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Statistical Analysis and Quality Assessment of ChIP-seq Data with DROMPA

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Genome Instability

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1672))

Abstract

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis can detect protein/DNA-binding and histone-modification sites across an entire genome. As there are various factors during sample preparation that affect the obtained results, multilateral quality assessments are essential. Here, we describe a step-by-step protocol using DROMPA, a program for user-friendly ChIP-seq pipelining. DROMPA can be used for quality assessment, data normalization, visualization, peak calling, and multiple statistical analyses.

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References

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Author information

Authors and Affiliations

  1. Institute of Molecular and Cellular Biosciences, The University of Tokyo, Tokyo, Japan

    Ryuichiro Nakato & Katsuhiko Shirahige

Authors
  1. Ryuichiro Nakato
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  2. Katsuhiko Shirahige
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Corresponding author

Correspondence to Ryuichiro Nakato .

Editor information

Editors and Affiliations

  1. Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, Milano, Italy

    Marco Muzi-Falconi

  2. Donnelly Centre and Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada

    Grant W Brown

Appendix

Appendix

  1. 1.

    Script for the visualization of human (data obtained from the Sequence Read Archive (SRA) under accession SRP006944)

# convert map files to bin data $ for sample in Control H3K27me3 H3K36me3 H3K4me3 H3K9me3 Pol2_b; do $ for bin in 100 1000 100000;do parse2wig -f BAM -i HeLa-S3_$sample-n2-m1-hg19.sort.bam -o $sample -gt genome_table -binsize $bin; done $ done # make pdf files $ IP1="parse2wigdir/Pol2_b" $ IP2="parse2wigdir/H3K4me3" $ IP3="parse2wigdir/H3K27me3" $ IP4="parse2wigdir/H3K36me3" $ IP5="parse2wigdir/H3K9me3" $ Input="parse2wigdir/Control" # Figure 2a $ s1="-i $IP1,$Input,Pol2_b,,,100" $ s2="-i $IP2,$Input,H3K4me3,,,80" $ s3="-i $IP3,$Input,H3K27me3,,1000,60" $ s4="-i $IP4,$Input,H3K36me3,,1000,60" $ drompa_draw PC_SHARP -gt genome_table -gene refFlat.txt $s1 $s2 $s3 $s4 -p Fig 2a -lpp 1 -chr 1 -ls 1000 -rmchr -show_itag 2 # Figure 2b $ s1="-i $IP2,$Input,H3K4me3" $ s2="-i $IP3,$Input,H3K27me3" $ s3="-i $IP4,$Input,H3K36me3" $ s4="-i $IP5,$Input,H3K9me3" $ drompa_draw GV -gt genome_table $s1 $s2 $s3 $s4 -p Fig 2b_liner -GC GCcontents -gcsize 500000 -GD genedensity -gdsize 500000 $ drompa_draw GV -gt genome_table $s1 $s2 $s3 $s4 -p Fig 2b_log -GC GCcontents -gcsize 500000 -GD genedensity -gdsize 500000 -showratio 2

  1. 2.

    Script for the visualization of S. cerevisiae (data obtained from SRA under accession SRP009385)

$ gt=genome_table_sacCer3 $ index=UCSC-sacCer3-cs # bowtie index for colorspace data # read mapping and parse2wig $ for num in $(seq 398609 398624); do $ prefix=SRR$num $ bowtie -C $index $prefix.fastq -p8 -S > $prefix.sam $ parse2wig -f SAM -i $prefix.sam -o $prefix -gt $gt $done # generate pdf files $ dir=parse2wigdir $ IP1_60="$dir/SRR398612$postfix" # YST1019 Gal 60min $ IP1_0="$dir/SRR398611$postfix" # YST1019 Gal 0min $ IP2_60="$dir/SRR398610$postfix" # YST1019 Raf 60min $ IP2_0="$dir/SRR398609$postfix" # YST1019 Raf 0min $ IP3_60="$dir/SRR398616$postfix" # YST1053 Gal 60min $ IP3_0="$dir/SRR398615$postfix" # YST1053 Gal 0min $ IP4_60="$dir/SRR398614$postfix" # YST1053 Raf 60min $ IP4_0="$dir/SRR398613$postfix" # YST1053 Raf 0min $ IP5_60="$dir/SRR398620$postfix" # YST1076 Gal 60min $ IP5_0="$dir/SRR398618$postfix" # YST1076 Gal 0min $ IP6_60="$dir/SRR398619$postfix" # YST1076 Raf 60min $ IP6_0="$dir/SRR398617$postfix" # YST1076 Raf 0min $ IP7_60="$dir/SRR398624$postfix" # YST1287 Gal 60min $ IP7_0="$dir/SRR398623$postfix" # YST1287 Gal 0min $ IP8_60="$dir/SRR398622$postfix" # YST1287 Raf 60min $ IP8_0="$dir/SRR398621$postfix" # YST1287 Raf 0min $ s1="-i $IP1_60,$IP1_0,YST1019_Gal" $ s2="-i $IP2_60,$IP2_0,YST1019_Raf" $ s3="-i $IP3_60,$IP3_0,YST1053_Gal" $ s4="-i $IP4_60,$IP4_0,YST1053_Raf" $ s5="-i $IP5_60,$IP5_0,YST1076_Gal" $ s6="-i $IP6_60,$IP6_0,YST1076_Raf" $ s7="-i $IP7_60,$IP7_0,YST1287_Gal" $ s8="-i $IP8_60,$IP8_0,YST1287_Raf" $ drompa_draw PC_ENRICH -p SRP009385 -gt $gt -ars ARS-oriDB_scer.txt -gene SGD_features.tab -gftype 3 $s1 $s2 $s3 $s4 $s5 $s6 $s7 $s8 -ls 250 -lpp 2 -scale_ratio 1 -bn 3 -ystep 14 -ethre 1.5

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Nakato, R., Shirahige, K. (2018). Statistical Analysis and Quality Assessment of ChIP-seq Data with DROMPA. In: Muzi-Falconi, M., Brown, G. (eds) Genome Instability. Methods in Molecular Biology, vol 1672. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7306-4_41

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  • DOI: https://doi.org/10.1007/978-1-4939-7306-4_41

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7305-7

  • Online ISBN: 978-1-4939-7306-4

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