1. Analysing ChIP-Seq Data
Simon Andrews
@simon_andrews v

2. Data Creation and Processing
Starting DNA
Fragmented DNA
ChIPped DNA
Mapped BAM File
FastQ Sequence File
Sequence Library
Filtered BAM File
Exploration
Analysis

3. Steps in Analysis Define enriched regions Quantitate Compare
Based around features
De-novo peak prediction
Quantitate
Corrections and Normalisation
Compare
Categorical
Quantitative

4. Defining Regions - Should I peak call?
You need a single set of reference positions for analysis
Peak calling to define solely from the data
Feature based measurements if your exploration showed linkage to features
If exploration showed strong and reasonably complete feature association then this is a good option
No worries about missing weaker peaks
More complete background (get both enriched and unenriched)
If no feature linkage then peak call
Only looking at enriched regions
More difficult to do functional interpretation later on

5. How Peak Callers Work (MACS)
Optimise the starting data
Build a background model
Test sliding windows
Report
Apply per-site adjustment

6. Optimise the starting data
Correct the for/rev offset
Deduplicate

7. Build a background model
Lambda value
Observed

8. Build a background model
Lambda value
Critical p-value (n=18)
Model

9. Build a background model
Lambda value
Critical p-value (n=18)
Observed + Model

10. Test Sliding Windows Generally use half of the library fragment size
Windows whose count exceeds the critical value are kept
Merge adjacent windows over the critical value to form peaks
Generates candidate (not final) peak set

11. Correct for local variation
Critical value
Generate localised model if input density
is higher than the global value
Most pessimistic p-value is kept

12. How should you apply peak callers
Multiple ChIPs (over multiple conditions)
Multiple Inputs

13. Multiple Inputs Input variability generally reflects general trends
Mappability
Genome Assembly
Fragmentation biases
Normally best to merge all inputs to one common reference input

14. Multiple ChIPs BAM Files Peak Sets WT ChIP 1 WT ChIP 2 KO ChIP 1+
WT ChIP 2
KO ChIP 1
KO ChIP 2
Peaks
WT ChIP 1 +
WT ChIP 2
KO ChIP 1
KO ChIP 2

15. Multiple ChIPs BAM Files Peak Sets WT ChIP 1 WT ChIP 2 KO ChIP 1
WT Peaks 1
WT Peaks 1
And
WT Peaks 2
KO Peaks 1
KO Peaks 2
WT Peaks 1
And
WT Peaks 2 Or
KO Peaks 1
KO Peaks 2
WT Peaks 2
KO Peaks 1
KO Peaks 2

16. Multiple ChIPs WT Rep1 WT Rep2 WT Rep3 WT All KO Rep1 KO Rep1 KO Rep1
KO All
Peaks All
Final Peaks (dedup)

17. Why isn't a peak called Calling a peak is a combination of
Degree of enrichment
Behaviour of the background
Total number of sequences

18. Why isn't a peak called
Fewer peaks are called by just sub-sampling the same data

19. Why isn't a peak called
With no input the region around the peak is used to model the background

20. Combining Peak sets
Don’t make claims based solely on the number of peaks (“there were more WT peaks than KO peaks” for example)
Don’t make claims based on regions being peaks in 1 set but not another (there were 465 peaks which were specific to KO)
It is OK to make statements about overlap (there were 794 peaks which were common to WT and KO)
You have to address differential enrichment problems quantitatively

21. Quantitating ChIP data for analysis
Quantitation of ChIP is not a simple problem
Can start with something simple but in many cases you will need to refine this.
Simple linear, globally corrected counts are a good place to start

22. Cleaning and Evaluating Filtering on input
As with the exploration we should remove measures with unusually high input signal
Easy if all measurements are the same size
For different size measures (eg gene bodies) make sure you calculate read density (not just count) so values are comparable

23. Cleaning and Evaluating Filtering on peak size
Many ChIPs will produce characteristically sized peaks
Look for peaks which fall outside the expected range
~1kb

24. Normalising to input?
Do you have significant variability in your input
If not then there's no need to normalise
Check it's not just related to different peak sizes!
Do you have ChIP signal which is correlated with the input level?
Only if you see correlation between ChIP and input is normalisation to be considered
Most of the time this isn't the case

25. See if the input has an influence
For truly enriched regions the input level is not predictive of the ChIP level.
Normalising to input would make things worse here.

