RNA‐seq Data: Challenges in and Recommendations for Experimental Design and Analysis
Open accessAbstract
RNA‐seq is widely used to determine differential expression of genes or transcripts as well as identify novel transcripts, identify allele‐specific expression, and precisely measure translation of transcripts. Thoughtful experimental design and choice of analysis tools are critical to ensure high‐quality data and interpretable results. Important considerations for experimental design include number of replicates, whether to collect paired‐end or single‐end reads, sequence length, and sequencing depth. Common analysis steps in all RNA‐seq experiments include quality control, read alignment, assigning reads to genes or transcripts, and estimating gene or transcript abundance. Our aims are two‐fold: to make recommendations for common components of experimental design and assess tool capabilities for each of these steps. We also test tools designed to detect differential expression, since this is the most widespread application of RNA‐seq. We hope that these analyses will help guide those who are new to RNA‐seq and will generate discussion about remaining needs for tool improvement and development. Curr. Protoc. Hum. Genet. 83:11.13.1‐11.13.20. © 2014 by John Wiley & Sons, Inc.
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If the main purpose of the analysis is to identify new transcripts, or examine allele-specific expression, higher depth of coverage is desirable.
“However, if the main purpose of the analysis is to identify new transcripts, or examine allele-specific expression, the higher depth of coverage is desirable [7–9]”
From Multi-omics approaches to disease (2017) · cites this paper as [9]
To identify differentially expressed genes between cases and controls, the power provided by more samples is generally preferable to the increased accuracy provided by higher depth of sequencing.
“To identify differentially expressed genes between the cases and controls, the power provided by more samples is generally preferable to the increased accuracy provided by higher depth of sequencing.”
From Multi-omics approaches to disease (2017) · cites this paper as [9]
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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 · see citing works
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895 · see citing works
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 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · see citing works
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556 · see citing works
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668 · see citing works
Fast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063 · see citing works
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,477 · see citing works
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876 · see citing works
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155 · see citing works
limma: Linear Models for Microarray Data
Smyth, G. K. (2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0_23 · Cited by 5,796 · see citing works
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195 · see citing works
DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
Wang, L., Feng, Z., Wang, X., Wang, X., & Zhang, X. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp612 · Cited by 4,553 · see citing works
Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S., & Weissman, J. S. (2009) · Science · doi:10.1126/science.1168978 · Cited by 3,853 · see citing works
ROCR: visualizing classifier performance in R
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti623 · Cited by 3,172 · see citing works
RSeQC: quality control of RNA-seq experiments
Wang, L., Wang, S., & Li, W. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts356 · Cited by 2,813 · see citing works
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771 · see citing works
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680 · see citing works
Parameter Orthogonality and Approximate Conditional Inference
Cox, D. R., & Reid, N. M. (1987) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1987.tb01422.x · Cited by 1,370 · see citing works
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Katz, Y., Wang, E. T., Airoldi, E. M., & Burge, C. B. (2010) · Nature Methods · doi:10.1038/nmeth.1528 · Cited by 1,353 · see citing works
HTSeq – A Python framework to work with high-throughput sequencing dataPreprint
Anders, S., Pyl, P. T., & Huber, W. K. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/002824 · Cited by 1,286 · see citing works
Dillies, M., Rau, A., Aubert, J., Hennequet‐Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estellé, J., et al. (2012) · Briefings in Bioinformatics · doi:10.1093/bib/bbs046 · Cited by 1,281 · see citing works
MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery
