Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

Background: High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data. Results: We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the rea...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: James H. Bullard, Elizabeth Purdom, Kasper D. Hansen, Sandrine Dudoit
• Keywords: BMCBI 2010, Computational Methods, Sequencing, Statistical Methods