Epigenetic transcription regulation Computational Lab

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Epigenetic transcription regulationComputational
Lab

• Prof. Cristian Coarfa

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Recap

• Chromatine Immunoprecipitation (ChIP)
• Massively parallel DNA sequencing (Seq)
• Model-based Analysis of ChIP-Seq (MACS)

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MACS

• Model-based analysis of ChIP-Seq data
• Python application
• Download from http//liulab.dfci.harvard.edu/MACS/
  
• Input in BED format
• Tab delimited file
• Chr chromStart chromStop strand

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MACS

• Extensive help
• Example
• Results
• sample1vs2.pv-0.00001_peaks.xls
• sample1vs2.pv-0.00001_negative_peaks.xls

macs -t sample1.bed -c sample2.bed
--namesample1vs2.pv-0.00001 --pvalue0.00001
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MACS Results

• This file is generated by MACS
• ARGUMENTS LIST
• name sample1vs2.pv-0.00001
• format BED
• ChIP-seq file mycn-induced.bed
• control file mycn-noninduced.bed
• effective genome size 2.70e09
• tag size 25
• band width 300
• model fold 32
• pvalue cutoff 1.00e-05
• Ranges for calculating regional lambda are
  1000,5000,10000
• d 91
• chr start end length summit tags
  -10log10(pvalue) fold_enrichment FDR()
• chr1 829864 830159 296 162 19
  126.87 18.30 21.57
• chr1 841895 842146 252 84 10
  95.60 27.55 27.66
• chr1 842280 842476 197 90 6
  55.51 18.49 37.30
• chr1 846201 846725 525 266 19
  51.35 9.39 39.66
• chr1 863434 864067 634 331 28
  50.30 4.27 39.83

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Peaks Analysis

• CEAS annotation server
• http//ceas.cbi.pku.edu.cn/
• Gene elements (promoter, enhancer, etc)
• Motifs
• Genboree
• Convert peaks file to lff format
• Visualize
• Intersect with tracks of interest
• Genes
• miRNAs
• Cpg islands
• Custom tracks eg disease outcome predictors

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