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ChIPseq in Yeast Species: From Chromatin Immunoprecipitation to High-Throughput Sequencing and Bioinformatics Data Analyses

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Yeast Functional Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1361))

Abstract

Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIPseq) is a powerful technique for the genome-wide location of protein DNA-binding sites. The ChIP experiment consists in treating living cells with a cross-linking agent to bind proteins to their DNA substrates. After fragmentation of DNA, specific fractions associated with a particular protein of interest are purified by immunoaffinity. They are next sequenced and identified on the reference genome using dedicated bioinformatics programs. Several technical aspects are important to obtain high-quality ChIPseq results. This includes the quality of antibodies, the sequencing protocols, the use of accurate controls and the careful choice of bioinformatics tools. We present here a general protocol to perform ChIPseq analyses in yeast species. This protocol has been optimized to identify target genes of specific transcription factors but can be used for any other DNA binding proteins.

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References

  1. Harbison CT et al (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431(7004):99–104

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Lando D et al (2012) Quantitative single-molecule microscopy reveals that CENP-A(Cnp1) deposition occurs during G2 in fission yeast. Open Biol 2(7):120078

    Article  PubMed Central  PubMed  Google Scholar 

  3. Barski A et al (2007) High-resolution profiling of histone methylations in the human genome. Cell 129(4):823–837

    Article  CAS  PubMed  Google Scholar 

  4. Thurtle DM, Rine J (2014) The molecular topography of silenced chromatin in Saccharomyces cerevisiae. Genes Dev 28(3):245–258

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Johnson DS, Mortazavi A, Myers RM, Wold B (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science 316(5830):1497–1502

    Article  CAS  PubMed  Google Scholar 

  6. Park PJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10(10):669–680

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Ho JW et al (2011) ChIP-chip versus ChIP-seq: lessons for experimental design and data analysis. BMC Genomics 12:134

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Cock PJ, Fields CJ, Goto N, Heuer ML, Rice PM (2010) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38(6):1767–1771

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Li R et al (2009) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25(15):1966–1967

    Article  CAS  PubMed  Google Scholar 

  10. Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26(5):589–595

    Article  PubMed Central  PubMed  Google Scholar 

  11. Chen Y et al (2013) CGAP-align: a high performance DNA short read alignment tool. PLoS One 8(4), e61033

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Schbath S et al (2012) Mapping reads on a genomic sequence: an algorithmic overview and a practical comparative analysis. J Comput Biol 19(6):796–813

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Wilbanks EG, Facciotti MT (2010) Evaluation of algorithm performance in ChIP-seq peak detection. PLoS One 5(7), e11471

    Article  PubMed Central  PubMed  Google Scholar 

  14. Malone BM, Tan F, Bridges SM, Peng Z (2011) Comparison of four ChIP-Seq analytical algorithms using rice endosperm H3K27 trimethylation profiling data. PLoS One 6(9), e25260

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Bailey T et al (2013) Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Comput Biol 9(11), e1003326

    Article  PubMed Central  PubMed  Google Scholar 

  16. Merhej J et al (2014) bPeaks: a bioinformatics tool to detect transcription factor binding sites from ChIPseq data in yeasts and other organisms with small genomes. Yeast 31(10):375–391

    Article  CAS  PubMed  Google Scholar 

  17. Zhang Y et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9(9):R137

    Article  PubMed Central  PubMed  Google Scholar 

  18. Kharchenko PV, Tolstorukov MY, Park PJ (2008) Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol 26(12):1351–1359

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Spyrou C, Stark R, Lynch AG, Tavare S (2009) BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinformatics 10:299

    Article  PubMed Central  PubMed  Google Scholar 

  20. Landt SG et al (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 22(9):1813–1831

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Blankenberg D et al (2010) Manipulation of FASTQ data with Galaxy. Bioinformatics 26(14):1783–1785

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. Li H et al (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25(16):2078–2079

    Article  PubMed Central  PubMed  Google Scholar 

  23. Rozowsky J et al (2009) PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat Biotechnol 27(1):66–75

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  24. Thomas-Chollier M et al (2012) RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res 40(4), e31

    Article  PubMed Central  CAS  PubMed  Google Scholar 

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Correspondence to Gaëlle Lelandais or Jawad Merhej .

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Lelandais, G., Blugeon, C., Merhej, J. (2016). ChIPseq in Yeast Species: From Chromatin Immunoprecipitation to High-Throughput Sequencing and Bioinformatics Data Analyses. In: Devaux, F. (eds) Yeast Functional Genomics. Methods in Molecular Biology, vol 1361. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3079-1_11

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  • DOI: https://doi.org/10.1007/978-1-4939-3079-1_11

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3078-4

  • Online ISBN: 978-1-4939-3079-1

  • eBook Packages: Springer Protocols

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