Wang, K., Singh, D., Zeng, Z., Coleman, S. J., Huang, Y., Savich, G. L., He, X., Mieczkowski, P., Grimm, S. A., Perou, C. M., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq622 · Cited by 1,193 · see citing works
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor
Anders, S., McCarthy, D. J., Chen, Y., Okoniewski, M. J., Smyth, G. K., Huber, W. K., & Robinson, M. D. (2013) · Nature Protocols · doi:10.1038/nprot.2013.099 · Cited by 1,129 · see citing works
Identification of novel transcripts in annotated genomes using RNA-Seq
Roberts, A., Pimentel, H., Trapnell, C., & Pachter, L. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr355 · Cited by 963 · see citing works
Flicek, P., Amode, M., Barrell, D., Beal, K., Billis, K., Brent, S., Carvalho-Silva, D., Clapham, P., Coates, G., Fitzgerald, S., et al. (2013) · Nucleic Acids Res. · doi:10.1093/nar/gkt1196 · Cited by 926 · see citing works
A comparison of methods for differential expression analysis of RNA-seq data
Soneson, C., & Delorenzi, M. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-91 · Cited by 887 · see citing works
Revisiting Global Gene Expression Analysis
Lovén, J., Orlando, D. A., Sigova, A. A., Lin, C. Y., Rahl, P. B., Burge, C. B., Levens, D. L., Lee, T. I., & Young, R. A. (2012) · Cell · doi:10.1016/j.cell.2012.10.012 · Cited by 616 · see citing works
Removing technical variability in RNA-seq data using conditional quantile normalization
Hansen, K. D., Irizarry, R. A., & WU, Z. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr054 · Cited by 607 · see citing works
Statistical Design and Analysis of RNA Sequencing Data
Auer, P. L., & Doerge, R. W. (2010) · Genetics · doi:10.1534/genetics.110.114983 · Cited by 393 · see citing works
Calculating Sample Size Estimates for RNA Sequencing Data
Hart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A., & Kocher, J.-P. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2012.0283 · Cited by 341 · see citing works
Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)
Grant, G. R., Farkas, M. H., Pizarro, A., Lahens, N. F., Schug, J., Brunk, B. P., Stoeckert, C. J., Hogenesch, J. B., & Pierce, E. A. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr427 · Cited by 336 · see citing works
Detection of splice junctions from paired-end RNA-seq data by SpliceMap
Au, K. F., Jiang, H., Lin, L., Xing, Y., & Wong, W. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq211 · Cited by 328 · see citing works
AltAnalyze and DomainGraph: analyzing and visualizing exon expression data
Emig, D., Salomonis, N., Baumbach, J., Lengauer, T., Conklin, B. R., & Albrecht, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq405 · Cited by 324 · see citing works
Nookaew, I., Papini, M., Pornputtapong, N., Scalcinati, G., Fagerberg, L., Uhlén, M., & Nielsen, J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks804 · Cited by 309 · see citing works
RNA-seq: technical variability and sampling
McIntyre, L. M., Lopiano, K. K., Morse, A. M., Amin, V., Oberg, A. L., Young, L. J., & Nuzhdin, S. V. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-293 · Cited by 285 · see citing works
A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data
Kvam, V. M., Liu, P., & Si, Y. (2012) · American Journal of Botany · doi:10.3732/ajb.1100340 · Cited by 281 · see citing works
A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
Wu, H., Wang, C., & Wu, Z. J. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs033 · Cited by 241 · see citing works
Design and validation issues in RNA-seq experiments
Fang, Z., & Cui, X. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr004 · Cited by 229 · see citing works
Vijay, N., Poelstra, J. W., Künstner, A., & Wolf, J. B. W. (2012) · Molecular Ecology · doi:10.1111/mec.12014 · Cited by 225 · see citing works
Skelly, D. A., Johansson, M., Madeoy, J., Wakefield, J., & Akey, J. M. (2011) · Genome Research · doi:10.1101/gr.119784.110 · Cited by 203 · see citing works
Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., & Taylor, J. M. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-484 · Cited by 198 · see citing works
Estimation of alternative splicing isoform frequencies from RNA-Seq data
Nicolae, M., Mangul, S., Măndoiu, I. I., & Zelikovsky, A. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-9 · Cited by 178 · see citing works
Heap, G., Yang, J. H. M., Downes, K., Healy, B. C., Hunt, K. A., Bockett, N. A., Franke, L., Dubois, P. C. A., Mein, C. A., Dobson, R., et al. (2009) · Human Molecular Genetics · doi:10.1093/hmg/ddp473 · Cited by 174 · see citing works
Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments
Richard, H., Schulz, M. H., Sultan, M., Nürnberger, A., Schrinner, S., Balzereit, D., Dagand, E., Rasche, A., Lehrach, H., Vingron, M., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq041 · Cited by 160 · see citing works
DiffSplice: the genome-wide detection of differential splicing events with RNA-seq
