Leaders in Pharmaceutical Business Intelligence (LPBI) Group

18th Annual 2019 BioIT, Conference & Expo, April 16-18, 2019, Boston, Seaport World Trade Center, Track 5 Next-Gen Sequencing Informatics – Advances in Large-Scale Computing

 

https://www.bio-itworldexpo.com/programs

https://www.bio-itworldexpo.com/next-gen-sequencing-informatics

 

 

Download Brochure | Workshops

TUESDAY, APRIL 16

2:00 – 6:30 Main Conference Registration Open

 

4:00 PLENARY KEYNOTE SESSION
Amphitheater

5:00 – 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing

 

WEDNESDAY, APRIL 17

7:30 am Registration Open and Morning Coffee

8:00 PLENARY KEYNOTE SESSION
Amphitheater

9:45 Coffee Break in the Exhibit Hall with Poster Viewing

 

CURRENT AND EMERGING TECHNOLOGIES
Waterfront 3

10:50 Chairperson’s Remarks

David LaBrosse, Director, Genomics, Research, Life Sciences & Healthcare, NetApp

11:00 Long Read Sequencing

Justin Zook, PhD, Researcher, National Institute of Standards and Technology

11:20 NovoGraph: Loading 7 Human Genomes into Graphs

Evan Biederstedt, Computational Biologist, Memorial Sloan Kettering Cancer Center

11:40 Building a Usable Human Pangenome: A Human Pangenomics Hackathon Run by NCBI at UCSC

Ben Busby, PhD, Scientific Lead, NCBI Hackathons Group, National Center for Biotechnology Information (NCBI)

12:00 pm Co-Presentation: Faster Genomic Data

Michael Hultner, PhD, Senior Vice President, Strategy; General Manager, US Operations, PetaGene

David LaBrosse, Director, Genomics, Research, Life Sciences & Healthcare, NetApp

Genetic testing demand is driving up the volume of genomic data that must be processed, analyzed, and stored. Gigabyte-scale genome sample files and terabyte- to petabyte-scale cohort data sets must be moved from data generation to processing to analysis sites, historically a slow, arduous process. NetApp and PetaGene will describe compression and data transfer technologies that overcome I/O bottlenecks to accelerate the movement of genomic data and reduce the time to process and analyze it.

12:30 Session Break

12:40 Luncheon Presentation I: Deep Phenotypic and Genomic Analysis of UK Biobank Data on the WuXi NextCODE Platform

Saliha Yilmaz, PhD, Research Geneticist, WuXi NextCODE

The increasing size and complexity of genetic and phenotypic data to include hundreds of thousands of participants poses a significant challenge for data storage and analysis. We demonstrate use of the GOR database and query language underlying our platform to mine UK Biobank and other datasets for efficient phenotype selection, GWAS and PheWAS, and to archive and query the results.

1:10 NEW: Luncheon Co-Presentation II: Optimizing Drug Discovery and Development with Data-Driven Insights

Christian Frech, PhD, Associate Director, Scientific Operations, Seven Bridges

Serhat Tetikol, Research & Development Engineer, Seven Bridges

1:40 Session Break

DATA VISUALIZATION, EXPLORATION & ANALYSIS
Waterfront 3

1:50 Chairperson’s Remarks

Jeffrey Rosenfeld, PhD, Manager of the Biomedical Informatics Shared Resource and Assistant Professor of Pathology, Rutgers Cancer Institute of NJ

1:55 AbbVie’s Target and Genomics Compilation (ATGC): A Target Knowledge Platform

Rishi Gupta, PhD, Senior Research Scientist, Information Research, AbbVie, Inc.

Author: Anne-Sophie Barthelet, Scientific Developer, Discngine

ATGC is a web-based platform that allows AbbVie scientists to gather relevant information to make accurate decisions on target ID, target validation, biomarker selection and drug discovery. This platform provides in-depth information on several key pieces of information such as gene expression, RNA expression, protein expression, mouse knockout studies, etc. for each target. This talk focuses on key aspects of this application including application architecture, currently available tool sets and how various pieces of information are provided to the user.

2:25 Self Service Data Visualization and Exploration at Genentech Research

Kiran Mukhyala, Senior Software Engineer, Bioinformatics and Computational Biology, Genentech Research and Early Development

Genomic data requires specialized infrastructure to enable data exploration and analysis at scale. We built an integrated, modular, end-to-end gene expression analysis platform implementing data import, storage, processing, analysis and visualization. The multi-layered architecture of the platform supports general, high-level applications for self-service analytics, as well as infrastructure for prototyping, incubating and integrating scientist-driven innovations. The platform coexists with other in-house and commercial software to provide a wide range of genomic data analysis and visualization options for Research scientists.

2:55 Exploring and Visualizing Single-cell RNA Sequencing Data

Michael DeRan, PhD, Scientific Consultant, Diamond Age Data Science

Recent advances in single-cell RNA sequencing (scRNA-seq) technology have made this powerful method accessible to many researchers, but have not brought with them a clear, simple workflow for data analysis. As the number of scRNA-seq datasets has increased, so too has the number of analysis tools available; for those looking to perform their first scRNA-seq analysis the range of options can seem daunting. In working with our clients, I have had the opportunity to apply many different tools to scRNA-seq data from a variety of tissues and organisms. I have used this experience to select a set of tools that are flexible and suitable to many common scRNA-seq analysis tasks. In this talk I will introduce popular tools and methods for identifying cell populations, assessing differential expression and visualizing biological processes. I will discuss common pitfalls encountered in analyzing this data and make recommendations that anyone can use in their own analysis.

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing, Meet the Experts: Bio-IT World Editorial Team, and Book Signing with Joseph Kvedar, MD, Author, The Internet of Healthy Things℠ (Book will be available for purchase onsite)

NGS APPROACHES FOR CANCER
Waterfront 3

4:00 Comparison of Different Approaches for Clinical Cancer Sequencing

Jeffrey Rosenfeld, PhD, Manager of the Biomedical Informatics Shared Resource and Assistant Professor of Pathology, Rutgers Cancer Institute of NJ

The sequencing of tumors is important for guiding the treatment of cancer patients. While it is agreed that there is a need to perform sequencing of the tumor, there are a wide variety of approaches ranging from paired whole genome tumor-normal sequencing to tumor-only small panel sequencing with many intermediate possibilities. Each of the approaches has a different cost and associated benefit. I will present a comparison of different methods and their efficacy for guiding cancer treatment.

4:30 Integrated NGS Analysis to Accelerate Disease Understanding for Drug Discovery

Helen Li, Director- Research IT – Biologics & Informatics, Eli Lilly and Company

5:00 Identification of Cancer Biomarker Genes

Maryam Nazarieh, PhD, Postdoctoral Researcher, Center for Bioinformatics, Universität des Saarlandes, Saarbrücken, Germany

Identification of biomarker genes plays a crucial role in disease detection and treatment. Computational approaches enhance the insights derived from experiments and reduce the efforts of biologists and experimentalists to identify biomarker genes which play key roles in complex diseases. This is essentially achieved through prioritizing a set of genes with certain attributes (1). Here, I propose a set of transcription factors that make the largest strongly connected component of the pluripotency network in embryonic stem cells as the global regulators that control differentiation process determining cell fate. This component can be controlled by a set of master regulatory genes.  The regulatory mechanisms underlying stem cells inspired us to formulate the problem where a set of master regulatory genes in regulatory networks is identified with two combinatorial optimization problems namely as minimum dominating set and minimum connected dominating set in weakly and strongly connected components. The developed methods were applied to regulatory cancer networks to identify disease-associated genes and anti-cancer drug targets in breast cancer and hepatocellular carcinoma.  As not all the nodes in the solutions are critical, a prioritization method was developed named TopControl to rank a set of candidate genes which relate to a certain disease based on systematic analysis of the genes that are differentially expressed in tumor and normal conditions. To this purpose, the NGS data were utilized taken from The Cancer Genome Atlas for matched tumor and normal samples of liver hepatocellular carcinoma (LIHC) and breast invasive carcinoma (BRCA) datasets. Moreover, the topological features were demonstrated in regulatory networks surrounding differentially expressed genes that were highly consistent in terms of using the output of several analysis tools. We present several web servers and software packages that are publicly available at no cost. The Cytoscape plugin of minimum connected dominating set identifies a set of key regulatory genes in a user provided regulatory network based on a heuristic approach. The ILP formulations of minimum dominating set and minimum connected dominating set return the optimal solutions for the aforementioned problems. Our source code is publicly available. The web servers TFmiR and TFmiR2 construct disease-, tissue-, process-specific networks for the sets of deregulated genes and miRNAs provided by a user. They highlight topological hotspots and offer detection of three- and four-node FFL motifs as a separate web service for both organisms mouse and human. 1) Maryam Nazarieh, Understanding regulatory mechanisms underlying stem cells helps to identify cancer biomarkers. Ph.D. thesis, Saarland University, Saarbrücken, Germany (2018).

5:30 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing