Uma Chandran

Biography

 I have extensive experience in both bench research and bioinformatics and co-direct the Cancer Bioinformatics Service (CBS), a translational core service at the University of Pittsburgh Cancer Institute. CBS is an interdisciplinary collaboration between my core team, the Department of Biomedical Informatics faculty, UPCI, the Institute for Personalized Medicine, the Pittsburgh Supercomputing Center and the University of Pittsburgh's Simulation and Modeling Center. The CBS aims are to 1) provide data analysis, 2) develop high performance computing (HPC) infrastructure and 3) develop research databases to integrate clinical and genomic data for biomarker discovery. I have deep experience working with all genomic platforms and applications including next generation sequencing (NGS) based RNA Seq, Whole Exome Seq (WXS) and Whole Genome Seq (WGS). I have also performed integrative analysis across multiple platforms and from large consortia datasets such as The Cancer Genome Atlas (TCGA) project. I am a key member of several data science projects in DBMI including the Pittsburgh Genome Resource Repository (PGRR), a regulatory, hardware and software infrastructure for TCGA data and the PACURE projects in breast and lung cancers. CBS supports all CCCG's disease specific programs and has performed somatic variant analysis in melanoma, lung, mesothelioma and renal cell cancers, copy number analysis to understand the differences and similarities between pediatric gliomas to adult gliomas, and RNA seq expression and integrative analysis of TCGA breast tumor samples. My team of analysts and I also support non-cancer genomic studies including development of methods for de novo transcriptome assembly. The combination of experience in bench science and bioinformatics allows me to not only provide data analysis services but also biological insight into the results of high throughput data such as integrative analysis of ChIP Seq and RNA seq data. We have partnered closely with the PSC and SaM to develop the HPC infrastructure to meet the computational demands of next generation sequencing analysis and have been awarded NSF's XSEDE award for computation and storage. With this team approach to bioinformatics, I have built a shared resource which can quickly meet the bioinformatics requirements of rapidly changing genomics landscape and a computing infrastructure which scales easily to terabytes of data.

Research Interest

 Genomic data mining, Next Generation Sequencing data analysis, Integrative analysis of The Cancer Genome Atlas Project (TCGA), Development of computing infrastructure for large genomics datasets