Use Model-Based Analysis of ChIP-Seq (MACS) to Analyze Short Reads Generated by Sequencing Protein–DNA Interactions in Embryonic Stem Cells (original) (raw)

Abstract

Model-based Analysis of ChIP-Seq (MACS) is a computational algorithm for identifying genome-wide protein–DNA interaction from ChIP-Seq data. MACS combines multiple modules to process aligned ChIP-Seq reads for either transcription factor or histone modification by removing redundant reads, estimating fragment length, building signal profile, calculating peak enrichment, and refining and reporting peak calls. In this protocol, we provide a detailed demonstration of how to apply MACS to analyze ChIP-Seq datasets related to protein–DNA interactions in embryonic stem cells (ES cells). Instruction on how to interpret and visualize the results is also provided. MACS is an open-source and is available from http://github.com/taoliu/MACS.

Similar content being viewed by others

References

  1. Johnson DS, Mortazavi A, Myers RM, Wold B (2007) Genome-wide mapping of in vivo protein–DNA interactions. Science 316:1497–1502
    Article CAS PubMed Google Scholar
  2. Barski A et al (2007) High-resolution profiling of histone methylations in the human genome. Cell 129:823–837
    Article CAS PubMed Google Scholar
  3. Robertson G et al (2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4:651–657
    Article CAS PubMed Google Scholar
  4. Mikkelsen TS et al (2007) Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448:553–560
    Article CAS PubMed Central PubMed Google Scholar
  5. ENCODE Project Consortium et al (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74
    Article Google Scholar
  6. Bernstein BE et al (2010) The NIH roadmap epigenomics mapping consortium. Nat Biotechnol 28:1045–1048
    Article CAS PubMed Central PubMed Google Scholar
  7. Pepke S, Wold B, Mortazavi A (2009) Computation for ChIP-seq and RNA-seq studies. Nat Methods 6:S22–S32
    Article CAS PubMed Google Scholar
  8. Zhang Y et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9:R137
    Article PubMed Central PubMed Google Scholar
  9. Landt SG et al (2012) ChIP-seq guidelines and practices used by the ENCODE and modENCODE consortia. Genome Res 22:1813–1831
    Article CAS PubMed Central PubMed Google Scholar
  10. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760
    Article CAS PubMed Central PubMed Google Scholar
  11. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359
    Article CAS PubMed Central PubMed Google Scholar
  12. Li H et al (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079
    Article PubMed Central PubMed Google Scholar
  13. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841–842
    Article CAS PubMed Central PubMed Google Scholar
  14. Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D (2010) BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26:2204–2207
    Article CAS PubMed Central PubMed Google Scholar
  15. Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinformatics 14:178–192
    Article PubMed Central PubMed Google Scholar

Download references

Acknowledgment

This work is supported by Startup funds from University at Buffalo.

Author information

Authors and Affiliations

  1. Department of Biochemistry, University at Buffalo-COEBLS, 701 Ellicott St, B2-163, Buffalo, NY, 14203-1221, USA
    Tao Liu

Authors

  1. Tao Liu
    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toTao Liu .

Editor information

Editors and Affiliations

  1. Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
    Benjamin L. Kidder

Rights and permissions

© 2014 Springer Science+Business Media New York

About this protocol

Cite this protocol

Liu, T. (2014). Use Model-Based Analysis of ChIP-Seq (MACS) to Analyze Short Reads Generated by Sequencing Protein–DNA Interactions in Embryonic Stem Cells. In: Kidder, B. (eds) Stem Cell Transcriptional Networks. Methods in Molecular Biology, vol 1150. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0512-6\_4

Download citation

Publish with us