We are analyzing https://link.springer.com/protocol/10.1007/978-1-4939-0512-6_4.

Title:
Use Model-Based Analysis of ChIP-Seq (MACS) to Analyze Short Reads Generated by Sequencing Protein–DNA Interactions in Embryonic Stem Cells | SpringerLink
Description:
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...
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Keywords {🔍}

pubmed, article, google, scholar, cas, central, chipseq, protocol, macs, stem, privacy, cookies, content, analysis, data, information, publish, proteindna, cells, methods, access, bioinformatics, search, cell, modelbased, reads, sequencing, interactions, liu, genomewide, genome, nat, nature, download, springer, usd, personal, log, journal, research, transcriptional, networks, analyze, short, embryonic, tao, book, biology, chapter, open,

Topics {✒️}

sequence alignment/map format month download article/chapter sequencing protein–dna interactions vivo protein–dna interactions fast gapped-read alignment protein–dna interactions model-based analysis embryonic stem cells privacy choices/manage cookies massively parallel sequencing device instant download genome-wide mapping removing redundant reads high-resolution profiling large distributed datasets integrative genomics viewer stat1 dna association genome-wide profiles genome-wide maps european economic area estimating fragment length building signal profile calculating peak enrichment reporting peak calls open-source parallel expression profiling complete-likelihood score lineage-committed cells burrows-wheeler transform comparing genomic features author information authors editor information editors chip-seq data rna-seq studies chip-seq guidelines journal finder publish conditions privacy policy accepting optional cookies tao liu check access ethics access protocol liu main content log protocol usd 49 protocol cite social media permissions reprints cell 129 chromatin state humana press

Schema {🗺️}

ScholarlyArticle:
      headline:Use Model-Based Analysis of ChIP-Seq (MACS) to Analyze Short Reads Generated by Sequencing Protein–DNA Interactions in Embryonic Stem Cells
      pageEnd:95
      pageStart:81
      image:https://media.springernature.com/w153/springer-static/cover/book/978-1-4939-0512-6.jpg
      genre:
         Springer Protocols
      isPartOf:
         name:Stem Cell Transcriptional Networks
         isbn:
            978-1-4939-0512-6
            978-1-4939-0511-9
         type:Book
      publisher:
         name:Springer New York
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Tao Liu
            affiliation:
                  name:University at Buffalo-COEBLS
                  address:
                     name:Department of Biochemistry, University at Buffalo-COEBLS, Buffalo, USA
                     type:PostalAddress
                  type:Organization
            email:[email protected]
            type:Person
      keywords:ChIP-seq, Peak calling, Transcription factor, Histone modification
      description: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 .
      datePublished:2014
      isAccessibleForFree:
      hasPart:
         isAccessibleForFree:
         cssSelector:.main-content
         type:WebPageElement
      context:https://schema.org
Book:
      name:Stem Cell Transcriptional Networks
      isbn:
         978-1-4939-0512-6
         978-1-4939-0511-9
Organization:
      name:Springer New York
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:University at Buffalo-COEBLS
      address:
         name:Department of Biochemistry, University at Buffalo-COEBLS, Buffalo, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Tao Liu
      affiliation:
            name:University at Buffalo-COEBLS
            address:
               name:Department of Biochemistry, University at Buffalo-COEBLS, Buffalo, USA
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Department of Biochemistry, University at Buffalo-COEBLS, Buffalo, USA
WebPageElement:
      isAccessibleForFree:
      cssSelector:.main-content

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