1. Analysis of ChIP-Seq Data
Biological Sequence Analysis
BNFO 691/602 Spring 2014
Mark Reimers

2. Analysis of ChIP-Seq Data
Genomic Data Analysis Course
Moscow July 2013
Mark Reimers, Ph.D

3. What Are the Questions? Where are histone modifications?
Where do TFs bind to DNA?
Where do miRNAs or RNABPs bind to 3’ UTRs?
How different is binding between samples?
Focal questions are more where…
We haven’t got good enough to ask how much?

4. Why ChIP-Seq?
ChIP-Seq is ideal (and is now the standard method) for mapping locations where regulatory proteins bind on DNA
Typically ‘only’ 2, ,000 active binding sites with footprint ~ base pairs
Similarly ChIP-Seq is fairly efficient for mapping uncommon histone modifications and for RNA Polymerase occupancy , because the genomic regions occupied are very narrow

5. Chromatin Immuno-Precipitation
Chromatin Immuno-Precipitation (ChIP) is a method for selecting fragments from DNA near specific proteins or specific histone modifications
From Massie, EMBO Reports, 2008

6. Chromatin Immuno-precipitation
Proteins are cross-linked to DNA by formaldehyde or by UV light
NB proteins are even more linked to each other than to DNA
DNA is fragmented
Antibodies are introduced
NB cross-linking may disrupt epitopes
Antibodies are pulled out (often on magnetic beads)
DNA is released and sequenced

7. CLIP-Seq – A Related Assay
Cross-linking immuno-precipitation (CLIP)-Seq is used to map locations of RNA-binding proteins on mRNA
Even miRNA binding can be mapped indirectly by CLIP-Seq with antibodies raised to Argonaute – an miRNA accessory protein

8. What ChIP-Seq Data Look Like
Note that Input also has peaks… in many of the same locations
Draw out
From Rozowsky et al, Nature Biotech 2009

9. The Value of Controls: ChIP vs. Control Reads
NB. Non-specific enrichment depends on protocol
Need controls for every batch run
Red dots are windows containing
ChIP peaks and black dots are windows
containing control peaks used for
FDR calculation

10. Goals of Analysis
Identify genomic regions - ‘peaks’ – where TF binds or histones are modified
Quantify and compare levels of binding or histone modification between samples
Characterize the relationships among chromatin state and gene expression or splicing

11. General Characteristics of ChIP-Seq Data
Fragments are quite large relative to binding sites of TFs
ChIP-exo (ChIP followed by exonuclease treatment) can trim reads to within a smaller number of bases
Histone modifications cover broader regions of DNA than TFs
Histone modification measures often undulate following well-positioned nucleosomes

12. ChIP Reads Pile Up in ‘Peaks’ at TF Binding Sites on Alternate Strands
Indicate

13. ChIP-Seq for Transcription Factors
Typically several thousand distinct peaks across the genome
Not clear how many of lower peaks represent low-affinity binding sites
From Rozowsky et al, Nature Biotech 2009

14. ChIP-Seq for Polymerase
Fine mapping of Pol2 occupancy shows peaks at 5’ and 3’ ends
From Rahl et al Cell 2010

15. ChIP-Seq Histone Modifications
Many histone modifications are over longer stretches rather than peaks
May have different profiles
Not clear how to compare

16. Issues in Analysis of ChIP-Seq Data
Many false positive peaks
How to use controls in data analysis
How to count reads starting at same locus
What are appropriate controls?
Naked DNA, untreated chromatin, IgG
Some DNA regions are not uniquely identifiable – ‘mappability’
How to compare different samples?
Overlap between peak-finding algorithm results are often poor

17. Mapability Issues
Many TFBS and histone modifications lie in low-complexity or repeat regions of DNA
With short reads (under 75 bp), with some errors, it may not be possible to uniquely identify (map) the locus of origin of a read
UCSC provides a set of mapability tracks
Select Mapping and Sequencing Tracks
Select Mapability
35, 40, 50 & 70-mer mapability (some with different error allowances)

18. END for Seq Analysis

19. ChIP-Seq for Histone Modifications
Various histone modifications characterize different regulatory states

20. Exon Peaks

21. Intronic Peaks
My guess is that there is an alternate initiation site for this gene

22. Intergenic H3K4me3 Peaks
Peak of H3K4me3 in region not annotated by RefSeq
Corresponds to unknown TAR in cerebellum
(annotated by Aceview)

23. H3K4me3 vs Gene expression
91.5% of expressed genes have H3K4me3 peaks
Biological significance?
Genes with peaks but no expression … maybe poised
Expressed genes with no peaks … maybe failure of peak-finder
H3K4me3 peaks at TSS - peaks within 1 kb of the TSS

24. CLIP-Seq for RNA Binding Proteins
Argonaute high throughput sequencing of cross-linking immunoprecipitation (HITS-CLIP) protocol, also known as CLIP-seq.
Thomson D W et al. Nucl. Acids Res. 2011;39:
© The Author(s) Published by Oxford University Press.

25. The ENCODE Project
Comprehensive characterization of chromatin state and locations of some TFs and other DNA-binding proteins (e.g. Pol2) across various conditions in human cell lines and in mouse tissues
So far no normal human tissues, which likely have rather different epigenetic marks

26. Demo: ENCODE Data at UCSC

27. ChIP-Seq Demo

28. Peak Calling

29. Goals of Peak-Calling
Identify discrete locations where a particular protein binds
Often applied more generally (and IMHO poorly) to identification of short regions with a particular histone modification

30. Issues in Peak-Calling
Background of random genomic reads is not uniform
Affected by CG content and other factors
Most good algorithms try to estimate a local background
Local background has peaks too!

31. Peak-Finding - Simple Extend tags; sum overlaps at each base
Find center of each discrete cluster
Issues:
Are clusters discrete?
How much to extend?
Fragment size unclear

32. Peak Finding – Better
Tags starting on opposite strands are likely to start at opposite ends of precipitated fragments
Identifying the cross-over point leads to better accuracy

33. Issue: How to Identify a Cluster
Background varies – often related to local CG content and chromatin state
Need statistical test for excess counts above local background
Usually done by binning counts into 1kb bins across genome

34. Control Reads Show Peaks Also
From Rozowsky et al Nature Biotech 2009

35. Cause of Variation in Read Density
In study of FoxA1 binding, even control reads enriched near FoxA1 binding site!
Probably due to open chromatin near FoxA1 binding site
Density of Control Channel
reads around FoxA1 site
Courtesy Shirley Liu

36. Peak Finding by MACS Smart peak imputation estimate
Uses read directions
Empirical estimate of fragment length
Local frequency estimate
Using control, if available
Using wide estimate, otherwise
Not using sequence

37. MACS Workflow
Key innovation is to estimate fragment length empirically
If no control sample MACS estimates background from median of ChIP bin counts
No use of CG content

38. The Value of Controls: ChIP vs. Control Reads
Red dots are windows containing
ChIP peaks and black dots are windows
containing control peaks used for
FDR calculation

39. Issue: Fragment Lengths
Puzzle: Fragments from sonication expected to be between 200 – 500 bp
Empirically estimated fragment size ~ 100
Shirley Liu’s explanation: preferential fragmentation near TF

41. Peak Calling Demo

42. Quantitative Comparison of ChIP Data