Meet Inspiring Speakers and Experts at our 3000+ Global Conference Series Events with over 1000+ Conferences, 1000+ Symposiums
and 1000+ Workshops on Medical, Pharma, Engineering, Science, Technology and Business.

Explore and learn more about Conference Series : World's leading Event Organizer

Back

Xiaowu Gai

Xiaowu Gai

Director of Bioinformatics, USA

Title: Implementation of A Hybrid Informatics Solution for A Large Personalized Medicine Program

Biography

Biography: Xiaowu Gai

Abstract

The bioinformatics needs of a large personalized or precision medicine program exemplify the 3Vs of big data challenges: volume, variety and velocity. At the Center for Personalized Medicine at the Children's Hospital Los Angeles, we offer a variety of genomics-based somatic and germline genetic tests (single-gene, gene-panel, whole-exome, whole-genome, chromosomal microarray) for a large number of pediatric patients with either cancers or constitutional disorders. To address the needs and challenges, we implemented a hybrid informatics solution in terms of both the IT infrastructure and the bioinformatics tools. First of all, we employed an on-premise High-Performance Computing Cluster (HPCC) to address the mission-critical computing needs, established the parallel IT infrastucture in AWS cloud for redundancy, bursting nature of our computing needs and cost-effectiveness. Similarly, while implementing a phenotype-driven analytic platform for these genomics-based tests, we used a combination of open-source and custom software tools, striving to provide the most cutting-edge bioinformatics solution at an academic medical center. Examples of our custom solution include 1) a cloud- and web-based variant store using the OpenCGA package that we developed in collaboration with the Genomics England Project team for storing and analyzing somatic and germline variants from whole-exome sequencing and whole-genome sequencing of thousdands of patients, 2) an algorithm for computational phasing two candidate heterozygous variants in a single gene and for improved clinical diagnosis rate of recessive disorders therefore, and 3) an NGS coverage analysis algorithm that is 10 times more efficient than the most popular similar tools, namely SAMtools and Sambamba.