TCGC: Clinical Genome ConferenceTCGC: Clinical Genome ConferenceTCGC: Clinical Genome Conference

Pre-Conference Short Courses*

Monday, June 9

1:30 pm Short Course Registration

2:00-5:30 (SCa) Hadoop and High-Dimensional Data Mining in the Era of the Clinical Genome

The exponential decline in the cost of next generation sequencing is making it possible to discover rare genetic variants that may explain rare childhood diseases and cancer. To detect these rare variants requires the aggregation and analysis of genomics and clinical data at scale. Meanwhile, the advent of Hadoop, a programming framework that supports the processing of large data sets in a distributed computing environment, is driving the re-thinking and re-design of traditional data mining approaches. During this short course, we will briefly review key Hadoop concepts, and highlight how Hadoop makes novel data mining applications possible. Session speakers will share novel data mining strategies and approaches such as topological data analysis (TDA) and massively parallel database processing to perform a spectrum of genomic and clinical analyses.

What’s Hadoop and Does It Matter for My Genome Interpretation?

E Sasha PaegleE Sasha Paegle, Senior Business Development Manager, Life Sciences, Isilon Storage Division, EMC²

It’s no surprise to hear the word “Hadoop” when discussing the latest data mining strategies. But what is Hadoop? What are the advantages and disadvantages of Hadoop? During this short course we will introduce key Hadoop concepts, highlight where Hadoop is used in biomedical informatics and discuss how Hadoop makes it possible to introduce a new era of data mining applications for clinical genomics.

Genomics with Query: Re-Thinking Analytics for the Clinical Setting

Sarah AerniSarah Aerni, Ph.D., Senior Data Scientist, Pivotal

Effective analysis and understanding of patient genomes requires integration and processing of large and diverse patient datasets. We will cover our work in speeding up genomics pipelines by leveraging Pivotal’s Hadoop and MPP database technologies and conclude with our approaches in the analysis, integration and visualization of genomics and clinical data.

Unfolding the Shape of Clinical Genomics

Eithon CadagEithon Cadag, Ph.D., Principal Data Scientist, Ayasdi

Comprehensive exploration of modern genomic data is challenging; new technologies provide myriad and massive datasets. As these data find application in clinical domains, difficulty and urgency is multiplied. In this session, we will discuss broad application of topological data analysis (TDA), a growing subfield of mathematics, to address fundamental data problems in clinical genomics.


4:30 Short Course Registration

5:00-5:30 Shared Dinner Buffet

5:00-8:30 (SC1) Implementing Next-Generation Sequencing for Clinical Diagnostics

The rapid evolution of next-generation sequencing and the resulting move into routine clinical practice requires arguably as much skill in navigating through new unchartered territories of clinical testing as the sequence generation, bioinformatics and interpretation of variants. Significant challenges for clinical diagnostics include the rapid evolution of platforms, protocols, kits and reagents as well as genome analysis, interpretation and ethics. This short course provides practical information on implementing clinical sequencing, genomic data analysis and interpretation, ethics and proficiency testing.

Next Generation of Operational Challenges for Implementing Clinical Genomics in Genetics Laboratories

Mike M. MoradianMike M. Moradian, Ph.D., Director of Operations and Molecular Genetics Scientist, Kaiser Permanente Southern California Regional Genetics Laboratory

Clinical genomics has become a reality in today’s complex and dynamic molecular pathology laboratory setup, which requires detailed operational planning and oversight. Laboratories could face many challenges when deciding to implement clinical genomics, including preanalytical (e.g., appropriate test orders, testing platforms, staff training), analytical (e.g., assay validation, quality control, result generation) and post-analytical (e.g., bioinformatics expertise, result interpretation, preparation of an appropriate report for physician/clinic use). Of course, regulatory, compliance and billing/reimbursement issues are vital to the survival of such operations. A brief description of these operational challenges and possible solutions will be discussed.

Adventures in the Land of Clinical Sequencing: Implementation of Next-Generation Sequencing-Based Tests in a CAP-Regulated Laboratory

Avni SantaniAvni B. Santani, Ph.D., Assistant Professor, Clinical Pathology, Perelman School of Medicine, University of Pennsylvania and Scientific Director, Molecular Genetics Lab - Biography 

The learning objectives of this presentation include: 1) Outline the strategy geared towards practical implementation of a genomics program in a molecular diagnostic laboratory, 2) Review the development and validation of next-generation sequencing-based tests, including panels and whole-exome sequencing and 3) Examine the impact of implementing NGS-based tests on the clinical laboratory.

Automation of Sample Preparation for Clinical NGS: The Requirements and the Challenges for CLIA-Certified Clinical Laboratories

Martin SlawMartin Siaw, Ph.D., MB(ASCP)CM, Associate Scientific Director, Advanced Sequencing, Quest Diagnostics Nichols Institute

Sample preparation is an important component of any molecular testing that is being done in clinical laboratories. With the increasing use of NGS for clinical testing comes the need to process increasingly larger numbers of patient samples. Automation of sample preparation should be considered to be critical to the workflow of diagnostic tests involving the use of NGS. My presentation will focus on the requirements and challenges for CLIA-certified clinical laboratories.


Dinner Short Course*

Wednesday, June 11

6:00 pm-9:00 pm (SC2) Variant Analysis and Contribution to Disease

Advances in NGS have provided unprecedented opportunities to mine genetic data from individuals to populations. The subsequent identification of genetic variants which may be implicated in disease is an important step in linking sequence data with disease and provides new approaches to improve human health. In this course you will explore genetic data science, an emergent discipline that seeks to deliver better answers from the data so that patients and their physicians can determine informed healthcare decisions.

Using Chromatin Contacts to Create High-Contiguity Genome Assemblies

Joshua BurtonJoshua N. Burton, Research Scientist, Jay Shendure Laboratory, Genome Sciences, University of Washington - Biography 

To study genetics and population variation in any new species, we must first sequence its genome. But de novo genome assemblies created from short next-generation reads are highly fragmented. We have developed a method to create chromosome-scale scaffolds in de novo genome assemblies by exploiting chromatin interaction datasets. Our method is cost-effective, scalable and generalizable to any species.

Detecting Indels and Structural Variants in the Clinical Setting

Kai YeKai Ye, Ph.D., Research Assistant Professor, Genetics, The Genome Institute, Washington University - Biography 

High-performance analysis tools for short indels and complex structural variants are demanded for clinical applications. Currently medium-sized indels and structural variants are often missed by standard pipelines like GATK but often contribute to disease. Here, the best practices for detecting those missing variants are described.

Extensive Variation in Chromatin States Across Humans

Maya KasowskiMaya Kasowski, Ph.D., Research Scientist, Mike Snyder Laboratory, Genetics, Stanford University - Biography 

The majority of disease-associated variants lie outside protein-coding regions, suggesting a link between variation in regulatory regions and disease predisposition. We studied differences in chromatin states using five histone modifications, cohesin and CTCF in lymphoblastoid lines from 19 individuals of diverse ancestry. We found extensive signal variation in regulatory regions, which often switch between active and repressed states across individuals. Enhancer activity is particularly diverse among individuals, whereas gene expression remains relatively stable. Chromatin variability shows genetic inheritance in trios, correlates with genetic variation and population divergence and is associated with disruptions of transcription factor binding motifs.

* Separate Registration Required

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Translation of Genomic Data