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9:30 Chairperson’s Remarks
Mao Mao, Ph.D., M.D., Research Fellow, Oncology Research, Pfizer Global Research and Development
9:35 Combined Analyses of Cancer Genomes and Transcriptomes
Zemin Zhang, Ph.D., Principal Scientist, Department of Bioinformatics and Computational Biology, Genentech, Inc.
Next-generation sequencing technologies have greatly reduced the barrier for whole genome and transcriptome analyses of human cancer samples. Using the Complete Genomics platform for whole genome sequencing and the Illumina platform for RNA Seq, we have analyzed multiple lung and liver tumor samples and cell lines. We highlight a number of key findings from such combined analyses of cancer genomes and transcriptomes.
10:05 Cancer Genome Sequencing for Drug Development
Mao Mao, Ph.D., M.D., Research Fellow, Oncology Research, Pfizer Global Research and Development
Next-generation sequencing has being used to character cancer at single base pair resolution. Key mutations are being identified as potential targets and biomarkers for drug development. In this presentation, examples of genome sequencing on large cohorts of clinically well-annotated tumors will be discussed that highlight advances in understanding key genetic drivers of cancers and how to utilize this information to enable drug development. The work of the Asian Cancer Research Group consortium will be introduced, together with its focus on Asia’s most prevalent cancers.
Sponsored by
 10:35 What Can We Learn from a Public Repository of More Than 60 Complete Human Genome Sequences?
Srinka Ghosh, Ph.D, Senior Product Marketing Manager, Complete Genomics Incorporated
A significant challenge of identifying rare variants in complex diseases is discriminating causal from neutral ones. An unbiased look across non-tumor genomes can refine our estimate of true novelty. Complete Genomics recently released 69 complete genomes representing 9 different populations at 47x(mean) coverage. Here, both alleles are called on 97.1% (median) of the genome and SNP concordance with the 1000 Genomes Project is 99.7%. We will highlight the power of the normal in understanding the novel.
10:50 Networking Refreshment Break in the Exhibit Hall with Poster Viewing
11:30 Isoform-Level Gene Expression Signatures in Brain Development and Glioblastoma
Ramana V. Davuluri, Ph.D., Associate Director, The Wistar Institute Center for Systems and Computational Biology
Despite our growing knowledge that many mammalian genes generate multiple transcript variants, which may encode functionally distinct protein isoforms, the transcriptomes of various tissues, their developmental stages and disease conditions are mostly undefined. We have built genome-wide inventory of non-coding and protein-coding transcripts and their variants (transcriptome), their promoters (promoterome) and histone modification states (epigenome) for developing and adult brain using integrative massive-parallel sequencing and bioinformatics approach. By integrative analysis of these NextGen sequencing data-sets with TCGA data, we have identified developmentally regulated isoforms and isoform-level gene expression signatures in glioblastoma multiforme, the most common and most aggressive type of primary brain tumor in humans. I will discuss the bioinformatics methods and associated software used in this study.
12:00 pm Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own
1:30 Chairperson’s Remarks
Toumy Guettouche, Ph.D., Director, Oncogenomics Core Facility (OCF), the Sylvester Cancer Center, University of Miami School of Medicine
1:35 MicroRNA Profiling of Clear Cell Renal Cell Carcinoma by Whole-Genome Small RNA Deep Sequencing of Paired FFPE Tissue Specimens
Huiqing Wu, M.D., Staff Pathologist and Assistant Professor, Pathology, City of Hope National Medical Center and Beckman Research Institute
We performed whole-genome small RNA sequencing analysis using a benign and RCC specimen set and have successfully profiled the miRNA expression. Studies performed on paired frozen and formalin fixed paraffin-embedded (FFPE) specimens showed very similar results. Moreover, a comparison study of microarray, deep-sequencing and RT-PCR methodologies also showed a high correlation among the three technologies. This study has demonstrated that FFPE specimens can be used reliably for miRNA deep-sequencing analysis, making future large-scale clinical cohort/trial-based studies possible.
2:05 High-Throughput Detection of Inherited Mutations for Breast and Ovarian Cancer Using Genomic Capture and Massively Parallel Sequencing
Tom Walsh, Ph.D., Research Assistant Professor, Medical Genetics, University of Washington
Inherited loss-of-function mutations in the tumor suppressor genes BRCA1, BRCA2, and multiple other genes, predispose to high risks of breast and/or ovarian cancer. Genetic testing for BRCA1 and BRCA2 mutations has become an integral part of clinical practice, but testing is generally limited to these two genes and to women with severe family histories of breast or ovarian cancer. We have developed a comprehensive DNA capture and sequencing approach that enables widespread genetic testing and personalized risk assessment for breast and ovarian cancer.
2:35 Technology Presentation (Sponsorship Opportunity Available)
2:50 Networking Refreshment Break in the Exhibit Hall with Poster Viewing
3:30 Identifying Key Regulators Controlling the Switch of Self-Renewal and Differentiation in EML Multipotential Hematopoietic Precursor Cells
Jiaqian Wu, Ph.D., Postdoctoral Research Fellow, Genetics, Stanford University School of Medicine
Understanding the mechanisms controlling the decision of self-renewal and differentiation of hematopoietic precursor cell is important for understanding hematological malignancy and developing therapy. Using EML (Erythroid, Myeloid, and Lymphocytic) multipotential hematopoietic precursor cell as a model and a systems-based approach employing RNA-Seq, ChIP-Seq and regulatory network analysis, we identified key regulators controlling the switch in early hematopoietic precursor self-renewal and differentiation. These studies in EML cells give insights to the fundamental properties of autonomous asymmetric division available for stem cells and intermediate-stage cancer precursor cells in general.
