1. ChIP-seq analysis
Luca Cozzuto
BioinformaticsCore

2. 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
•…

3. ChIP-seq analysis
Typical workflow

4. ChIP-seq analysis
Typical workflow

5. ChIP-seq analysis
Starting 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
@HWUSI-EAS621:69:64EKPAAXX:3:1:11477:1265 1:N:0: @(HEADER)
GAAACTTGAGGACTGCCCAGCTCGACAGACACTGGA
(SEQUENCE)
+ +(HEADER)
GEGGDGG@GGDGGGGGGGBDGGDG8GG@3D6:3:67
(QUALITY)
The quality is encoded with a ASCII character and
represents the Phred quality score.
p = probability that that base call is incorrect
Q = 20 means base call accuracy of 99%

6. ChIP-seq analysis
Starting the analysis.
• It is strongly recommended to check the quality of the
sequences we received before doing the analysis!
Fastqc analysis

7. ChIP-seq analysis
Starting the analysis.
Mapping by using ultra-fast mappers:
• GEM
• Bowtie
• BWA
• Stampy
It is required to index the reference genome before doing
the analysis.

8. ChIP-seq analysis
Peak calling – MACS
Model-based Analysis of ChIP-Seq data.
TF

9. ChIP-seq analysis
Peak calling – MACS
Sequences from IP
TF

10. ChIP-seq analysis
Peak calling – MACS
Sequences from IP
TF
Sequenced tags on
+ strand
- strand

11. ChIP-seq analysis
Peak calling - MACS

12. ChIP-seq analysis
Peak 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).

13. ChIP-seq analysis
Peak calling – MACS
MACS select at least 1,000 “model peaks” for calculating the distance
“d” between paired peaks.

14. ChIP-seq analysis
Peak 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.

15. ChIP-seq analysis
Peak calling – MACS
Example:
Tag count = 2
Number of reads = 30,000,000
Read length = 36
Mappable human genome = 2,700,000,000

16. ChIP-seq analysis
Peak calling – MACS
Example:
Tag count = 10
Number of reads = 30,000,000
Read length = 36
Mappable human genome = 2,700,000,000

17. ChIP-seq analysis
Peak 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

18. ChIP-seq analysis
Peak 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.
Fold enrichment =
Enrichment over the
λlocal

19. ChIP-seq analysis
Peak 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!

20. ChIP-seq analysis
Practical part

21. ChIP-seq analysis
Practical part
Connect to the Etna machine by using ssh.
• MAC or Linux users can do using this command
$ ssh –X course@xxx.crg.es
course@xxx.crg.es'spassword:
Password:xxxxxxx
• Windows users should first download Putty and PSCP
programs and then use them for accessing that
machine. http://goo.gl/4BWud

22. ChIP-seq analysis
course@xxx.crg.es
Password:xxxxxx

23. ChIP-seq analysis
Different formats can be used as input files:
BED, ELAND, SAM, BAM, BOWTIE and for paired ends
ELAND-MULTIPET
$ head ../data/Input_tags.bed
chr1 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 -
Bed fields: chromosome name, start, end, name, score
strand

24. ChIP-seq analysis
Launching 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 -ghs-n FoxA1

25. ChIP-seq analysis
Check the output printed to the screen.
$macs14 -t ../data/Treatment_tags.bed -c ../data/Input_tags.bed -ghs -n FoxA1
INFO @ 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.

26. ChIP-seq analysis
Check 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: 11861
INFO @ 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!

27. ChIP-seq analysis
Output files
•FoxA1_model.r
• FoxA1_negative_peaks.xls
• FoxA1_peaks.bed
• FoxA1_peaks.xls
• FoxA1_summits.bed

28. ChIP-seq analysis
MACS peak model
$R --vanilla < FoxA1_model.r
..
$evince FoxA1_model.pdf

29. ChIP-seq analysis
FoxA1_peaks.xls
-
10*LOG10 fold_enri
chr start end length summit tags (pvalue) chment FDR(%)
chr1 858357 858641 285 128 6 51 13.93 4.09
chr1 998955 999229 275 106 9 74.39 18.28 0.26
chr1 1050021 1050286 266 154 13 152 52.23 0
chr1 1684288 1684577 290 176 9 89.7 32.14 0.01
chr1 1775031 1775371 341 270 6 51.08 16.71 4.06
chr1 1780682 1780965 284 183 6 61.17 19.9 1.45
FoxA1_negative_peaks.xls
-
10*LOG1 fold_enric
chr start end length summit tags 0(pvalue) hment
chr1 7155010 7155530 521 311 9 61.64 44.47
chr1 11265816 11266025 210 106 6 59.86 38.12
chr1 18597004 18597307 304 188 8 66.25 31.77
chr1 33412779 33412964 186 94 6 58.68 22.92
chr1 33759125 33759514 390 234 9 62.88 19.77
chr1 37102727 37102952 226 114 6 55.14 31.51

30. ChIP-seq analysis
FoxA1_peaks.bed
chr, start, end, peak id and score = -10*LOG10(pvalue)
chr1 858356 858641 MACS_peak_1 51
chr1 998954 999229 MACS_peak_2 74.39
chr1 1050020 1050286 MACS_peak_3 152
chr1 1684287 1684577 MACS_peak_4 89.7
chr1 1775030 1775371 MACS_peak_5 51.08
chr1 1780681 1780965 MACS_peak_6 61.17
chr1 1923146 1923449 MACS_peak_7 164.87
FoxA1_summits.bed
chr, start, end, peak id and score = height of the summit
chr1 858483 858484 MACS_peak_1 4
chr1 999059 999060 MACS_peak_2 7
chr1 1050173 1050174 MACS_peak_3 12
chr1 1684462 1684463 MACS_peak_4 8
chr1 1775299 1775300 MACS_peak_5 4
chr1 1780863 1780864 MACS_peak_6 4
chr1 1923347 1923348 MACS_peak_7 14

31. ChIP-seq analysis
$macs14 -t ../data/Treatment_tags.bed -c ../data/Input_tags.bed -ghs -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

32. ChIP-seq analysis
Upload files in the UCSC genome browser
http://genome.ucsc.edu/index.html

33. ChIP-seq analysis
Upload files in the UCSC genome browser
http://genome.ucsc.edu/index.html

34. ChIP-seq analysis
Upload files in the UCSC genome browser
http://genome.ucsc.edu/index.html

35. ChIP-seq analysis
Upload files in the UCSC genome browser
http://genome.ucsc.edu/index.html

36. ChIP-seq analysis
Upload files in the UCSC genome browser

37. ChIP-seq analysis
Upload files in the UCSC genome browser
Peak example: chr22:20141500..20141987

38. ChIP-seq analysis
Analyze 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.

39. ChIP-seq analysis
Upload files in the UCSC genome browser
Peak example: chrX:47,922,749-47,926,228

40. ChIP-seq analysis
Galaxy platform
Soon a local installation at CRG!!!
https://main.g2.bx.psu.edu/

41. ChIP-seq analysis
Bibliography:
• 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=The_GEM_libra
ry
• 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