RNA-SeQC: RNA-seq metrics for quality control and process optimization

Abstract

RNA-seq, the application of next-generation sequencing to RNA, provides transcriptome-wide characterization of cellular activity. Assessment of sequencing performance and library quality is critical to the interpretation of RNA-seq data, yet few tools exist to address this issue. We introduce RNA-SeQC, a program which provides key measures of data quality. These metrics include yield, alignment and duplication rates; GC bias, rRNA content, regions of alignment (exon, intron and intragenic), continuity of coverage, 3'/5' bias and count of detectable transcripts, among others. The software provides multi-sample evaluation of library construction protocols, input materials and other experimental parameters. The modularity of the software enables pipeline integration and the routine monitoring of key measures of data quality such as the number of alignable reads, duplication rates and rRNA contamination. RNA-SeQC allows investigators to make informed decisions about sample inclusion in downstream analysis. In summary, RNA-SeQC provides quality control measures critical to experiment design, process optimization and downstream computational analysis.

Availability and implementation: See www.genepattern.org to run online, or www.broadinstitute.org/rna-seqc/ for a command line tool.

Fig. 1.

Overview of the RNA-SeQC process. (a) RNA-SeQC will work with one or more input samples to produce both a comparative summary across samples as well as a more detailed report for each sample. (b) The comparative summary report includes an extensive range of metrics (in addition to those shown) as well as coverage plots. (c) For each sample, additional reports quantify the coverage profile (variation, gaps, etc.) for individual transcripts