ChIP-seq Theory
•
3 likes•2,660 views
This document provides an overview of next generation sequencing analysis for ChIP-seq experiments. It describes the basic ChIP-seq workflow including performing ChIP, preparing libraries, sequencing, alignment, filtering duplicates, peak calling, and downstream analysis. It also reviews several common peak finding tools and demonstrates how to perform ChIP-seq analysis using the USeq toolset, including running commands and visualizing results.
More Related Content
What's hot
What's hot (20)
Viewers also liked
Similar to ChIP-seq Theory
Similar to ChIP-seq Theory (20)
More from Bioinformatics and Computational Biosciences Branch
More from Bioinformatics and Computational Biosciences Branch (20)
Recently uploaded
Recently uploaded (20)
ChIP-seq Theory
- 1. Next Generation Sequencing Analysis Series February 11, 2015 Andrew Oler, PhD High-throughput Sequencing Bioinformatics Specialist BCBB/OCICB/NIAID/NIH
- 2. Bioinformatics and Computational Biosciences Branch § “BCBB” § Group of ~30 § Bioinformatics Software Developers § Computational Biologists § Project Managers & Analysts http://www.niaid.nih.gov/about/organization/odoffices/omo/ocicb/Pages/bcbb.aspx 2
- 3. The plan… § Overview of ChIP-seq technology and concepts § Comparison of peak finding tools § Hands-on – USeq § Peak finding § Peak annotation § Visualization (in IGB) § More demos as time permits 3
- 4. Why are people interested in protein-DNA contacts? § Proteins called transcription factors (TFs) are involved in regulation of gene activation § The first step in gene activation is binding of the TF to its target gene. Gene X RNA Polymerase TF
- 5. Chromatin Immunoprecipitation (ChIP) See also: ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 2012 Sep;22(9):1813-31
- 6. Steps for ChIP-seq Analysis 1. Perform ChIP and prepare ChIP library. 2. Prepare separate library of input DNA from the same sonicate. 3. Sequence both libraries (at least 20 million reads per library). 4. Align reads to the reference genome 5. Remove duplicate alignments 6. Shift data and call peaks 7. Downstream analysis 6 Sonicate Input DNA ChIP Eluate DNA
- 7. Short Read Alignment CTCTGCACGCGTGGGTTCGAATCCCACCTTCGTCGA! Coordinate: chr6 27,373,801 chr6
- 8. Empirical Determination of Peak Shift Valouev et al., Nature Methods 2008 • Peak determined for each strand alignments • Distance between peaks is “Peak Shift” • Center-shift data half the Peak Shift distance (e.g., USeq) • Alternatively, extend 3’ end to recreate the original fragments (e.g., MACS)
- 9. Define Regions of Enrichment • Determine enrichment of reads in ChIP library versus control input library • Set statistical false discovery rate (FDR) threshold, • e.g., 5% FDR = 5 in 100 peaks false positives • Output • Interval data • (e.g., chr, start, stop) • Browser graph file 9 Control for non-specific peaks due to “open” chromatin Park, Nat Rev Genet, 2009
- 10. ChIP-seq Downstream Analysis 10 Supplemental Table 2: D1 Histone-enriched loci (Illumina GAII FDR< 0.0001) Go Category Total Genes Changed Genes Enrichment FDR Cell fate commitment 75 60 1.59848 0 Sequence-specific DNA binding 424 337 1.588112 0 Cellular morphogenesis during differentiation 125 99 1.582495 0 Cell projection organization and biogenesis 169 131 1.548823 0 Cell part morphogenesis 169 131 1.548823 0 Embryonic morphogenesis 88 68 1.543986 0 Regionalization 82 63 1.535126 0 Neurogenesis 221 168 1.518918 0 Wnt receptor signaling pathway 107 80 1.493907 0 Regulation of cell differentiation 119 88 1.477587 0 Regulation of transcription from RNA polymerase II promoter 99 72 1.453164 0 Organ morphogenesis 304 221 1.452566 0 Embryonic development 226 164 1.449949 0 Regulation of developmental process 191 138 1.443653 0 Voltage-gated ion channel activity 171 123 1.43723 0 Nervous system development 604 433 1.432413 0 Cation channel activity 228 162 1.419703 0 Transcription factor activity 791 552 1.394376 0 Muscle development 136 94 1.38104 0 # Peaks Found in Different Tissues Allele-specific Binding Oler et al., NSMB, 2010; Mikkelsen et al., Nature, 2007; Park, Nat Rev Genet, 2009; Barski et al., Cell, 2007; Hammoud et al., Nature, 2009
- 11. Peak finding tools § CisGenome § FindPeaks § PeakSeq § ChIPseqR § PICS § F-Seq § GLITR § MACS § QuEST • SISSRS • USeq • Hpeak • SICER • ERANGE • ChromSig • Partek • Genomatix • CLC Bio
- 12. ChIP-seq Analysis with USeq § ChIPSeq wrapper • SamParser (converts SAM to PointData) • FilterDuplicateReads • ReadCoverage • PeakShiftFinder • MultipleReplicaScanSeqs or ScanSeqs (genome-wide windowed statistical analysis) § MRSS uses DESeq2 package, SS uses Q-value package • EnrichedRegionMaker § Other Applications • DefinedRegionScanSeqs • MultipleReplicaDefinedRegionScanSeqs • AggregatePlotter • IntersectRegions • FindNeighboringGenes 12
- 13. ChIPSeq options Options: -s Save directory -t Treatment directory containing aligned ChIP reads -c Control directory containing aligned Input reads -y Type of alignments (e.g., sam, bed, eland, novoalign) -v Genome version (e.g., H_sapiens_Feb_2009), see http://genome.ucsc.edu/FAQ/FAQreleases -r Full path to R containing DESeq and Qvalue packages -m Single replica analysis (run ScanSeqs instead of MultipleReplicaScanSeqs) -f File to use to filter out known false positives (e.g., Satellites) Command: java -Xmx3G -jar ~/bio_apps/USeq/Apps/ChIPSeq -s ChIPSeq_out -t Pol2 -c Input -y sam -v H_sapiens_Feb_2009 -r ~/bio_apps/R/bin/R -m -f hg19_Satellites.bed.gz Exercise 1: cd ~/chipseq qsub test_useq_chipseq.sh 13
- 14. Other Demos 1. Run ChIPSeq in USeq GUI • No command-line required 2. Run FindNeighboringGenes in USeq GUI • Find distance to nearest gene, or all genes within a neighborhood 3. View peaks in IGB 4. Run AggregatePlotter in USeq GUI • Make a class average map, e.g., 1kb window around transcription start sites 14
- 15. Thank You For questions or comments please contact: andrew.oler@nih.gov ScienceApps@niaid.nih.gov 15