The document discusses the typical analysis pipeline and challenges for ChIP-seq data, which involves mapping millions of sequencing reads to the genome, detecting protein binding sites (peaks), normalizing data for comparison, and visualizing thousands of enriched regions. It provides an overview of common tools for each step, including Bowtie for mapping, MACS for peak calling, and CEAS or UCSC Genome Browser for visualization. An example analysis workflow is shown that filters read qualities and parses the data.

1. Tools and challenges for
ChIP-seq data analysis
Alba Jené Sanz
Biomedical Genomics Lab (UPF)

2. Overview
1. ChIP-seq – The basics
2. Typical pipeline
3. Challenges in ChIP-seq data analysis
4. To take into account
5. Available tools
6. Analysis example
7. Future Challenges
8. Where to look for help

3. 1. ChIP-seq – The Basics

4. 1. ChIP-seq – The Basics
ChIP-on-chip
ChIP-seq

5. 1. ChIP-seq – The Basics
ChIP-on-chip
Bioinformatics
ChIP-seq

6. 1. ChIP-seq – The Basics
35 bp
500 bp 35 bp

7. 1. ChIP-seq – The Basics

8. 2. Typical pipeline

9. 2. Typical pipeline

10. 2. Typical pipeline
Bowtie

11. 2. Typical pipeline
Bowtie MACS

12. 2. Typical pipeline
Bowtie MACS
CEAS

13. 2. Typical pipeline
Mapping…
Unique / multiple locations
Allowing mismatches – seed sequence
Balance accuracy / performance
Peak calling…

14. 2. Typical pipeline

15. 3. Challenges in ChIP-seq data analysis
Millions of segments that need a fast mapping to the genome (allowing
mismatches or gaps, performance issues)
Peak detection – find the exact binding site
Data normalization – compare results, background noise
Visualization – thousands of enriched regions. UCSC, JBrowse…

16. 4. To take into account
Transcription Factors vs Nucleosomes / Histone modifications
Control available?
Sequencing depth bias in Control vs IP
Different alignment methods produce different peak calling results, but the difference is
not as much as the one due to different peak caller or replicate
Many differences on peak callers can be explained by the different thresholds used
Some peak callers may be specific to some data types
Consistency may be used to set threshold if replicates are available

17. 4. To take into account
There are many tools for the analysis of ChIP-
seq data, but no standards yet

18. 5. Available tools

19. 5. Available tools

20. 5. Available tools

21. 5. Available tools

22. 5. Available tools
Uses regional averaging to mitigate sample fluctuations in the control library
Uses the control to model the distribution across the genome using the Poisson
distribution (BG). After identifying candidate peaks significantly enriched over the
BG, a local labda is estimated using windows around each peak to eliminate local
biases
Open-source, open to contributions (Artistic License) and being actively
improved
Easy to use and fast-responding developers
Compares very well to other methods

23. 6. Analysis example
QSEQ files (Solexa's FASTQ with ASCII Phred64, the 3rd FASTQ type)
lane5_SNAIL_F9_qseq.txt
SOLEXA 90320 5 1 0 476 0 1 .ACGGGGGAGGG.C...CAAC..A...C............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 1222 0 1 .AATTGAAAAAT.A..TTTAA..G...A............ DO[[XVX[BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 133 0 1 .CCAGTCTATTAATT.TTGCC..GA..C............ DPXXXYYYYYYBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 145 0 1 .ATTGTTTCTGACTA.TTGAT..GC..T............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 153 0 1 .ACCGCTATCAGTAC.TAGCT..GT..A............ DMUYUVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 215 0 1 .TGTTGCCATTGCTA.AGGCA..GT..T............ DOVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 1 827 0 1 AGGAGATCGGCCGGTTGATGAGCCGAGTG........... Z__U_]PXYXTGRZ]QXBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 1 56 0 1 AAAAATCGACGCTCAAGTCAGAGGTGGCG........... ababba`_aabaab`UbaBBBBBBBBBBBBBBBBBBB 1
SOLEXA 90320 5 1 1 925 0 1 TGCAGCACTGGGGCCAGATGGTAAGCCCT........... _Z_T]`]M]OLP^^[`WBBBBBBBBBBBBBBBBBBBB 1
SOLEXA 90320 5 1 2 1637 0 1 GGGCTTCTGCCCCGGTGGGTACATGAGTA........... aaa`a`a`aa`aX_`^^^[``BBBBBBBBBBBBBBBBBB 1
@3_1_3_89
CACAGTGTCCTCCAGGTTCATCCC................
+3_1_3_89
@FC30C11AAXX:8:1:1649:1790
abbab^aaaaaaaVUVbBBBBBBBBBBBBBBBBBBBBBB
GAAAAGTATTTGCAATTTGTTGCCTCTCATCCAAGAATGAAATTCCTATTG
@3_1_3_762
+
GCAAACAAATGGCGGAAAGCGGCG................
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6776666666566663
+3_1_3_762
@FC30C11AAXX:8:1:1655:1811
aabba`b`a`]]TXOY`BBBBBBBBBBBBBBBBBBBBBBB
GAAGCAGAAGCCTATATACCCTGTAGAACTGGGAGCCAATTAAACCTCTTT
@3_1_90_512
+
GCTGAGGCAGGAGAATTGCTTGAACTGGGAAGGCAGAGGT
<<<<<<<<<<<<<<<<<<5<<<<><<:>:<<;<:;3665537.6+6.33.+
+3_1_90_512
@FC30C11AAXX:8:1:1609:1848
ab`a_a`X``WTGW]T]SZ]T[aXa_T^]XP]H_VXY
GATGTGGTTTCACATAAATTGACATATATAGTTCCAGGCTGTAAATGTTGT
@3_1_90_1028
+
GTTACGGCTTATCCTGCACATTACGACCGTTTGCGTAACG
<<;<<;;+<<7:<<<<<<7::7:<<<<<<:7,777402-4.-+*20+0-%-
+3_1_90_1028
@FC30C11AAXX:8:1:1667:1880
`bba`X_^ab_aWS`_b[`aa_]TZ^VYa`VW^`^b`a
GTTTTATACAAATCAAAACCATAGTGAGATACCATCTCACACTAGTCAGAA
@3_1_90_1651
+
TAATTTTAGATTTTATCCTTGACATTGTAAATATTACATT
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6767366767357566
+3_1_90_1651
@FC30C11AAXX:8:1:1577:1853
aUVF[aa`VU_`aaU[__aaaYV^aP`aQUa`_^_a
GTGATGGGAGGAAAGCTAGGGGGCTATAATGTCTATTACAAGGCTCAGTAG
@3_1_90_1670
+
<6<<<<<<<<<<<<<<<<<<<<<<<<<<<<:99:9646066,6+0604044

