Cambridge Healthtech Institute's Fourth Annual
Genomic Data Analysis: Sequencing's Strategic Step
March 18-20, 2013 | Hilton San Diego Resort, San Diego, CA
Day 1 | Day 2 | Day 3 | Download Brochure
Tuesday, March 19
8:00 Java and Jive Discussion Groups
Grab a cup of coffee and join a discussion group. These are moderated discussions with brainstorming and interactive problem solving, allowing conference participants from diverse backgrounds to exchange ideas, experiences, and develop future collaborations around a focused topic:
Table 1: Vetting Clinical Interpretations of SNP Changes
Terry Gaasterland, Ph.D., Director, Genomics, University of California, San Diego
• How do we translate observations from the literature into clinical reports?
• How do we assign levels of evidence to published research papers that associate genome variants with a disease?
• How do we convey to the end user (physician or person)?
• What are the implications of this evidence?
• How do we define and enforce good interpretation of genomes?
Table 2: Utilization of Next-Generation Sequencing for Clinical Microbiology Applications
Andrew Camilli, Ph.D., Associate Professor, Molecular Biology & Microbiology, Tufts University; Investigator, Howard Hughes Medical Institute
• What are the critical limitations for use in diagnosis of infections?
• What measures are being taken to overcome these limitations?
• What are clear-cut and feasible opportunities?
Table 3: Cloud, Desktop Computer, Tablet or Phone – Where is the Best Place to Do My Sequence Assembly and Analysis?
Tom Schwei, Vice President & General Manager, DNASTAR, Inc.
• What tasks or functions are best done on what device?
• What are the major challenges in using each device?
• How do I effectively collaborate with others and share and transfer data?
• What will things look like two years from now?
Table 4: Understanding Non-Canonical DNA Modifications
Terry Kelly, Ph.D., R&D Manager, Active Motif
• Current evidence describing 5hmc, 5caC, 5fc
• What techniques are used to study these modifications?
• Are these modifications stable states or part of a demethylation pathway?
• What is the clinical relevance of these non-canonical DNA modifications?
Table 5: RNA-seq for Gene Expression Profiling
Melanie Lehman, Ph.D., Research Fellow, Australian Prostate Cancer Research Centre, Queensland University of Technology
• What analysis methods are available for RNA-seq analysis and how do they compare?
• Should microarray and RNA-seq data be compared?
• Is the term ‘gene’ useful in RNA-seq analysis given the prevalence of alternative splice variants?
• How can RNA-seq data be used for both clinical application and biological discovery?
9:45 Chairperson’s Remarks
Sanjay Joshi, CTO, Life Sciences Isilon Storage Division, EMC Corporation
9:50 Prioritization of Cis-Regulatory Variants in Cancer Using Whole-Genome Sequencing and Integrative Analysis of ChIP-Seq and Chromatin-State Data
Hamid Bolouri, Ph.D., Research Member, Human Biology Division, Fred Hutchinson Cancer Research Center
Most pediatric Acute Myeoloid Leukemias are caused by genomic aberrations that dysregulate gene expression. I will describe a collaboration with the laboratory of Soheil Meshinchi (FHCRC) and the NCI TARGET project, in which we are integrating whole-genome sequences with expression, ChIP-seq and chromatin-state data to identify and prioritize novel cis-regulatory aberrations.
10:25 Clinical Genomics: A Storage Perspective
Sanjay Joshi, CTO, Life Sciences Isilon Storage Division, EMC Corporation
The major informatics criteria for the implementation of Clinical Genomics are: sample and lab management, sequence validation and medical records integration. Storage is the latent criteria which is critical to all of the above. We will briefly discuss current trends and best practices for Clinical Genomics.
10:40 Coffee Break in the Exhibit Hall with Poster Viewing
11:15 Normalization of ChIP-Seq Data
Kun Liang, Ph.D., Assistant Professor, Department of Statistics and Actuarial Science, University of Waterloo
ChIP-Seq has become the primary tool for identifying genome-wide protein-DNA interactions, including transcription factor binding and histone modifications. In this talk, we will first present our results on normalization of ChIP-Seq data with respect to control. Then we will delve into quality control issues of ChIP-Seq data, for example, diagnostic of binding signal quality and artifact filtering.