26. Why not always do "fold over input"?
Inputs are generally poorly measured
Coverage over enriched areas will be low in the input
Fold values more influenced by input than ChIP
Biases in input are smaller than enrichment power of the antibody

28. Evaluating and Normalising Enrichment
Normalised Read Count
Percentile through data

29. Look for systematic enrichment changes (real biology!!)

30. Normalising Enrichment
Simple
Single point of reference (percentile, size factor etc)
Works for small differences, not for large ones
Enrichment specific
Two points of reference
Low percentile to reflect baseline
High percentile to reflect close to saturation
Add to match first, Multiply to match second
Quick and Dirty
Quantile normalisaiton to force a common distribution
Don't normalise the input or use it to calculate distributions!

31. Normalising Enrichment

32. Checking Normalisation
Before Normalisation
After Normalisation

33. Differential enrichment analysis
Needs to be quantitative
Needs to operate on non-deduplicated data
Two statistical options
Count based stats on raw uncorrected counts
DESeq
EdgeR
Continuous quantiation stats on normalised enrichment values
LIMMA

34. Which statistic to pick?
If baselines do not look obviously separated
Raw counts, then DESeq/EdgeR
If baselines have separated
Enrichment normalisation
LIMMA statistics

35. Validation of hits Map onto scatterplot for simple verification
Check properties of peaks
Sizes
Strand bias
Genomic location
Look at the data underneath candidates you make specific claims about

36. Hit validation Linear View Log View Look whether hits make sense
Look at points which change but were not selected
Log scale can be useful for visualising hits
Keep the context of non-hits

37. Hit validation Directionality
Most ChIP enrichments are not strand-specific
Should expect to see enrichment on both strands

38. Hit validation Hits shouldn't look different in unrelated metrics
Strand Bias Plot
Variance Plot
Hits shouldn't look different in unrelated metrics
Directionality
Variance (within replicate group)

39. Hit validation
You should be able to see consistency between replicates

40. Downstream Analyses

41. Composition / Motif Analysis
Good place to start, can provide either biological or technical insight
See if hits (up vs down) cluster based on the underlying sequence composition
Motifs
Great for defining putative binding sites
Interesting to do sensitivity check
Can do differential motif calling (for hit/non-hit)

42. Compter - composition analysis

43. MEME - Motif Analysis

44. Gene Ontology / Pathway
Be careful how you relate hits to genes
Really need to have a global link between peak positions and genes
Random positions will give significant GO hits if you just use closest/overlapping genes

45. Experimental Design

46. Experimental Design Considerations
All normal rules apply
Think about sources of variation
Don't confound variables
Think about what batch effects might exist
Test your antibody well before starting
By far the biggest factor in success
Good performance on Western / in-situ is not a guarantee, but it's a good start

47. Experimental Design Considerations
Number of replicates
Lots of studies use 2 replicates
Fine for just finding binding sites (motif analysis)
Not really enough for differential binding
Huge reliance on 'information sharing'
No accurate measurement of variance per peak
Potentially over-predicts differential binding
Should think about likely levels of variability and make replicates to match

48. Experimental Design Considerations
Amount of sequencing
Can be difficult to predict
Depends on
Genome size
Proportion of genome which is enriched
Efficiency of enrichment
ENCODE standard is ~20M reads per sample
Can get away with fewer (K4me3 for example)
Will need more for some marks (H3 for example)
Sequencing depth will affect ability to detect changes

49. Experimental Design Considerations
Type of sequencing
Single end is find for most applications
ATAC-Seq can require paired end for some analyses
Moderate read length is required
Can map anywhere in the genome
50bp is probably OK. 100bp would be preferable

50. Material for the Course
All
Slides
Exercises
Data
Virtual Machine Images
Are available at