macs course
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ChIP-seq analysis
Luca CozzutoBioinformatics Core
ChIP-seq analysis
• ChIP-seq is the combination of chromatin immuno-precipitation with ultra-sequencing.
• Allows to detect genomic portions bound by proteins such as:
• Transcription factors• Histones• Polymerase II • …
ChIP-seq analysisTypical workflow
ChIP-seq analysisTypical workflow
ChIP-seq analysisStarting the analysis.
• Typically you will receive from 10 to 30 millions of raw reads per sample corresponding to a zipped file of 0.5-1.5 Gbytes.
FASTQ format
The quality is encoded with a ASCII character and represents the Phred quality score.
p = probability that that base call is incorrectQ = 20 means base call accuracy of 99%
@HWUSI-EAS621:69:64EKPAAXX:3:1:11477:1265 1:N:0:@(HEADER)GAAACTTGAGGACTGCCCAGCTCGACAGACACTGGA
(SEQUENCE)+
+(HEADER)GEGGDGG@GGDGGGGGGGBDGGDG8GG@3D6:3:67
(QUALITY)
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Q = −10log10 p
ChIP-seq analysisStarting the analysis.
• It is strongly recommended to check the quality of the sequences we received before doing the analysis!
Fastqc analysis
ChIP-seq analysisStarting the analysis.
Mapping by using ultra-fast mappers:
• GEM • Bowtie• BWA• Stampy
It is required to index the reference genome before doing the analysis.
ChIP-seq analysisPeak calling – MACS
Model-based Analysis of ChIP-Seq data.
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ChIP-seq analysisPeak calling – MACS
Sequences from IP
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ChIP-seq analysisPeak calling – MACS
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Sequences from IP
Sequenced tags on + strand- strand
ChIP-seq analysisPeak calling - MACS
ChIP-seq analysisPeak calling – MACS
Given a sonication size (bandwith) and a fold-enrichment (mfold), MACS slides 2*bandwidth windows across the genome to find regions enriched to a random tag genome distribution >= mfold (default between 10 and 30).
ChIP-seq analysisPeak calling – MACS
MACS select at least 1,000 “model peaks” for calculating the distance “d” between paired peaks.
ChIP-seq analysisPeak calling – MACS
How to determine if peaks are greater than expected by chance?
• x = observed read number• λ= expected read number
Probability to find a peak higher than x.
Tag distribution along the genome could be modeled by a Poisson distribution.
ChIP-seq analysisPeak calling – MACS
Example:Tag count = 2 Number of reads = 30,000,000Read length = 36Mappable human genome = 2,700,000,000
ChIP-seq analysisPeak calling – MACS
Example:Tag count = 10 Number of reads = 30,000,000Read length = 36Mappable human genome = 2,700,000,000
ChIP-seq analysisPeak calling – MACS• shifting each tag d/2 to the 3’• sliding windows with 2*d length across the
genome to detect the enriched regions (Poisson distribution p-value <= 1e-5).
• Overlapping enriched regions are fused.• Summit of the peak is considered the putative
binding site
TF
ChIP-seq analysisPeak calling – MACS
In order to address local biases in the genome such as local chromatin structure, sequencing bias, genome copy number variation… MACS evaluates candidates peaks by comparing them against a “local” distribution.
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λlocal =max(λ BG,λ1k,λ 5k,λ10k )
Fold enrichment =Enrichment over the λlocal
ChIP-seq analysisPeak calling – MACS
False Discovery Rate (FDR) is calculated as number of control peaks called / number of sample peaks. Control peaks are calculated by swapping control and sample.
FDR is calculated only when a control is provided!
ChIP-seq analysisPractical part
ChIP-seq analysisPractical part
Connect to the Etna machine by using ssh.
• MAC or Linux users can do using this command
Password: xxxxxxx
• Windows users should first download Putty and PSCP programs and then use them for accessing that machine. http://goo.gl/4BWud
$ ssh –X [email protected]@xxx.crg.es's password:
ChIP-seq analysisDifferent formats can be used as input files: BED, ELAND, SAM, BAM, BOWTIE and for paired ends ELAND-MULTIPET
Bed fields: chromosome name, start, end, name, score strand
$ head ../data/Input_tags.bedchr1 233604 233639 0 2 -chr1 559767 559802 0 3 +chr1 742600 742635 0 2 +chr1 742600 742635 0 0 +chr1 744231 744266 0 0 +chr1 744307 744342 0 2 -chr1 746885 746920 0 2 +chr1 746958 746993 0 1 +chr1 748226 748261 0 2 +chr1 748357 748392 0 0 -
ChIP-seq analysisLaunching MACS passing the sample, the control, the genome size (hs = homo sapiens) and the name
$macs14 -t ../data/Treatment_tags.bed -c ../data/Input_tags.bed -g hs -n FoxA1
ChIP-seq analysisCheck the output printed to the screen.
$macs14 -t ../data/Treatment_tags.bed -c ../data/Input_tags.bed -g hs -n FoxA1INFO @ Thu, 29 Mar 2012 14:58:35: # ARGUMENTS LIST:# name = FoxA1# format = AUTO# ChIP-seq file = ./Treatment_tags.bed# control file = ./Input_tags.bed# effective genome size = 2.70e+09# band width = 300# model fold = 10,30# pvalue cutoff = 1.00e-05# Small dataset will be scaled towards larger dataset.# Range for calculating regional lambda is: 1000 bps and 10000 bps INFO @ Thu, 29 Mar 2012 14:58:35: #1 read tag files... INFO @ Thu, 29 Mar 2012 14:58:35: #1 read treatment tags... INFO @ Thu, 29 Mar 2012 14:58:35: Detected format is: BED
Regional lambda has two values in this version: small to consider bias around the summit and large for the surrounding area.
ChIP-seq analysisCheck the output printed to the screen.
INFO @ Thu, 29 Mar 2012 14:59:41: #1 tag size is determined as 35 bps INFO @ Thu, 29 Mar 2012 14:59:41: #1 tag size = 35 INFO @ Thu, 29 Mar 2012 14:59:41: #1 total tags in treatment: 3909805..INFO @ Thu, 29 Mar 2012 14:59:46: #2 Build Peak Model... INFO @ Thu, 29 Mar 2012 15:00:00: #2 number of paired peaks: 11861INFO @ Thu, 29 Mar 2012 15:00:00: #2 finished! INFO @ Thu, 29 Mar 2012 15:00:00: #2 predicted fragment length is 119 bps INFO @ Thu, 29 Mar 2012 15:00:00: #2.2 Generate R script for model : FoxA1_model.r INFO @ Thu, 29 Mar 2012 15:00:00: #3 Call peaks... INFO @ Thu, 29 Mar 2012 15:00:00: #3 shift treatment data INFO @ Thu, 29 Mar 2012 15:00:01: #3 merge +/- strand of treatment data INFO @ Thu, 29 Mar 2012 15:00:01: #3 call peak candidates INFO @ Thu, 29 Mar 2012 15:00:13: #3 shift control data INFO @ Thu, 29 Mar 2012 15:00:13: #3 merge +/- strand of control data INFO @ Thu, 29 Mar 2012 15:00:15: #3 call negative peak candidates INFO @ Thu, 29 Mar 2012 15:00:25: #3 use control data to filter peak candidates... INFO @ Thu, 29 Mar 2012 15:00:31: #3 Finally, 13591 peaks are called!INFO @ Thu, 29 Mar 2012 15:00:31: #3 find negative peaks by swapping treat and control INFO @ Thu, 29 Mar 2012 15:00:36: #3 Finally, 594 peaks are called!