11:50 Motif Discovery through Memetic Computing
Chengpeng (Charlie) Bi, Ph.D., Laboratory of Bioinformatics and Intelligent Computing, Division of Clinical Pharmacology Children’s Mercy Hospitals and Clinics
This talk will introduce a new hybrid motif discovery algorithm by memetic computing. A memetic algorithm is briefly described, and we will show how it is incorporated to perform multiple sequence local alignment. Several examples including ChIP-Seq data are used to demonstrate its excellence.
12:25 pm Close of Session
12:30 Co-Luncheon Presentation
HPC Solution for Next Generation Sequencing
Yinhe Cheng, Ph.D., Life Sciences Software Engineering Consultant, Computational Biologist, IBM
Scott Markel, Ph.D., Principal Bioinformatics Architect, AccelrysThe rapid advance of sequencing technology and falling cost is driving the use of NGS in a great variety of domains. While processing raw data from a sequencer and converting it to usable genomic insight is a formidable task on its own, large scale genomic comparisons require unprecedented computational power, data storage and scalable performance. IBM’s scalable high performance computing solution with Accelrys' NGS Collection addresses these needs.
2:00 Chairperson’s Remarks
Alexander Kaplun, Ph.D., Field Applications Scientist, BIOBASE Corporation
2:05 Speaker to be Announced
2:40 Effect Size Distribution for Mapping Genetic Determinants of Diseases
Dmitri Zaykin, Ph.D. Principal Investigator, Biostatistics Branch, National Institute of Environmental Health Sciences, NIH
In large scale association studies, only a small proportion of variants contribute substantially to disease susceptibility. Owning to this sparseness of effects, the proportion of genuine findings among top hits of a study is difficult to estimate. A crucial step in ascertainment of the proportion of genuine and spurious findings among top hits is an accurate representation of the effect size distribution among all variants examined in a study. We will describe methods for characterizing the effect size distribution with applications to data analysis and design of genomic studies.
3:15 Genome TraxTM: Whole Genome Variant Analysis for Diagnostics and Medical Research
Alexander Kaplun, Ph.D., Field Applications Scientist, BIOBASE Corporation
Interpretation of human whole genome sequence data is emerging as the fundamental bioinformatics challenge of the 21st century. We describe Genome TraxTM, an essential annotation source for identifying disease-related variants and for understanding the effect of variants in a medical context, and demonstrate how relevant variants can be discovered in a sample patient.
3:30 Refreshment Break in the Exhibit Hall with Poster Viewing
4:00 FunciSNP: A R/Bioconductor Tool Integrating Functional Non-Coding Data Sets with Genetic Association Studies to Identify Candidate Regulatory SNPs
Gerhard Coetzee, Ph.D., Professor, Urology, Microbiology and Preventive Medicine, Co-Leader, Cancer Center GU Program, Norris Comprehensive Cancer Center; University of Southern California
Because a large proportion of tagSNPs have been identified within non-coding regions of the genome, distinguishing functional from non-functional SNPs has been a great challenge. A strategy was recently proposed that prioritizes surrogate SNPs based on non-coding chromatin and epigenomic mapping techniques that have become feasible with the advent of massively parallel sequencing. Here, we introduce an R/Bioconductor software package that enables the identification of candidate functional SNPs by integrating information from tagSNP locations, lists of linked SNPs from the 1000 genomes project and locations of chromatinfeatures which may have functional significance.
4:35 Combining Gene Regulatory Networks with Genome Wide Association Studies
Sergey V. Nuzhdin, Ph.D., Professor, Molecular Computational Biology, University Southern California
Understanding how metabolic reactions, cell signaling, and developmental pathways translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular biology approach directly ties gene function to phenotype through gene regulatory networks (GRNs). Using natural variation in allele-specific expression, GWAS and GRN approaches can be merged into a single framework via structural equation modeling (SEM). This approach leverages the myriad of polymorphisms in natural populations to elucidate and quantitate the molecular pathways that underlie phenotypic variation. The SEM framework can be used to quantitate a GRN, evaluate its consistency across environments or sexes, identify the differences in GRNs between species, and annotate GRNs de novo in non-model organisms.
5:10 Close of Day
5:30-8:30 Dinner Short Courses
Day 1 | Day 2 | Day 3 | Download Brochure
*IBM and the IBM logo are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide.