NOV 15, 2018 9:00 AM PST

Best Practice Strategies and Tools for Scaling Clinical Bioinformatics

Speaker
  • Founder and President, BigHead Analytics Group
    Biography
      Elaine Gee, PhD, is the founder and principal consultant of BigHead Analytics Group with industry expertise in clinical bioinformatics and scaling compute platforms for clinical genomic testing. Previously Dr. Gee was the Director of Bioinformatics at ARUP Laboratories, where she supported the bioinformatics and compute infrastructure for clinical next-generation sequencing-based assays. At ARUP she led her team to scale bioinformatics by creating an elastic cloud-based compute infrastructure in AWS that executed standardized bioinformatics pipelines (a.k.a. "Pipey", see https://www.genomeweb.com/clinical-lab-management/arup-launches-cloud-based-ngs-analytics-platform-massively-larger-scale#). This work included tuning somatic and germline pipelines by variant class, developing a central genomic variant datastore to house discrete genomic data, and automating and scaling the infrastructure. Additionally her work included incorporation of unique molecular identifiers to enable low frequency variant detection and creation of tools to optimize NGS target design in low complexity genomic regions. Dr. Gee pairs analytic rigor with automation and design architecture to scale bioinformatics solutions and accelerate the pipeline development life cycle. Her background includes cross-disciplinary experience in machine learning, signal processing, molecular dynamics modeling, and instrument integration and control. Dr. Gee earned her Ph.D. in biophysics from Harvard University and a B.S. in physics from the California Institute of Technology. She currently serves as the informatics subdivision lead on the AMP Global 2019 Organizing Committee and works at Medtronic as a Principal Machine Learning Algorithm Development Engineer.

    Abstract

    Clinical testing with next generation sequencing requires a complex bioinformatics pipeline to process raw DNA sequence into  interpretable variants for medical reporting. With sequencing costs dropping and medical testing trending towards larger targeted panels, exomes, and even genomes, bioinformatics pipelines are under a pressure test to perform at scale. This talk will provide a high level overview of best practice recommendations for leveraging team resources to build scalable bioinformatics solutions that can seamlessly scale from small to large sample volumes. This talk will highlight best practice strategies and automation tools to efficiently build and validate clinical bioinformatics pipelines to handle large sample volumes without sacrificing quality and discuss solutions for monitoring performance of a scalable bioinformatics ecosystem in production.


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