1. EDACC Quality Characterization for Various Epigenetic Assays
Cristian Coarfa
Bioinformatics Research Laboratory
Molecular and Human Genetics

2. Data Types Submitted To EDACC
ChIP-Seq
Methyl-C
RRBS
MRE-Seq
MeDIP-Seq
Chromatin Accessibility
small RNA-Seq
mRNA-Seq

3. Quality Characterization
How to measure the quality of mapped reads?
Note: not quality of sequencing
statistics on this are provided by the sequencer
Most labs do some sort of visual inspection
Metrics for characterizing level 2 data quality
Apply it to various data types submitted to EDACC

4. Enrichment Based Protocols
ChIP-Seq, MeDIP-Seq, Chromatin Accessibility
Methods implemented
PTIH (percent tags in hotspots)
iROC (integral of ROC)
Percent tags in peaks (FindPeaks)
Poisson enrichment metric
Implemented in EDACC pipeline
Metrics computed on all submitted data

5. PTIH (percent tags in hotspots)
Detect enriched regions using “hotspot” algorithm
PTIH = percentage of all tags that fall in hotspots

6. Hotspot algorithm
Scan statistic gauging enrichment with a z-score based
on the binomial distribution.
n tags
250 bp
50kb
N tags
Binomial distribution gives probability of seeing n tags in the small window given N tags total in the large window. This adjusts for local background fluctuations (due to CNV, for instance).

7. PTIH values
0.48
0.19
0.72
0.48

8. PTIH values
0.48
0.19
0.72
0.48

9. Ratio of Tags in Peaks Determine uniquely mapping reads
Use FindPeaks to call peaks
Count reads mapping into peaks
percentage of total mapped reads

10. Poisson Based Enrichment Method
Determine uniquely mapping reads
Remove duplicate reads
Bin the reads into 1kb windows
Infer parameters of a simple poisson distribution
Filter enriched windows
p-value < 0.01
Count reads mapping into enriched windows

11. Next Step – Metrics Evaluation
Metrics probe different features of data
Use visual inspection to ascertain which (one or more) of the proposed methods captures useful aspects of data quality. 11

12. ChIP-Seq/Chromatin Accessibility/FindPeaks QC Metrics
Collaborative efforts between centers
~330 lanes of verified ChIP-Seq, MeDIP-Seq, and Chromatin accesibility data
Accesible in Epigenome Atlas

13. Going forward EDACC will run continuously on all submitted data
Option to automatically flag data that fall below specified thresholds
For most data types we need further experience on what thresholds make sense
Include QC metrics in metadata
Provide downstream users with this information
Note that we are breaking new ground
uniform quality scoring is not being performed by other major consortia (ENCODE, modENCODE)

14. Pearson correlation for ChIP-Seq Histone Modification
Using raw density maps at 10kb resolution
Process
Select uniquely mapping reads
Extend 200bp in mapping strand direction
Remove monoclonal reads
Build density map
Pearson correlation with other submitted marks
Ideally: a mark correlates best with other experiments for the same assay
How well does Pearson correlation work ?
Help us identify 5 bad lanes, REMCs retracted the data

15. PCA Analysis 10kb windows on chr20
PCA using Pearson correlation metric

16. Pearson correlation metric
Input
H3K36me3
H3K9me3
H3K79me1
H3K20me1
Pearson correlation metric
H3K27me3
PCA 53.8%
H3K4me3
H3K9ac
H2AK5ac
H2BK120ac
H2BK12ac
H2BK15ac
H2BK20ac
H3K14ac
H3K18ac
H3K23ac
H3K27ac
H3K4ac
H3K56ac
H4K5ac
H4K8ac
H4K91ac

17. MRE-Seq Reads are mapped onto reference genome
Uniquely mapping reads are kept
Build the fragment map of expecting mapping locations
based on the enzyme cocktail used
Count reads mapping within the expected digest fragments
76-99% of reads map within expected fragment

18. mRNA-Seq Reads are mapped onto reference genome
Uniquely mapping reads are kept
Count reads mapping within UCSC genes exons
70-90% of reads map within gene exons
UCSC known genes
Entrez genes

19. Small RNA-Seq Trim adaptors Reads are mapped onto reference genome
Reads mapping up to 100 locations are kept
Count reads overlapping with known small RNAs
miRNAs, piRNAs, sno/scaRNAs, piRNAs, repeat RNAs
At least 30% of reads overlap with known small RNAs

20. Bisulfite Sequencing Map using Pash Methyl-C RRBS Genome wide QC
C->T Conversion rates; typically 99%
RRBS
Enzyme cocktail
Map within expected cut sites
Ratio varies 40%-90%

21. QC for MeDIP-Seq Data Using Galaxy

22. Exercise Download the input MeDIP-Seq file from the workshop wiki
Determine the ratio of reads in peaks using FindPeaks in Galaxy