The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Comparative analysis of RNA sequencing methods for degraded or low-input samples
Open accessAbstract
RNA-Seq is an effective method to study the transcriptome, but can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations, or cadavers. Recent studies have proposed several methods for RNA-Seq of low quality and/or low quantity samples, but their relative merits have not been systematically analyzed. Here, we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery, and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and two control libraries. We find that the RNase H method performed best for low quality RNA, and confirmed this with actual degraded samples. RNase H can even effectively replace oligo (dT) based methods for standard RNA-Seq. SMART and NuGEN had distinct strengths for low quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development.
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Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Improving RNA-Seq expression estimates by correcting for fragment bias
Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L., & Pachter, L. (2011) · Genome Biology · doi:10.1186/gb-2011-12-3-r22 · Cited by 1,725
RNA-SeQC: RNA-seq metrics for quality control and process optimization
DeLuca, D. S., Levin, J. Z., Sivachenko, A., Fennell, T., Nazaire, M.-D., Williams, C., Reich, M., Winckler, W., & Getz, G. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts196 · Cited by 899
Self-citation
Cited by
Zmora, N., Zilberman-Schapira, G., Suez, J., Mor, U., Dori-Bachash, M., Bashiardes, S., Kotler, E., Zur, M., Regev-Lehavi, D., Brik, R. B.-Z., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.041 · Cited by 1,695
Clinical Metagenomic Next-Generation Sequencing for Pathogen Detection
Gu, W., Miller, S., & Chiu, C. Y. (2019) · Annual Review of Pathology: Mechanisms of Disease · doi:10.1146/annurev-pathmechdis-012418-012751 · Cited by 1,458
Loss of a mammalian circular RNA locus causes miRNA deregulation and affects brain function
Piwecka, M., Glažar, P., Hernandez-Miranda, L. R., Memczak, S., Wolf, S. A., Rybak-Wolf, A., Filipchyk, A., Klironomos, F., Cerda Jara, C. A., Fenske, P., et al. (2017) · Science · doi:10.1126/science.aam8526 · Cited by 1,210
Suez, J., Zmora, N., Zilberman-Schapira, G., Mor, U., Dori-Bachash, M., Bashiardes, S., Zur, M., Regev-Lehavi, D., Ben-Zeev Brik, R., Federici, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.047 · Cited by 1,184
Genetic and functional drivers of diffuse large B cell lymphoma
Reddy, A., Zhang, J., Davis, N. S., Moffitt, A. B., Love, C. L., Waldrop, A., Leppa, S., Pasanen, A., Meriranta, L., Karjalainen-Lindsberg, M.-L., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.027 · Cited by 1,104
Metadata
- Authors
- Adiconis, X., Borges-Rivera, D., Satija, R., DeLuca, D. S., Busby, M. A., Berlin, A. M., Sivachenko, A., Thompson, D. A., Wysoker, A., Fennell, T., Gnirke, A., Pochet, N., Regev, A., & Levin, J. Z.
- Venue
- Nature Methods
- Published
- 2013-05-19
- Volume
- 10
- Issue
- 7
- Pages
- 623-629
- ISSN
- 1548-7091
- 1548-7105
- OpenAlex
- W2031874858
- Cited by
- 503
- References
- 5
- FWCI
- 16.00
- Type
- journal article
- Language
- en
- Open access
- green
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