24. 6. Analysis example
QSEQ files (Solexa's FASTQ with ASCII Phred64, the 3rd FASTQ type)
lane5_SNAIL_F9_qseq.txt
SOLEXA 90320 5 1 0 476 0 1 .ACGGGGGAGGG.C...CAAC..A...C............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 1222 0 1 .AATTGAAAAAT.A..TTTAA..G...A............ DO[[XVX[BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 133 0 1 .CCAGTCTATTAATT.TTGCC..GA..C............ DPXXXYYYYYYBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 145 0 1 .ATTGTTTCTGACTA.TTGAT..GC..T............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 153 0 1 .ACCGCTATCAGTAC.TAGCT..GT..A............ DMUYUVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 215 0 1 .TGTTGCCATTGCTA.AGGCA..GT..T............ DOVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 1 827 0 1 AGGAGATCGGCCGGTTGATGAGCCGAGTG........... Z__U_]PXYXTGRZ]QXBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 1 56 0 1 AAAAATCGACGCTCAAGTCAGAGGTGGCG........... ababba`_aabaab`UbaBBBBBBBBBBBBBBBBBBB 1
SOLEXA 90320 5 1 1 925 0 1 TGCAGCACTGGGGCCAGATGGTAAGCCCT........... _Z_T]`]M]OLP^^[`WBBBBBBBBBBBBBBBBBBBB 1
SOLEXA 90320 5 1 2 1637 0 1 GGGCTTCTGCCCCGGTGGGTACATGAGTA........... aaa`a`a`aa`aX_`^^^[``BBBBBBBBBBBBBBBBBB 1
Filter qualities and parse
@3_1_3_89
CACAGTGTCCTCCAGGTTCATCCC................
+3_1_3_89
@FC30C11AAXX:8:1:1649:1790
abbab^aaaaaaaVUVbBBBBBBBBBBBBBBBBBBBBBB
GAAAAGTATTTGCAATTTGTTGCCTCTCATCCAAGAATGAAATTCCTATTG
@3_1_3_762
+
GCAAACAAATGGCGGAAAGCGGCG................
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6776666666566663
+3_1_3_762
@FC30C11AAXX:8:1:1655:1811
aabba`b`a`]]TXOY`BBBBBBBBBBBBBBBBBBBBBBB
GAAGCAGAAGCCTATATACCCTGTAGAACTGGGAGCCAATTAAACCTCTTT
@3_1_90_512
+
GCTGAGGCAGGAGAATTGCTTGAACTGGGAAGGCAGAGGT
<<<<<<<<<<<<<<<<<<5<<<<><<:>:<<;<:;3665537.6+6.33.+
+3_1_90_512
@FC30C11AAXX:8:1:1609:1848
ab`a_a`X``WTGW]T]SZ]T[aXa_T^]XP]H_VXY
GATGTGGTTTCACATAAATTGACATATATAGTTCCAGGCTGTAAATGTTGT
@3_1_90_1028
+
GTTACGGCTTATCCTGCACATTACGACCGTTTGCGTAACG
<<;<<;;+<<7:<<<<<<7::7:<<<<<<:7,777402-4.-+*20+0-%-
+3_1_90_1028
@FC30C11AAXX:8:1:1667:1880
`bba`X_^ab_aWS`_b[`aa_]TZ^VYa`VW^`^b`a
GTTTTATACAAATCAAAACCATAGTGAGATACCATCTCACACTAGTCAGAA
@3_1_90_1651
+
TAATTTTAGATTTTATCCTTGACATTGTAAATATTACATT
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6767366767357566
+3_1_90_1651
@FC30C11AAXX:8:1:1577:1853
aUVF[aa`VU_`aaU[__aaaYV^aP`aQUa`_^_a
GTGATGGGAGGAAAGCTAGGGGGCTATAATGTCTATTACAAGGCTCAGTAG
@3_1_90_1670
+
<6<<<<<<<<<<<<<<<<<<<<<<<<<<<<:99:9646066,6+0604044

