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Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

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Pertea, Mihaela, Kim, Daehwan, Pertea, Geo M, Leek, Jeffrey T, Salzberg, Steven L·2016·Nature Protocols

Abstract

High-throughput sequencing of mRNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate and flexible software to reduce the raw read data to comprehensible results. HISAT (hierarchical indexing for spliced alignment of transcripts), StringTie and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol's execution time depends on the computing resources, but it typically takes under 45 min of computer time. HISAT, StringTie and Ballgown are available from http://ccb.jhu.edu/software.shtml.

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  1. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008). · DOI · Paper page
  2. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012). · DOI · Paper page
  3. Kim, D., Langmead, B. & Salzberg, S.L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015). · DOI · Paper page
  4. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015). · DOI · Paper page
  5. Frazee, A.C. et al. Ballgown bridges the gap between transcriptome assembly and expression analysis. Nat. Biotechnol. 33, 243–246 (2015). · DOI · Paper page
  6. Li, W., Feng, J. & Jiang, T. IsoLasso: a LASSO regression approach to RNA-seq based transcriptome assembly. J. Comput. Biol. 18, 1693–1707 (2011). · DOI
  7. Grabherr, M.G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011). · DOI · Paper page
  8. Schulz, M.H., Zerbino, D.R., Vingron, M. & Birney, E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 28, 1086–1092 (2012). · DOI · Paper page
  9. Xie, Y. et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics 30, 1660–1666 (2014). · DOI · Paper page
  10. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). · DOI · Paper page
  11. Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017 (2012). · DOI · Paper page
  12. Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl. Acad. Sci. USA 111, E5593–E5601 (2014). · DOI · Paper page
  13. Katz, Y., Wang, E.T., Airoldi, E.M. & Burge, C.B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010). · DOI · Paper page
  14. Ferragina, P. & Manzini, G. Opportunistic data structures with applications. Proceedings 41st Annual Symposium on Foundations of Computer Science (2000).
  15. Raj, A. et al. Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling. eLife 5, e13328 (2016). · DOI
  16. Kodama, Y., Shumway, M. & Leinonen, R. The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res. 40, D54–D56 (2012). · DOI · Paper page
  17. Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015). · DOI · Paper page
  18. Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015). · DOI · Paper page
  19. Paulson, J.N., Stine, O.C., Bravo, H.C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200–1202 (2013). · DOI · Paper page
  20. Robert, C. & Watson, M. Errors in RNA-Seq quantification affect genes of relevance to human disease. Genome Biol. 16, 177 (2015). · DOI · Paper page
  21. Pertea, M. The human transcriptome: an unfinished story. Genes 3, 344–360 (2012). · DOI
  22. Chow, J.C. et al. Inducible XIST-dependent X-chromosome inactivation in human somatic cells is reversible. Proc. Natl. Acad. Sci. USA 104, 10104–10109 (2007). · DOI
  23. Lee, J.T., Davidow, L.S. & Warshawsky, D. Tsix, a gene antisense to Xist at the X-inactivation centre. Nat. Genet. 21, 400–404 (1999). · DOI · Paper page
  24. Talebizadeh, Z., Simon, S.D. & Butler, M.G. X chromosome gene expression in human tissues: male and female comparisons. Genomics 88, 675–681 (2006). · DOI

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Metadata

Authors
Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., & Salzberg, S. L.
Venue
Nature Protocols
Published
2016-08-11
Volume
11
Issue
9
Pages
1650-1667
ISSN
  • 1750-2799 (ISSN-L)
  • 1754-2189
OpenAlex
W2510875395
Cited by
6,845
References
15
FWCI
116.17
Type
journal article
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)

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Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., & Salzberg, S. L. (2016). Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols. https://doi.org/10.1038/nprot.2016.095

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