Open AccessRNA-SeQC: RNA-seq metrics for quality control and process optimization
Author(s) -
David S. DeLuca,
Joshua Z. Levin,
Andrey Sivachenko,
Timothy Fennell,
MarcDanie Nazaire,
Chris F. Williams,
Michael Reich,
Wendy Winckler,
Gad Getz
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts196
Subject(s) - computer science , rna seq , rna , data mining , software , quality (philosophy) , pipeline (software) , computational biology , modularity (biology) , sample (material) , transcriptome , biology , genetics , gene , gene expression , philosophy , chemistry , epistemology , chromatography , programming language
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.