4:00 Identifying Structural Variants in Clinical NGS Data
Haley Abel, Ph.D., Research Associate, Genetic Epidemiology, University of Utah
Structural variations including translocations, insertions, and deletions are common genetic events in cancer pathogenesis, and many have defined prognostic value. Identification of such events by next generation sequencing in the clinical setting is difficult as whole genome methods have high false positive rates, requiring time consuming and expensive validation. Using hybrid capture enrichment and a variety of software tools we exploit off-target coverage to find prognostically significant translocations in acute myeloid leukemia with a relatively low false positive rate.
4:30 Panel Discussion with Afternoon Speakers
5:00 Close of Conference
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9:30 Chairperson’s Remarks
Weiwen Zhang, Ph.D., Associate Professor, Research, Biodesign Institute, Arizona State University
9:35 Whole-Transcriptome Amplification and Analysis of Microbial Communities from Natural Environments
Weiwen Zhang, Ph.D., Associate Professor, Research, Biodesign Institute, Arizona State University
Limitation in sample quality and quantity is one of the big obstacles for applying metatranscriptomic technologies to explore gene expression and functionality of microbial communities in natural environments. We evaluated several amplification methods were for whole-transcriptome amplification of deep-sea microbial samples, which are of low cell density and high impurity. The best amplification method was identified and incorporated into a complete protocol to isolate and amplify deep-sea microbial sample for the metatranscriptomic analysis.
10:05 Viral Pathogen Discovery and Characterization by Deep Sequencing
Charles Chiu, M.D., Ph.D., Assistant Professor, Laboratory Medicine and Medicine, Infectious Diseases, University of California, San Francisco
Traditional approaches to identify novel pathogens are limited in sensitivity and scope. We still fail to diagnose approximately 25% of cases of respiratory infections and more than 50% of diarrheal disease and encephalitis., and threats from novel outbreak viruses such as 2009 pandemic influenza H1N1 are continually on the horizon. New strategies are sorely needed to increase the breadth, speed, and “throughput” of pathogen detection. Here we will discuss the applications and challenges of implementing next-generation sequencing (NGS) technologies for viral pathogen discovery.
10:35 Technology Presentation (Sponsorship Opportunity Available)
10:50 Networking Refreshment Break in the Exhibit Hall with Poster Viewing
11:30 Detection of HIV Dual Infection by Ultra-Deep Sequencing
Mary Pacold, Ph.D., Division of Infectious Diseases, Stanford University Medical Center
To investigate the potential of ultra-deep 454 sequencing to screen for HIV dual infection, we compared it to the current standard of single genome sequencing. In our study set, ultra-deep sequencing captured more intra-host viral diversity than single genome sequencing at approximately 40% of the cost and 20% of the laboratory and analysis time.
12:00 pm Luncheon Presentation or Lunch on Your Own
1:30 Chairperson’s Remarks
1:35 Novel Viruses Encountered in Deep Sequencing Data
Maher Al Rwahnih, Ph.D., Project Scientist, Foundation Plant Services, University of California, Davis
Total genomic deep sequencing data from an agricultural plant has revealed sequences of pathogens distantly related to known viruses, which were infecting the subject plant. Analysis of the information resulted in the description of previously unknown viral pathogens, from a range of virus families.
2:05 Deep Sequencing for Antiviral Drug Resistance Mutations
Robert W. Shafer, M.D., Associate Professor of Medicine, Infectious Diseases, Stanford University Medical Center
HIV, HBV, and HCV are the most prevalent deadly chronic viral diseases. Although HIV is a retrovirus, HBV is a double stranded DNA virus, and HCV is a single-stranded RNA virus, each are characterized by high mutation rates and frequent drug resistance. Viral deep sequencing detects drug resistant viral variants before they can be detected by standard sequencing methods. However, viral deep sequencing also uncovers a complex world of virus evolution that requires novel analytic approaches.
Sponsored by
 2:35 RTG Software: Implementing A Comprehensive Sequence Analysis Pipeline for Metagenomics
Brian Hilbush, Ph.D., Technical Director, Real Time Genomics
Deep sequencing with high-throughput technologies promises to revolutionize metagenomic investigations, but the short reads create significant challenges in analysis. Real Time Genomics presents a solution with clustering, nucleotide and protein search.
2:50 Networking Refreshment Break in the Exhibit Hall with Poster Viewing
3:30 Computational Methods for Functional Metagenomics
Curtis Huttenhower, Ph.D., Assistant Professor, Department of Biostatistics, Harvard School of Public Health
One of the key biological challenges addressed by shotgun metagenomic data is exposing the genes, pathways, and metabolism at work in microbial communities. Mining hundreds of metagenomic samples to reconstruct these pathways is, conversely, a substantial computational challenge. I will discuss our work to enable scalable metabolic and functional reconstruction in the human microbiome. The resulting genes and pathways can be used in tandem with organismal information from 16S data to discover metagenomic biomarkers describing the functionality characteristic of specific body sites, diseases such as Crohn’s or colitis, and individual microbial communities.
4:00 Human Microbiome Visualization Using 3D Technology
Jason H. Moore, Ph.D. Professor of Genetics and Community and Family Medicine; Director, Institute for Quantitative Biomedical Sciences, Dartmouth Medical School
Metagenomics is by nature data and information intensive. Our working hypothesis is that 3D visualization methods will facilitate the exploration, analysis and interpretation of large-scale omics data and research results. We describe here the use of a 3D video game development engine for the visual exploration and analysis of human microbiome data from guts of premature infants.
4:30 Panel Discussion with Afternoon Speakers
5:00 Close of Conference
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