25. 6. Analysis example
QSEQ files (Solexa's FASTQ with ASCII Phred64, the 3rd FASTQ type)
lane5_SNAIL_F9_qseq.txt
SOLEXA 90320 5 1 0 476 0 1 .ACGGGGGAGGG.C...CAAC..A...C............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 1222 0 1 .AATTGAAAAAT.A..TTTAA..G...A............ DO[[XVX[BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 133 0 1 .CCAGTCTATTAATT.TTGCC..GA..C............ DPXXXYYYYYYBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 145 0 1 .ATTGTTTCTGACTA.TTGAT..GC..T............ BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 153 0 1 .ACCGCTATCAGTAC.TAGCT..GT..A............ DMUYUVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 0 215 0 1 .TGTTGCCATTGCTA.AGGCA..GT..T............ DOVWBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 1 827 0 1 AGGAGATCGGCCGGTTGATGAGCCGAGTG........... Z__U_]PXYXTGRZ]QXBBBBBBBBBBBBBBBBBBBBBB 0
SOLEXA 90320 5 1 1 56 0 1 AAAAATCGACGCTCAAGTCAGAGGTGGCG........... ababba`_aabaab`UbaBBBBBBBBBBBBBBBBBBB 1
SOLEXA 90320 5 1 1 925 0 1 TGCAGCACTGGGGCCAGATGGTAAGCCCT........... _Z_T]`]M]OLP^^[`WBBBBBBBBBBBBBBBBBBBB 1
SOLEXA 90320 5 1 2 1637 0 1 GGGCTTCTGCCCCGGTGGGTACATGAGTA........... aaa`a`a`aa`aX_`^^^[``BBBBBBBBBBBBBBBBBB 1
Filter qualities and parse
@3_1_3_89
CACAGTGTCCTCCAGGTTCATCCC................
+3_1_3_89
@FC30C11AAXX:8:1:1649:1790
abbab^aaaaaaaVUVbBBBBBBBBBBBBBBBBBBBBBB
GAAAAGTATTTGCAATTTGTTGCCTCTCATCCAAGAATGAAATTCCTATTG
@3_1_3_762
+
GCAAACAAATGGCGGAAAGCGGCG................
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6776666666566663
+3_1_3_762
@FC30C11AAXX:8:1:1655:1811
aabba`b`a`]]TXOY`BBBBBBBBBBBBBBBBBBBBBBB
GAAGCAGAAGCCTATATACCCTGTAGAACTGGGAGCCAATTAAACCTCTTT
@3_1_90_512
+
GCTGAGGCAGGAGAATTGCTTGAACTGGGAAGGCAGAGGT
<<<<<<<<<<<<<<<<<<5<<<<><<:>:<<;<:;3665537.6+6.33.+
+3_1_90_512
@FC30C11AAXX:8:1:1609:1848
ab`a_a`X``WTGW]T]SZ]T[aXa_T^]XP]H_VXY
@3_1_90_1028
GTTACGGCTTATCCTGCACATTACGACCGTTTGCGTAACG
BOWTIE GATGTGGTTTCACATAAATTGACATATATAGTTCCAGGCTGTAAATGTTGT
+
<<;<<;;+<<7:<<<<<<7::7:<<<<<<:7,777402-4.-+*20+0-%-
+3_1_90_1028
@FC30C11AAXX:8:1:1667:1880
`bba`X_^ab_aWS`_b[`aa_]TZ^VYa`VW^`^b`a
GTTTTATACAAATCAAAACCATAGTGAGATACCATCTCACACTAGTCAGAA
@3_1_90_1651
+
TAATTTTAGATTTTATCCTTGACATTGTAAATATTACATT
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<:::::6767366767357566
+3_1_90_1651
@FC30C11AAXX:8:1:1577:1853
aUVF[aa`VU_`aaU[__aaaYV^aP`aQUa`_^_a
GTGATGGGAGGAAAGCTAGGGGGCTATAATGTCTATTACAAGGCTCAGTAG
@3_1_90_1670
+
<6<<<<<<<<<<<<<<<<<<<<<<<<<<<<:99:9646066,6+0604044

26. 6. Analysis example
SNAIL_F9.bwt
5_1_0_1409 + gi|51511750|ref|NC_000021.7|NC_000021 34604194 AGTTGCACCTTTAACAATTTCCCAT %/6::9::;;;;7279######### 0 17:G>T,24:G>T
5_1_0_811 + gi|89161218|ref|NC_000023.9|NC_000023 77246408 TTCTGCAAGCCTCCGGAGCGCACGTG BBB@5<?=9<9@>96/:0######## 0 25:C>G
5_1_1_1665 + gi|89161199|ref|NC_000002.10|NC_000002 201785208 GCCCAGCTGTCACTGTGGTTTTGATTTGC BBCCCBBBCBBB@BABBBACCA####### 0
5_1_2_1637 + gi|51511731|ref|NC_000015.8|NC_000015 92942360 GGGCTTCTGCCCCGGTGGGTACATGAGTA BBBABABABBAB9@A??=?<AA####### 0
5_1_2_1359 + gi|89161205|ref|NC_000003.10|NC_000003 101351498 CAATTCCCTCCTTGAAAGGCTCCTCCACC BCCBBBBAAAABA9@B?59@ABA###### 0
5_1_2_730 - gi|51511721|ref|NC_000005.8|NC_000005 1314600 GGACTTCCATGCAAACAAGCTGCTTTCCA ########BB>9@B@;@B<;??ABCBABB 0
5_1_2_1118 - gi|89161213|ref|NC_000007.12|NC_000007 157199758 CATCTTTGATGAGTTACTACCTGTGGGGT ########@B@?=B@;8@659@@BAABAB 0
5_1_3_920 + gi|51511727|ref|NC_000011.8|NC_000011 133317176 GGTAGACTCACAAAACTACCAAAGTCCTCTAC ABABAABCBBBBCBCBCBBBCCA>@@###### 0
5_1_3_971 + gi|89161190|ref|NC_000012.10|NC_000012 7497006 TTTTCATGCAGCCCGAGACATCAAGCTAGCAG B@86646330/250################## 0 31:T>G

27. 6. Analysis example
SNAIL_F9.bwt
5_1_0_1409 + gi|51511750|ref|NC_000021.7|NC_000021 34604194 AGTTGCACCTTTAACAATTTCCCAT %/6::9::;;;;7279######### 0 17:G>T,24:G>T
5_1_0_811 + gi|89161218|ref|NC_000023.9|NC_000023 77246408 TTCTGCAAGCCTCCGGAGCGCACGTG BBB@5<?=9<9@>96/:0######## 0 25:C>G
5_1_1_1665 + gi|89161199|ref|NC_000002.10|NC_000002 201785208 GCCCAGCTGTCACTGTGGTTTTGATTTGC BBCCCBBBCBBB@BABBBACCA####### 0
5_1_2_1637 + gi|51511731|ref|NC_000015.8|NC_000015 92942360 GGGCTTCTGCCCCGGTGGGTACATGAGTA BBBABABABBAB9@A??=?<AA####### 0
5_1_2_1359 + gi|89161205|ref|NC_000003.10|NC_000003 101351498 CAATTCCCTCCTTGAAAGGCTCCTCCACC BCCBBBBAAAABA9@B?59@ABA###### 0
5_1_2_730 - gi|51511721|ref|NC_000005.8|NC_000005 1314600 GGACTTCCATGCAAACAAGCTGCTTTCCA ########BB>9@B@;@B<;??ABCBABB 0
5_1_2_1118 - gi|89161213|ref|NC_000007.12|NC_000007 157199758 CATCTTTGATGAGTTACTACCTGTGGGGT ########@B@?=B@;8@659@@BAABAB 0
5_1_3_920 + gi|51511727|ref|NC_000011.8|NC_000011 133317176 GGTAGACTCACAAAACTACCAAAGTCCTCTAC ABABAABCBBBBCBCBCBBBCCA>@@###### 0
5_1_3_971 + gi|89161190|ref|NC_000012.10|NC_000012 7497006 TTTTCATGCAGCCCGAGACATCAAGCTAGCAG B@86646330/250################## 0 31:T>G
Parsing
SNAIL_F9.bwt.bed
chr21 34604194 34604219 5_1_0_1409 . +
chr23 77246408 77246434 5_1_0_811 . +
chr02 201785208 201785237 5_1_1_1665 . +
chr15 92942360 92942389 5_1_2_1637 . +
chr03 101351498 101351527 5_1_2_1359 . +
chr05 1314600 1314629 5_1_2_730 . -
chr07 157199758 157199787 5_1_2_1118 . -
chr11 133317176 133317208 5_1_3_920 . +
chr12 7497006 7497038 5_1_3_971 . +
chr01 201404048 201404081 5_1_3_1986 . +

28. 6. Analysis example
SNAIL_F9.bwt
5_1_0_1409 + gi|51511750|ref|NC_000021.7|NC_000021 34604194 AGTTGCACCTTTAACAATTTCCCAT %/6::9::;;;;7279######### 0 17:G>T,24:G>T
5_1_0_811 + gi|89161218|ref|NC_000023.9|NC_000023 77246408 TTCTGCAAGCCTCCGGAGCGCACGTG BBB@5<?=9<9@>96/:0######## 0 25:C>G
5_1_1_1665 + gi|89161199|ref|NC_000002.10|NC_000002 201785208 GCCCAGCTGTCACTGTGGTTTTGATTTGC BBCCCBBBCBBB@BABBBACCA####### 0
5_1_2_1637 + gi|51511731|ref|NC_000015.8|NC_000015 92942360 GGGCTTCTGCCCCGGTGGGTACATGAGTA BBBABABABBAB9@A??=?<AA####### 0
5_1_2_1359 + gi|89161205|ref|NC_000003.10|NC_000003 101351498 CAATTCCCTCCTTGAAAGGCTCCTCCACC BCCBBBBAAAABA9@B?59@ABA###### 0
5_1_2_730 - gi|51511721|ref|NC_000005.8|NC_000005 1314600 GGACTTCCATGCAAACAAGCTGCTTTCCA ########BB>9@B@;@B<;??ABCBABB 0
5_1_2_1118 - gi|89161213|ref|NC_000007.12|NC_000007 157199758 CATCTTTGATGAGTTACTACCTGTGGGGT ########@B@?=B@;8@659@@BAABAB 0
5_1_3_920 + gi|51511727|ref|NC_000011.8|NC_000011 133317176 GGTAGACTCACAAAACTACCAAAGTCCTCTAC ABABAABCBBBBCBCBCBBBCCA>@@###### 0
5_1_3_971 + gi|89161190|ref|NC_000012.10|NC_000012 7497006 TTTTCATGCAGCCCGAGACATCAAGCTAGCAG B@86646330/250################## 0 31:T>G
Parsing
SNAIL_F9.bwt.bed
chr21 34604194 34604219 5_1_0_1409 . +
chr23 77246408 77246434 5_1_0_811 . +
chr02 201785208 201785237 5_1_1_1665 . +
chr15 92942360 92942389 5_1_2_1637 . +
chr03 101351498 101351527 5_1_2_1359 . +
chr05 1314600 1314629 5_1_2_730 . -
chr07
chr11
157199758
133317176
157199787
133317208
5_1_2_1118
5_1_3_920
.
.
MACS
-
+
chr12 7497006 7497038 5_1_3_971 . +
chr01 201404048 201404081 5_1_3_1986 . +

29. 6. Analysis example
MACS pipeline
Output:
- Peak locations in BED and XLS format (genome browser)
- Tag count in wiggle format (genome browser)
- Bimodal model in R scripts

30. 6. Analysis example
H3K27me3
PolII

31. 6. Analysis example
snail_mfold_15_tsize41_newbwt_peaks.bed
track name="MACS peaks for snail_mfold_15_tsize41_newbwt"
chr1 559644 559924 MACS_peak_1 79.29
chr1 2435221 2435542 MACS_peak_2 51.58
chr1 14624217 14624571 MACS_peak_3 66.12
chr1 15610639 15611000 MACS_peak_4 56.69
chr1 16822564 16822753 MACS_peak_5 52.84
chr1 18411948 18412187 MACS_peak_6 82.46
chr1 22857612 22857985 MACS_peak_7 88.74
chr1 27541904 27542134 MACS_peak_8 69.47
snail_mfold_15_MACS.wig
track type=wiggle_0 name="MACS_counts_after_shifting" description="Shifted Merged MACS
tag counts for every 10 bp"
variableStep chrom=chr10 span=10
85171 1
85181 1
85191 1
85201 1
85211 1
85221 1
85231 2
85371 2

32. 6. Analysis example
snail_mfold_15_tsize41_newbwt_peaks.bed
track name="MACS peaks for snail_mfold_15_tsize41_newbwt"
chr1 559644 559924 MACS_peak_1 79.29
chr1 2435221 2435542 MACS_peak_2 51.58
chr1 14624217 14624571 MACS_peak_3 66.12
chr1 15610639 15611000 MACS_peak_4 56.69
chr1 16822564 16822753 MACS_peak_5 52.84
chr1 18411948 18412187 MACS_peak_6 82.46
chr1 22857612 22857985 MACS_peak_7 88.74
chr1 27541904 27542134 MACS_peak_8 69.47
snail_mfold_15_MACS.wig
track type=wiggle_0 name="MACS_counts_after_shifting" description="Shifted Merged MACS
tag counts for every 10 bp"
variableStep chrom=chr10 span=10
85171 1
85181 1
85191 1
85201 1
85211 1 CEAS
85221 1
85231 2
85371 2

33. 6. Analysis example
Input:
-BED format peak locations
- Optional signal profile in wiggle format
- BED format extra regions of interest

34. CEAS output

35. CEAS output

36. CEAS output

37. CEAS output

38. CEAS output

39. CEAS output

40. 7. Future challenges
Re-analyze data with new algorithms – sequences remain the same
ChIP-seq combined with Chromatin Conformation Capture (3C) –
long-range physical interactions
Technical improvements: RNA-seq will benefit from longer reads
Integrated computational analyses – integration of TF, histone
marks, methylation, polymerase loading to predict regulatory output

41. 8. Where to look for help...
Seqanswers.com

42. 8. Where to look for help...
Seqanswers.com
Google groups, mailing lists of each project
MACS
CEAS FindPeaks

43. 8. Where to look for help...
Seqanswers.com
Google groups, mailing lists of each project
MACS
CEAS FindPeaks
Lab mates!