an extensive epithelial alternative splicing program in development and disease.
9:05 Design Considerations for Measuring Differential Gene Expression Using RNA Sequencing Data
Michelle Busby, Research Scientist in Marth Lab, Biology, Boston College
An RNA-Seq experiment’s ability to detect differential gene expression is based on the ability to distinguish true, biological differences between conditions from the variability that occurs in repeated measurements of the same condition. We will demonstrate how to quantify these sources of variability in existing data sets and show how this can be used to design experiments with adequate number of samples sequenced to a sufficient depth, both cost-effectively, and with reproducible results.
Selected Poster Presentations:
9:35 RNA-Seq and MicroRNA Expression Profiling Reveal Networks of RNA Interactions in Regenerating Dorsal Root Ganglion Neurons
Dario Motti, Ph.D., Postdoctoral Fellow, The Miami Project to Cure Paralysis, University of Miami, Miller School of Medicine
9:50 AssemEval: Illumina RNA-Seq Assembly Evaluation; Pitfalls and Opportunities for Non-Model Organisms
Shaadi F. Pooyaei Mehr, Research Scientist, American Museum of Natural History; The Graduate Center, City University of New York
10:05 NGS Data Analysis and Interpretation – From Raw Sequences to Clinical RelevanceMartin Seifert, Ph.D., CEO, GenomatixWhile NGS wet-lab protocols have become straightforward, data analysis remains difficult, with BioIT specialists often sacrificing ease-of-use for efficiency. Quality NGS data interpretations require intuitive tools and extensive background information. We will present an example of a Genomatix integrated solution for gene-fusion analysis that led to a clinically relevant result.
10:20 Coffee Break in the Exhibit Hall with Poster Viewing
11:00 RNA-Seq Analysis of Citrus Mature Leaves and Young Seedlings
Chunxian Chen, Ph.D., Associate, Citrus Genomics, University of Florida, IFAS, Citrus Research and Education Center
RNA-seq analysis of 454 data from citrus mature leaves and young seedlings were performed using DNASTAR’s QSeq. Among the genes aligned with at least one read in both experiments (R2 = 0.6377), 6515 genes showed expression change with 2-fold, 1308 with 4-fold, and 254 with 8-fold. Selected validation is underway.
11:30 Self-Training Algorithm for Splice Junction Detection Using RNA-seq
Jian Ma, Ph.D., Assistant Professor of Bioengineering, Assistant Professor of Biophysics and Computational Biology Affiliate, Assistant Professor of Computer Science Institute for Genomic Biology, University of Illinois at Urbana-Champaign
Accurately mapping RNA-seq reads to splice junctions is critically important for the analysis of alternative splicing and isoform construction. We developed a novel method, called TrueSight, utilizing semi-supervised learning to precisely identify splice junctions. Both simulation and real data evaluations showed that TrueSight achieved higher sensitivity and specificity than other tools. We believe this new tool will be highly useful to comprehensively study splice variants based on RNA-seq.
12:00 pm Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own
Analysis: Structural Variation
1:45 Chairperson’s Remarks
Kevin Davies, Ph.D., Editor-in-Chief, Bio-IT World
1:50 Rapid Detection and Characterization of Structural Variation in Hundreds of Human Genomes
Ira M. Hall, Ph.D., Assistant Professor, Department of Biochemistry and Molecular Genetics; Center for Public Health Genomics, University of Virginia
Structural variation (SV) is a major source of genomic diversity in mammals, but accurate detection of SV from NGS data remains a challenge. Here, we present a multi-sample version of our breakpoint detection algorithm, HYDRA, that can simultaneously detect and genotype SV breakpoints in hundreds to thousands of whole genome sequence datasets while using minimal RAM. We further outline a general framework for the characterization of both simple and complex SV in cancer genomes, and present results from an initial study of 64 tumor/normal pairs.
2:20 Discovery and Functional Impact of Structural Variation across 1000 Genomes
Ryan E. Mills, Ph.D., Assistant Professor, Department of Computational Medicine and Bioinformatics; Department of Human Genetics, University of Michigan Medical School
Genomic structural variants (SVs) are a poorly understood class of genetic variation. Through the 1000 Genomes Project, we have analyzed sequence data for 1,092 individuals across 14 populations and have precisely identified many variable regions that have been assessed for functional implications. We have additionally constructed integrated maps of genetic variation incorporating a subset of high quality SNP, INDEL, and deletion variants in these individuals onto phased haplotypes. These data sets will help further our understanding of the formation and prevalence of common and rare chromosomal rearrangements, informing future studies investigating the impact of such variation in human health and disease phenotypes.
2:50 Refreshment Break, Last Chance for Exhibit and Poster Viewing
3:15 Poster Awards Sponsored by Quantum
3:30 Combining Effects from Rare and Common Genetic Variants in Exome-Wide Association Study of Sequence Data
Hugues Aschard, Ph.D., Research Fellow, Department of Epidemiology, School of Public Health, Harvard University
Next-generation sequencing has the potential to revolutionize complex trait genetics by directly measuring common and rare genetic variants within a genome-wide context. Strategies that model the effects of both common and rare variants could enhance the power of identifying disease-associated genes. We evaluated various strategies for association of rare, common, or a combination of both rare and common variants on quantitative phenotypes in unrelated individuals using the Genetic Analysis Workshop 17 data.
4:00 Taking NGS into the Clinic
Gholson J. Lyon, M.D., Ph.D., Assistant Professor, Human Genetics, Cold Spring Harbor Laboratory; Research Scientist, Utah Foundation for Biomedical Research; Adjunct Assistant Professor, Psychiatry, New York University Child Study Center
For the implementation of genomic analysis in the clinic, it will be critically important to optimize and standardize pipelines with high sensitivity and specificity for variant calling. This includes having sufficient sequencing depth as well as high quality of sequencing data. One way to reduce false positives could be to use variants called by two or more variant calling approaches on one set of sequencing data. We are using various tools such as ANNOVAR and VAAST to prioritize variants from various families for follow-up in case-control and biological studies.
4:30 Tools for Processing Next-Generation Sequencing Data
Mark Gerstein, Ph.D., Albert L. Williams Professor of Biomedical Informatics, Molecular Biophysics and Biochemistry, Computer Science, Yale University - Download Podcast
A central problem for 21st century science is annotating the human genome and making this annotation useful for the interpretation of personal genomes. My talk will focus on annotating the bulk of the genome that does not code for canonical genes, concentrating on intergenic features such as TF binding sites and non-coding RNAs (ncRNAs) and structural variations and pseudogenes. Much of this work has been carried out within the ENCODE and 1000 Genomes projects.
5:15 Close of Conference
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