Hu, Y., Huang, Y., Du, Y., Orellana, C. F., Singh, D., Johnson, A., Monroy‐Eklund, A., Kuan, P. F., Hammond, S. M., Makowski, L., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1026 · Cited by 156 · see citing works
Łabaj, P. P., Leparc, G. G., Linggi, B. E., Markillie, L. M., Wiley, H. S., & Kreil, D. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr247 · Cited by 141 · see citing works
Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression
Busby, M. A., Stewart, C., Miller, C. A., Grzeda, K. R., & Marth, G. T. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt015 · Cited by 140 · see citing works
KIS SPLICE: de-novo calling alternative splicing events from RNA-seq data
Sacomoto, G., Kielbassa, J., Chikhi, R., et al. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-s6-s5 · Cited by 128 · see citing works
Lu, J., Tomfohr, J. K., & Kepler, T. B. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-165 · Cited by 79 · see citing works
A survey of software for genome-wide discovery of differential splicing in RNA-Seq data
Hooper, J. E. (2014) · Human Genomics · doi:10.1186/1479-7364-8-3 · Cited by 67 · see citing works
SplicingCompass: differential splicing detection using RNA-Seq data
Aschoff, M., Hotz‐Wagenblatt, A., Glatting, K., Fischer, M., Eils, R., & König, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt101 · Cited by 59 · see citing works
Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates
Baggerly, K., Deng, L., Morris, J. S., & Aldaz, C. M. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-144 · Cited by 58 · see citing works
FDM: a graph-based statistical method to detect differential transcription using RNA-seq data
Singh, D., Orellana, C. F., Hu, Y., Jones, C. D., Liu, Y., Chiang, D. Y., Liu, J., & Prins, J. F. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr458 · Cited by 56 · see citing works
Bioinformatics for High Throughput Sequencing
Rodríguez-Ezpeleta, N., Hackenberg, M., & Aransay, A. (2012) · Springer: New York · doi:10.1007/978-1-4614-0782-9 · Cited by 54 · see citing works
RNA-Seq Mapping and Detection of Gene Fusions with a Suffix Array Algorithm
Sakarya, O., Breu, H., Radovich, M., Chen, Y., Wang, Y., Bárbácioru, C., Utiramerur, S., Whitley, P., Brockman, J. P., Vatta, P., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002464 · Cited by 46 · see citing works
TrueSight: a new algorithm for splice junction detection using RNA-seq
Li, Y., Li‐Byarlay, H., Burns, P. D., Borodovsky, M. Y., Robinson, G. E., & Ma, J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1311 · Cited by 37 · see citing works
Nariai, N., Hirose, O., Kojima, K., & Nagasaki, M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt381 · Cited by 37 · see citing works
Differential expression analysis for paired RNA-seq data
Chung, L. M., Ferguson, J. P., Zheng, W., Qian, F., Bruno, V. M., Montgomery, R. R., & Zhao, H. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-110 · Cited by 32 · see citing works
Bashir, A., Bansal, V., & Bafna, V. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-385 · Cited by 24 · see citing works
Janda, J.-O., Busch, M. R., Kück, F., et al. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-55 · Cited by 18 · see citing works
Biological Averaging in RNA-SeqPreprint
Biswas, S., Agrawal, Y., Mucyn, T. S., Dangl, J. L., & Jones, C. D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.0670 · Cited by 14 · see citing works
Young, M. D., McCarthy, D. J., Wakefield, M. J., Smyth, G. K., Oshlack, A., & Robinson, M. D. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0782-9_10 · Cited by 10 · see citing works
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Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
Calculating Sample Size Estimates for RNA Sequencing Data
Hart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A., & Kocher, J.-P. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2012.0283 · Cited by 341 · see citing works
- Authors
- Williams, A. G., Thomas, S., Wyman, S. K., & Holloway, A. K.
- Affiliations
- Gladstone Institutes (US)
- Venue
- Current Protocols in Human Genetics
- Publisher
- Wiley
- Published
- 2014-10
- Volume
- 83
- Issue
- 1
- Pages
- 11.13.1-20
- ISSN
- 1934-8258 (ISSN-L)
- 1934-8266
- PMC
- PMC4230301
- OpenAlex
- W1501862196
- Cited by
- 93
- References
- 67
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 5 · 2023: 5 · 2022: 5 · 2021: 11 · 2020: 10 · 2019: 7 · 2018: 7 · 2017: 10 · 2016: 14 · 2015: 6 · 2013: 1
- FWCI
- 4.10
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Protocols in Human Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (U01HL098188)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, experimental design, differential gene expression, sequencing depth, number of replications, read mapping, gene expression quantification, quality control, allele-specific expression
- MeSH terms
- Quality Control, RNA Splicing, RNA, Messenger, Polymerase Chain Reaction, Sequence Analysis, RNA
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