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Sequencing Data Analysis and Interpretation Header 

The explosion of second-generation sequencing platforms and the emergence of third-generation sequencers have resulted in the extraordinary output of genomic data. Thus, the ability to analyze and interpret the data has rapidly become the rate limiting step. Cambridge Healthtech Institute’s Sequencing Data Analysis and Interpretation combines unique perspectives from biological researchers, biostatisticians, and software developers creating a common ground on which to explore how best to manage, analyze, and interpret datasets. This team of scientists will reference case studies as they provide a forum to discuss recent trends and issues in the arena of sequencing data, sequencing data analysis, and interpretation.

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Wednesday, March 16 


7:30 am Conference Registration

8:00 Java and Jive Breakout Discussion Groups

These focused groups are designed for conference attendees to discuss important and interesting topics related to sequencing and genomic tools. 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. Complimentary coffee is included.

Please click here for a complete listing of Breakout Discussion Groups.

 

9:00 Close of Breakout Discussion Groups


 

ArrowPlenary Keynote Session: The Personal Impact of Sequencing from Patient to Population 

Podcast9:25 It Takes a Village

Hugh RienhoffHugh Rienhoff, M.D., Director, MyDaughtersDNA.org - Biography 

Next-generation sequencing has made possible the study of rare genetic disease where traditional linkage analysis is not possible because of the rarity of the disorder. This description fits more than 2000 familial diseases. But the discovery of the genetic cause of rare disease remains a formidable challenge demanding the talents of many to generate the necessary data that rises to the level of proof of causality. 

10:05 Data-Driven Personalized Medicine

Atul ButteAtul Butte, M.D., Ph.D., Assistant Professor, Pediatrics, Medicine, Computer Science, Stanford University, Lucille Packard Children’s Hospital - Biography 

Dr. Butte builds and applies tools that convert more than 15 billion points of molecular, clinical, and epidemiological data measured by researchers and clinicians over the past decade into insights into diagnostic and therapeutic potential. Dr. Butte, a bioinformatician and pediatric endocrinologist, will highlight his recent work on the first clinical evaluation of a patient presenting with a personal genome. 

10:45 Networking Coffee Break in Exhibit Hall with Poster Viewing

11:15 Human Genome Sequencing: Relevance of Defining the Outer Limits of Human Diversity for Global Health

Vanessa HayesVanessa Hayes, Ph.D., Professor, Human Genomics, J. Craig Venter Institute - Biography 

The African continent, birthplace of all modern man, home to a third of the world’s ethnic diversity, and epicenter for many globally significant diseases, has been poorly characterized in genetic terms. With a focus on recently diverged populations, non-migrant Africans have largely been excluded from the era of genomics and therefore disease association studies. Data will be presented from the first indigenous genome sequencing and how defining indigenous genome diversity will advance genotype-phenotype correlations of global significance.

11:55 Close of Session

12:15 pm Luncheon PresentationSponsored by
Agilent Technologies

Understanding Glaucoma through Genome-Wide Targeted Exome Re-Sequencing  

Terry Gaasterland, Ph.D., Professor, University of San Diego, CaliforniaPrimary open angle glaucoma is a complex disease with genetic foundations, but to date, with no clear causal gene variants. We are sequencing exomes from ~300 cases to compare with 500+ random controls. This talk will present results from Phase 1 in which we evaluated alternative methods for capture, library preparation, and analysis, and established standards for the full project.    


Sponsored by
BGI 
12:50 Luncheon Presentation
Partnering for Multi-omics Excellence
Joyce Peng, Ph.D., Marketing Director, BGI AmericasWith the diverse analytical technologies now available, we can focus the full spectrum of multi-omics methods on the important research questions that impact agriculture and human health. BGI is deeply committed to supporting multi-omics research by providing our deep sequencing, analytical, and cloud computing resources to our collaborators and clients. Here we present projects that make use of leading-edge genomic, transcriptomic, epigenomic, proteomic, metagenomic, and single-cell sequencing methods, with special focus on research relevant to human disease and agriculture.

 

ChIP-Seq 

2:15 Chairperson’s Remarks

Thomas Schwei, Vice President and General Manager, DNASTAR, Inc.

2:20 ChIP-PaM: An Algorithm for Identifying Transcription Factor Targets through ChIP-Seq Data

Song Wu, Ph.D., Assistant Member, Biostatistics, St. Jude Children’s Research Hospital

ChIP-PaM is an algorithm to detect transcription factor (TF) binding targets by capitalizing on three lines of evidences from a specific TF-DNA binding pattern: 1) The peak count of sequencing tags within a genomic region, 2) pattern matching of a specific tag count distribution, and 3) motif search along the genome. A novel data-based two-step eFDR procedure is proposed to integrate the three lines of evidences to determine significantly enriched regions. In a comparison with other existing methods, ChIP-PaM provides more accurate binding site discovery while maintaining comparable statistical power.

2:50 An Integrated Pipeline for Analyzing ChIP-Seq Transcription Factor Datasets

Gordon Robertson, Ph.D., Staff Scientist, Canada’s Michael Smith Genome Sciences Centre, British Columbia Cancer Agency

We have developed an R/Bioconductor analysis pipeline for ChIP-seq data for transcription factors. The pipeline addresses data import, probabilistic binding event identification, sequence motif analysis, visualization, and data export. Datasets with tens of thousands of enriched regions can be efficiently processed on a standard multicore computer. We are extending the probabilistic methods to address whole-genome, nucleosome-based short-read data types.

3:20 BiNGS!SL-Seq: Computational Analytical Pipeline to Analyze and Interpret Genome-Wide Synthetic Lethal Screen

Aik Choon Tan, Ph.D., Assistant Professor, Bioinformatics, Medical Oncology, University of Colorado Denver School of Medicine

We have developed a bioinformatics analysis pipeline (BiNGS!SL-seq) to analyze and interpret the massive sequencing data generated from the SL-seq. In this talk, I will describe the tools and methods implemented in this pipeline. I will also discuss lessons learned from developing this computational workflow.

3:50 Networking Refreshment Break in Exhibit Hall with Poster Viewing

 

4:30 RNA Binding Networks Determined by CLIP-SeqGene Yeo, Ph.D., Assistant Professor, Cellular and Molecular Medicine, Institute for Genomic Medicine, University of California, San DiegoPost-transcriptional regulation of gene expression is an area of increasing importance in our understanding of development and disease.  RNA binding proteins (RBPs) are key determinants of the proper processing of RNA and ensure that the correct RNA species is generated at the right time and place. We have developed a powerful multipronged approach using computational modeling, biochemistry, molecular biology, and cell biology to identify the functional RNA elements recognized by RBPs in a genome-wide fashion. I will discuss how these methods are elucidating RNA regulatory networks in the nervous system and in human cells, pertinent to neurological diseases.
 

5:00 Analysis of High-Resolution and Genome-Scale DNA Methylation Data

Elena Harris, Ph.D., Research Associate, Molecular and Computational Biology, University of Southern California

Shotgun bisulfite sequencing has revolutionized the study of DNA methylation. We will discuss computational methods for extracting the most information from genome-wide single-CpG DNA methylation data, including identification of hypomethylated regions, regions showing partial methylation, differential methylation between two conditions, and regions of allelic methylation.

5:30 Evening Reception

6:30 Close of Day



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