SAMQA: error classification and validation of high-throughput sequenced read data.

ABSTRACT:

Background

The advances in high-throughput sequencing technologies and growth in data sizes has highlighted the need for scalable tools to perform quality assurance testing. These tests are necessary to ensure that data is of a minimum necessary standard for use in downstream analysis. In this paper we present the SAMQA tool to rapidly and robustly identify errors in population-scale sequence data.

Results

SAMQA has been used on samples from three separate sets of cancer genome data from The Cancer Genome Atlas (TCGA) project. Using technical standards provided by the SAM specification and biological standards defined by researchers, we have classified errors in these sequence data sets relative to individual reads within a sample. Due to an observed linearithmic speedup thr

SUBMITTER: Robinson T 

PROVIDER: S-EPMC3170309 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature