1. Chromatin basics & ChIP-seq analysis
BS312 – Genome Bioinformatics
Lecture 5
Chromatin basics & ChIP-seq analysis
Vladimir Teif

2. Next generation sequencing analysis

3. Chromatin basics -- reminder

4. Transcription factor-centric view
Transcription factor (TF) concentrations
Protein assembly at regulatory regions
Transcription start site
Proteins produced (including TFs)
Teif et al. (2013), Methods. 62, 26-38

5. Transcription factor-centric view
Transcription factor (TF) concentrations
Enhancer
RNA polymerase: enzyme which makes RNA
Promoter
Proteins produced (including TFs)
Teif et al. (2013), Methods. 62, 26-38

6. Histone modifications-centric view
Turner B.M. (2005) Nature Structural & Molecular Biology, 12,

7. Histone modifications-centric view

8. NGS METHODS AND THEIR APPLICATIONS
Chromatin
domains
Hi-C
Figure adapted from

10. ChIP-seq (Chromatin Immunoprecipitation followed by sequencing)
1. Crosslink Protein-DNA complexes in situ
2. Isolate nuclei and fragment DNA (sonication or digestion)
3. Immunoprecipitate with antibody against target nuclear protein and reverse crosslinks
4. Release DNA and submit for sequencing
Adapted from

11. MNase-seq (Micrococcal Nuclease digestion followed by sequencing)MM
MNase-seq (Micrococcal Nuclease digestion followed by sequencing)
MNase = Micrococcal Nuclease (enzyme that cuts DNA between nucleosomes)
Teif et al. (2012), Methods, 62, 26-38

12. FAIRE-seq (Formaldehyde-Assisted Isolation of Regulatory Elements)
sequencing
Giresi et al (2007), Genome Res. 17, 877–885

13. DNAse-seq (DNase I digestion followed by sequencing
Wang et al. (2012), PLoS ONE 7, e42414

14. ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing)
How transposase works:
Buenrostro et al. (2013) Nat Methods. 10,

15. Methods for 1D genome mappingMM
Methods for 1D genome mapping
Meyer & Liu, Nature Reviews Genetics 15, 709–721 (2014)

16. Methods for 1D genome mapping
Tsompana and Buck, Epigenetics & Chromatin20147:33

17. Timeline of NGS methods
Bulk methods that require many cells
River and Ren (2013), Cell, 155, 39-55
Single-cell methods
Hu et al, Front. Cell Dev. Biol., 2018

18. Where to get NGS data? Do your own experiment
Gene Expression Omnibus (GEO)
Sequence read archive (SRA)
European Nucleotide Archive
The Cancer Genome Atlas (TCGA)
Exome Aggregation Consortium (ExAC)
You also have to upload your data!

19. How to analyze NGS data? Ask a bioinformatician
you need to explain what do you want, and for that you need to understand what/how can be done
Do it yourself
Command line –> become a bioinformatician
Online wrappers –> simpler, but file size limits
Example of a convenient online tool:
Galaxy

20. ChIP-seq (Chromatin ImmunoPrecipitation followed by sequencing)
1. Crosslink Protein-DNA complexes in situ
2. Isolate nuclei and fragment DNA (sonication or digestion)
3. Immunoprecipitate with antibody against target nuclear protein and reverse crosslinks
4. Release DNA and submit for sequencing
Adapted from

21. Experiment Data analysis

22. ChIP-seq analysis workflow

23. NGS data after sequencing but before mapping (
NGS data after sequencing but before mapping (.fastq file aka “raw” data):

24. Mapping with Bowtie http://bowtie-bio.sourceforge.net/manual.shtml
-v <N> Allow no more than N mismatches, where V may be a number from 0 through 3 set using the -v option.
-p <N> Use N computer processors/cores in parallel
-m <N> disregard reads with >N possible alignments

25. Guess what this command does
bowtie -v 2 -p 2 -m 1 mm9 filename.fastq filename.map
-v <N> Allow no more than N mismatches, where V may be a number from 0 through 3 set using the -v option.
-p <N> Use N computer processors/cores in parallel
-m <N> disregard reads with >N possible alignments

26. NGS data after mapping: .bed files (BED format)
Bowtie, BWA, ELAND, Novoalign, BLAST, ClustalW
TopHat (for RNA-seq)

27. Reads can align to overlapping locations
We need to count all reads at each base pair

28. From mapped reads to occupancy landscapes
HOMER, BedTools, BamTools, NucTools
Teif et al., Methods, 2012

29. Calculating occupancy with HOMER
makeTagDirectory <Directory Name> [options] <alignment file>

30. Quality control (QC)

31. Quality control (QC) Good ChIP-seq Bad ChIP-seq
Good ChIP-seq
Bad ChIP-seq

32. Data view in genome browsers
Jung et al., NAR 2014
UCSC Genome Browser (online)
IGV (install on a local computer)

33. UCSC Genome Browser

34. Create UCSC files with HOMER
makeUCSCfile <tag directory> -o auto

36. Peak shapes can be different
Park P. J., Nature Genetics, 2009

37. Systematic analysis requires to identify all peaks in all datasets and compare differences
Badet et al. (2012) Nature Protocols, 7, 45-61

38. Peak calling is a method to identify areas in a genome enriched with aligned reads
Wilbanks EG (2010) PLoS ONE 5, e11471.

39. Peak calling: finding the peaks
Input: sample that was prepared in the same way as in the ChIP-seq, but no antibody was added, so it has no specific enrichment of our protein of interest
Pepke et al. (2009). Nature Methods, 6, S22–S32. 

40. Peak calling: defining statistical significance

41. Peak calling: defining statistical significance
MACS (good for TFs)
CISER (histones, etc)
HOMER (universal)
PeakSeq
edgeR
CisGenome
Is this peak
statistically
significant?
Is this peak
statistically
significant?
Park P. J., Nature Genetics, 2009

42. Finding peaks with HOMER

43. Guess what this command does
findPeaks ChIPDirectory -style factor -i InputDirectory
We need to map our ChIP-seq and its Input (control), then
create their HOMER tag directories ChIPDirectory and InputDirectory, then find peaks using both these directories.
Additional optional parameters:
-F <#> Enrichment ratio ChIP vs. Input (by default 4-fold)
-P <#> P-value cut off (by default

44. ChIP-seq: reads to peaks/regions
MACS, CISER, HOMER
PeakSeq, edgeR, DESeq, CisGenome

45. Peaks/regions in BED format
pos2bed.pl peakfile.txt > peakfile.bed bed2pos.pl peakfile.bed > peakfile.txt

46. Intersecting genomic regions
BedTools (command line)
Galaxy (online)

47. Genomic features are also regions
Mattout et al., Genome Biology, 2015

48. Let’s look at many similar regions
Each horisontal line is one genomic region
deepTools
NucTools

49. ChIP-seq heat maps for all genes, scaled with respect to their start (TSS) and end (TES)

50. Cluster heatmaps deepTools 2.0

51. Comparing cluster heatmaps between two cell conditions
NucTools

52. Histone modifications around TSS
deepTools

53. Motif enrichment analysis
HOMER, MEME
Pavlaki et al., 2017

54. Finding motifs with HOMER
HOMER takes the coordinates of all ChIP-seq peaks, looks at the corresponding DNA sequences of each peak and finds the common consensus motifs that are encountered in many of these peaks.
Then HOMER looks in a database and reports which motifs are similar to already known TF binding motifs, and which motifs are new.

55.
The MEME Suite is even more sophisticated and contains all tools that are needed for motif analysis

56. Summary of ChIP-seq analysis:
Map all reads
Occupancy calculation
Differential peak calling
Intersection of different signals
Correlation of different signals
Motif enrichment in peaks

57. HEATMAP; AGGREGATE PROFILE; GENE ONTOLOGY (GO)
Take home message
Raw reads -> mapping -> peak calling
MUST KNOW:
Where NGS data is stored (GEO, etc)
~100s types of NGS experiments; we focus on chromatin
ChIp-seq data structure
RAW DATA; MAPPED READS; REGIONS; SITES
GENOME BROWSERS. PEAKS. PEAK CALLING
HEATMAP; AGGREGATE PROFILE; GENE ONTOLOGY (GO)
Optional video: