Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Computation for ChIP-seq and RNA-seq studies

Abstract

Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: A hierachical overview of ChIP-seq and RNA-seq analyses.
Figure 2: ChIP-seq peak types from various experiments.
Figure 3: ChIP-seq peak calling subtasks.
Figure 4: The impact of fragment length and complex peak structures in ChIP-seq.
Figure 5: Overview of RNA-seq.
Figure 6: Approaches to handle spliced reads.

References

  1. ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).

  2. Wold, B. & Myers, R.M. Sequence census methods for functional genomics. Nat. Methods 5, 19–21 (2008).

    Article  CAS  Google Scholar 

  3. Trapnell, C. & Salzberg, S.L. How to map billions of short reads onto genomes. Nat. Biotechnol. 27, 455–457 (2009).

    Article  CAS  Google Scholar 

  4. Johnson, D.S., Mortazavi, A., Myers, R.M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

    Article  CAS  Google Scholar 

  5. Rozowsky, J. et al. PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat. Biotechnol. 27, 66–75 (2009).

    Article  CAS  Google Scholar 

  6. Baugh, L.R., Demodena, J. & Sternberg, P.W. RNA Pol II accumulates at promoters of growth genes during developmental arrest. Science 324, 92–94 (2009).

    Article  CAS  Google Scholar 

  7. Barski, A. et al. High-resolution profiling on histone methylations in the human genome. Cell 129, 823–837 (2007).

    Article  CAS  Google Scholar 

  8. Mikkelsen, T.S. et al. Genome-wide maps of chromatin state in pluripotent and linearge-committed cells. Nature 448, 553–560 (2007).

    Article  CAS  Google Scholar 

  9. Valouev, A. et al. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat. Methods 5, 829–834 (2008).

    Article  CAS  Google Scholar 

  10. Ji, H. et al. An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat. Biotechnol. 26, 1293–1300 (2008).

    Article  CAS  Google Scholar 

  11. Jothi, R., Cuddapah, S., Barski, A., Cui, K. & Zhao, K. Genome-wide identification of in vivo protein-DNA binding sites from ChIP-seq data. Nucleic Acids Res. 36, 5221–5231 (2008).

    Article  CAS  Google Scholar 

  12. Kharchenko, P.V., Tolstorukov, M.Y. & Park, P.J. Design and anlysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008).

    Article  CAS  Google Scholar 

  13. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137.1– R137.9 (2008).

    Google Scholar 

  14. Boyle, A.P., Guinney, J., Crawford, G.E. & Furey, T.S. F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics 24, 2537–2538 (2008).

    Article  CAS  Google Scholar 

  15. Zang, C. et al. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25, 1952–1958 (2009).

    Article  CAS  Google Scholar 

  16. Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007).

    Article  CAS  Google Scholar 

  17. Tuteja, G., White, P., Schug, J. & Kaestner, K.H. Extracting transcription factor targets from ChIP-Seq data. Nucleic Acids Res. advance online publication doi:10.1093/nar/gkp536 (24 June 2009).

  18. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  Google Scholar 

  19. Fejes, A.P. et al. FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology. Bioinformatics 24, 1729–1730 (2008).

    Article  CAS  Google Scholar 

  20. Nix, D.A., Courdy, S.J. & Boucher, K.M. Empirical methods for controlling false positives and estimating confidence in ChIP-seq peaks. BMC Bioinformatics 9, 523 (2008).

    Article  Google Scholar 

  21. Xu, H., Wei, C., Lin, F. & Sung, W.K. An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data. Bioinformatics 24, 2344–2349 (2008).

    Article  CAS  Google Scholar 

  22. Hon, G., Ren, B. & Wang, W. ChromaSig: a probabilistic approach to finding common chromatin signatures in the human genome. PLOS Comput. Biol. 4, e1000201 (2008).

    Article  Google Scholar 

  23. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).

    Article  CAS  Google Scholar 

  24. Wihelm, B.T. et al. Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453, 1239–1243 (2008).

    Article  Google Scholar 

  25. Cloonan, N. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 5, 613–619 (2008).

    Article  CAS  Google Scholar 

  26. Marioni, J.C., Mason, C.E., Mane, S.M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

    Article  CAS  Google Scholar 

  27. Sultan, M. et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956–960 (2008).

    Article  CAS  Google Scholar 

  28. Wang, E.T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).

    Article  CAS  Google Scholar 

  29. Oshlack, A. & Wakefield, M.J. Transcript length bias in RNA-seq data confounds systems biology. Biol. Direct 4, 14 (2009).

    Article  Google Scholar 

  30. Bullard, J.H., Purdom, E.A., Hansen, K. D, Durinck, S. & Dudoit, S. Statistical inference in mRNA-seq: exploratory data analysis and differential expression. UC Berkeley Division of Biostatistics Working Paper Series 247 (2009).

    Google Scholar 

  31. Zerbino, D.R. & Birney, E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008).

    Article  CAS  Google Scholar 

  32. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  Google Scholar 

  33. Birol, I. et al. De novo transcriptome assembly with ABySS. Bioinformatics advance online publication, doi:10.1093/bioinformatics/btp367 (15 June 2009).

  34. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  35. Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008).

    Article  CAS  Google Scholar 

  36. Cloonan, N. et al. RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data. Bioinformatics advance online publication, doi:10.1093/bioinformatics/btp459 (30 July 2009).

  37. Denoeud, F. et al. Annotating genomes with massive-scale RNA sequencing. Genome Biol. 9, R175 (2009).

    Article  Google Scholar 

  38. De Bona, F., Ossowski, S., Schneeberger, K. & Rätsch, G. Optimal spliced alignments of short sequence reads. Bioinformatics 24, i175–i180 (2008).

    Article  Google Scholar 

  39. Zhang, Z., Carriero, N. & Gerstein, M. Comparative analysis of processed pseudogenes in the mouse and human genomes. Trends Genet. 20, 62–67 (2004).

    Article  Google Scholar 

  40. Jiang, H. & Wong, W.H. Statistical inferences for isoform expression in RNA-seq. Bioinformatics 25, 1026–1032 (2009).

    Article  CAS  Google Scholar 

  41. Zheng, S. & Chen, L. A hierarchical Bayesian model for comparing transcriptomes at the individual transcript isoform level. Nucleic Acids Res. 37, e75 (2009).

    Article  Google Scholar 

  42. Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A. & Vingron, M. Variance stabilization applied to microarray data calibration and to the quantification of differential gene expression. Bioinformatics 18 Suppl 1, S96–S104 (2002).

    Article  Google Scholar 

  43. Chepelev, I., Wei, G., Tang, Q. & Zhao, K. Detection of single nucleotide variations in expressed exons of the human genome using RNA-seq. Nucleic Acids Res. advance online publication, doi:10.1093/nar/gkp507 (15 June 2009).

  44. Li, J.B. et al. Genome-wide identification of human RNA editing sites by parallel DNA capturing and sequencing. Science 324, 1210–1213 (2009).

    Article  CAS  Google Scholar 

  45. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008).

    Article  CAS  Google Scholar 

  46. Dostie, J. et al. Chromosome conformation capture carbon copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 16, 1299–1309 (2006).

    Article  CAS  Google Scholar 

  47. Fullwood, M.J., Wei, C.L., Liu, E.T. & Ruan, Y. Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genomes analyses. Genome Res. 19, 521–532 (2009).

    Article  CAS  Google Scholar 

  48. Armour, C.D. et al. Digital transcriptome profiling using selective priming for cDNA synthesis. Nat. Methods 6, 647–649 (2009).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by The Beckman Foundation, The Beckman Institute, The Simons Foundation and US National Institutes of Health (NIH) grant U54 HG004576 to B.W., Fellowships from the Gordon and Betty Moore Foundation, Caltech's Center for the Integrative Study of Cell Regulation, and the Beckman Institute to A.M., and support from the Gordon and Betty Moore foundation to S.P. The authors would like to especially thank G. Marinov and P. Sternberg for many helpful discussions of this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Mortazavi.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Pepke, S., Wold, B. & Mortazavi, A. Computation for ChIP-seq and RNA-seq studies. Nat Methods 6 (Suppl 11), S22–S32 (2009). https://doi.org/10.1038/nmeth.1371

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.1371

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing