title: Use model-based Analysis of ChIP-Seq (MACS) to analyze short reads generated by sequencing protein-DNA interactions in embryonic stem cells.
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.
authors.affiliation: Department of Biochemistry, University at Buffalo-COEBLS, 701 Ellicott St, B2-163, Buffalo, NY, 14203-1221, USA, tliu4@buffalo.edu.
authors.forename: Tao
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D002460:Cell Line; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D004247:DNA / Q000235:genetics* / Q000378:metabolism*; D004268:DNA-Binding Proteins / Q000378:metabolism*; D053595:Embryonic Stem Cells / Q000378:metabolism*; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006657:Histones / Q000378:metabolism; D006801:Humans; D011485:Protein Binding; D017422:Sequence Analysis, DNA; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D004268:DNA-Binding Proteins; D006657:Histones; D014157:Transcription Factors; D004247:DNA