ChIP-seq analysisOutput files
• FoxA1_model.r
• FoxA1_negative_peaks.xls
• FoxA1_peaks.bed
• FoxA1_peaks.xls
• FoxA1_summits.bed
ChIP-seq analysisMACS peak model
$R --vanilla < FoxA1_model.r..$evince FoxA1_model.pdf
ChIP-seq analysisFoxA1_peaks.xls
FoxA1_negative_peaks.xls
chr start end length summit tags
-10*LOG10(pvalue)
fold_enrichment FDR(%)
chr1 858357 858641 285 128 6 51 13.93 4.09chr1 998955 999229 275 106 9 74.39 18.28 0.26chr1 1050021 1050286 266 154 13 152 52.23 0chr1 1684288 1684577 290 176 9 89.7 32.14 0.01chr1 1775031 1775371 341 270 6 51.08 16.71 4.06chr1 1780682 1780965 284 183 6 61.17 19.9 1.45
chr start end length summit tags
-10*LOG10(pvalue)
fold_enrichment
chr1 7155010 7155530 521 311 9 61.64 44.47chr1 11265816 11266025 210 106 6 59.86 38.12chr1 18597004 18597307 304 188 8 66.25 31.77chr1 33412779 33412964 186 94 6 58.68 22.92chr1 33759125 33759514 390 234 9 62.88 19.77chr1 37102727 37102952 226 114 6 55.14 31.51
ChIP-seq analysisFoxA1_peaks.bedchr, start, end, peak id and score = -10*LOG10(pvalue)
FoxA1_summits.bedchr, start, end, peak id and score = height of the summit
chr1 858356 858641 MACS_peak_1 51chr1 998954 999229 MACS_peak_2 74.39chr1 1050020 1050286 MACS_peak_3 152chr1 1684287 1684577 MACS_peak_4 89.7chr1 1775030 1775371 MACS_peak_5 51.08chr1 1780681 1780965 MACS_peak_6 61.17chr1 1923146 1923449 MACS_peak_7 164.87
chr1 858483 858484 MACS_peak_1 4chr1 999059 999060 MACS_peak_2 7chr1 1050173 1050174 MACS_peak_3 12chr1 1684462 1684463 MACS_peak_4 8chr1 1775299 1775300 MACS_peak_5 4chr1 1780863 1780864 MACS_peak_6 4chr1 1923347 1923348 MACS_peak_7 14
ChIP-seq analysis
$macs14 -t ../data/Treatment_tags.bed -c ../data/Input_tags.bed -g hs -n FoxA1 -w
-w option allows to create“wiggle” files for each chromosome analyzed.
-B option creates “bedgraph” files. -S option together with either –w or –B creates a single huge file for the whole genome.
--space=NUM can be used for change the resolution of the wiggle file
ChIP-seq analysisUpload files in the UCSC genome browserhttp://genome.ucsc.edu/index.html
ChIP-seq analysisUpload files in the UCSC genome browserhttp://genome.ucsc.edu/index.html
ChIP-seq analysisUpload files in the UCSC genome browserhttp://genome.ucsc.edu/index.html
ChIP-seq analysisUpload files in the UCSC genome browserhttp://genome.ucsc.edu/index.html
ChIP-seq analysisUpload files in the UCSC genome browser
ChIP-seq analysisUpload files in the UCSC genome browserPeak example: chr22:20141500..20141987
ChIP-seq analysisAnalyze histone modifications
• Broader peaks• No clear shape (more summits)• The peak model is often impossible to create.
$macs14 -t ../data/ES.H3K27me3.bed –g mm --nomodel --nolambda -n H3K27me3
• It is recommended to skip the model with the --nomodel option.
• Since no control is available the comparison will be done against the sample background. It is recommended to skip the local background when you have no control and very broad peaks.
ChIP-seq analysisUpload files in the UCSC genome browserPeak example: chrX:47,922,749-47,926,228
ChIP-seq analysisGalaxy platformSoon a local installation at CRG!!!
https://main.g2.bx.psu.edu/
ChIP-seq analysisBibliography:
• http://en.wikipedia.org/wiki/File:ChIP-sequencing.svg
• http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html
• http://liulab.dfci.harvard.edu/MACS/
• http://sourceforge.net/apps/mediawiki/gemlibrary/index.php?title=Th
e_GEM_library
• http://bio-bwa.sourceforge.net/
• http://www.well.ox.ac.uk/project-stampy
• http://bowtie-bio.sourceforge.net/index.shtml
• http://genome.ucsc.edu/
• http://www.r-project.org/
• http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
• https://main.g2.bx.psu.edu/root