RNA-SeQC: RNA-seq metrics for quality control and process optimization
Open accessAbstract
Summary: 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. Contact: ddeluca@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.
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The findings confirm GC-content and insert size as recognized RNA-seq latent variables and identify gene coverage variation and error rate as additional contributions to technical variation.
“This confirms the impact of two already recognized RNA-seq latent variables, GC-content and insert size15,22, and it also identifies two more relevant contributions to technical variation, gene coverage variation and error rate.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [22]
A quality check of aligned data was done with RNA-SEQC and showed high quality alignment and data.
“A quality check of aligned data was done with RNA-SEQC 55 and showed high quality alignment and data.”
From Transcriptomic analysis of purified human cortical microglia reveals age-associated changes (2017) · cites this paper as [55]
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- Authors
- DeLuca, D. S., Levin, J. Z., Sivachenko, A., Fennell, T., Nazaire, M.-D., Williams, C., Reich, M., Winckler, W., & Getz, G.
- Affiliations
- Broad Institute (US)
- Corresponding author
- DeLuca, David S.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-04-25
- Volume
- 28
- Issue
- 11
- Pages
- 1530-1532
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3356847
- OpenAlex
- W2160443244
- Cited by
- 899
- References
- 9
- Citations by year
- 2026: 44 · 2025: 70 · 2024: 75 · 2023: 68 · 2022: 90 · 2021: 88 · 2020: 90 · 2019: 74 · 2018: 85 · 2017: 60 · 2016: 57 · 2015: 45 · 2014: 33 · 2013: 14 · 2012: 5
- FWCI
- 12.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (HHSN268201000029C)
- National Human Genome Research Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- RNA-seq quality control, sequencing metrics, GC content bias, process optimization
- MeSH terms
- Quality Control, RNA, RNA, Ribosomal, Software, Gene Library, Sequence Analysis, RNA, Internet, Gene Expression Profiling, High-Throughput Nucleotide Sequencing
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