Analyses of High-Throughput Sequencing Data from Cancer Samples

• Activity Number: 357
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Su Yeon Kim*+ and Terence Speed
• Companies: University of California at Berkeley and University of California at Berkeley
• Keywords: cancer genomics ; next-generation sequencing ; quality control ; somatic alterations ; somatic mutations

Abstract

Analyses of genomic alternations and gene expression levels expedite pinpointing underlying mechanisms for cancer development and developing cancer diagnosis and therapy. Rapid development in high-throughput sequencing technologies allows additional advances due to the un-precedented level of information contained in the sequence data. A vast amount of sequencing data is rapidly being produced and one such example is The Cancer Genome Atlas (TCGA) project. For several tumor types, hundreds of exomes and transcriptomes are sequenced from tumor and the matched normal tissues. Developing efficient tools for processing the data and doing quality control (QC) is essential for improving downstream analyses. In this talk, I will present QC metrics for exome DNA sequencing data, and illustrate how our methods can contribute to reducing false positives in downstream analyses such as identifying somatic mutations.