Tools for a Reproducible Workflow for ChIP-seq QC and Visualisation.

Thomas Carroll

Head of Bioinformatics

MRC Clinical Sciences Centre

Overview

Higher output more complex hypotheses.
New challenges for:
- Quality control
- Visualisation
- Dynamics of epigenetic states.
- Reproducibility and reporting.

Then

Limited sequence depth.
Shorter reads.
Developing protocols.
Single sample studies.
Non standardised toolset.
ChIP-seq != RNA-seq

Now

Greater depths per lane.
Established protocols
Multiplexing technologies - Sample replication.
Advanced toolsets
Borrowing of analysis techniques between data-types

High-throughput Sequencing technologies

Bigger studies require large scale quality control

ChIP-seq has many sources of noise
- Inefficient or non-specific antibodies.
- Artefact signals.
- Signal complexity.
Requirement for methods to assess quality within sample groups and across large experiments.

How to assess what is useful.

Encode and others have established metrics of ChIP-seq quality.
- SPP/phantomQualityTools.
- HTSeqtools.
Use and interpretation of these metrics varies with ChIP type.
Such metrics may be overfitted to these big studies and dependent on the standardised processing protocols applied.

Epigenetic's data QC study

> 500 datasets of ChIP-seq for transcription factor and epigenetic marks.
Evaluate quality control metrics under different conditions.
Identify importance/redundancy of QC metrics.
Establish effect of processing on QC metrics

Carroll et al Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. Front Genet April 2014.

Blacklisted Regions

Identified by Peter Park.
DAC list created by Anshul Kundaje for Encode.
Species specific regions of consistent ultra high signal
Contains repeat regions and genomic expansion.

Blacklisted Regions and the "Phantom Peak"

Fragment length peak indicates ChIP signal strength.
"Phantom peak" often identified in cross-correlation analysis at read length.

Signal from blacklisted regions contribute solely to this peak

Measures of inequality of coverage (SSD)

Standardised Standard Deviation (SSD) of ChIP-signal.
- Measure of overall "peakiness".
- More suited for longer marks
SSD strongly influenced by blacklisted regions.

Library complexity and duplication levels

Library complexity often assessed by duplication rate.

Artefact duplicates often overestimated.

Duplicates may arise from high efficiency and/or high depth.

ChIP-exo

ChIP-exo offers higher resolution and greater efficiency.
Reduced "Blacklisted" signal.
Higher duplication in peaks.
Cross-correlation profiles require intrepretation.

ChIPQC package

High-throughput QC of epigenetics data.

Implemented QC learned from study.
Designed for work on large experiments with multiple groups.
Implemented in R and Bioconductor for ease on installation, implementation and integration with reporting

Examples ChIP-QC

Evaluate +/- read clustering around peaks.

Contribution of artifact signal.

Distribution of ChIP signal across genome.

QC from low depth

(MiSeq)

Speed of Miseq's run allows for rapid evaluation of ChIP prior to large scale sequencing.
Direct comparison of Hiseq vs Miseq demonstrates its ability to evaluate ChIP quality

~ 1 Million reads

Useful links

Good papers to read.

Timothy Bailey - Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data
Encode - Large-Scale Quality Analysis of Published ChIP-seq Data
Shirley Liu - Systematic evaluation of factors influencing ChIP-seq fidelity
Carroll and Liang - Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data

ChIP-QC courses

https://www.bioconductor.org/help/course-materials/2014/BioC2014/Bioc2014_ChIPQC_Practical.pdf
https://www.bioconductor.org/help/course-materials/2014/BioC2014/ChIPQC_Presentation.pdf

Visualisation of data

Essential step is to look at your data.

Evaluate results in their genomic context

Identify patterns and artefact regions.

Genome Browsing

Genome Browsers present a linear representation of genomics data.

Allow for integration of multiple data-types as well as inclusion of both the user's and public datasets.

Popular browsers include UCSC (server) and IGV (local).

Integration with genome browsers

Trackhubs and Sample information files may be created and manually curated.

For bigger studies, the organisation of tracks in a genome browser is not a trivial task.

Evaluation of results requires exporting, conversion and potentially uploading to FTPs.

tracktables package.

Generation and integration of R objects and dynamic HTML reports with the IGV genome browser.

Allow for rapid presentation of high-throughput sequencing results in the IGV genome browser.

Produce experiment focused IGV-linked HTML reports.

Cast R objects into HTML tables with links to genomic locations.

Integration with pre-existing reporting tools reinforcing reproducible research.

Building Experiment Reports

Rapid prototyping by visualisation

Cast R/Bioconductor objects into HTML tables.

Allows user to evaluate results in genomic context.

Interval report

IGV tips.

User guide
- http://www.broadinstitute.org/igv/UserGuide
MRC CSC courses -
- File formats - http://mrccsc.github.io/genomicFormats.html#/
- IGV - http://mrccsc.github.io/igvPres.html#/

Dynamics of epigenetic states

With the advent of multiplexing technologies
- Greater number of sample groups
- Higher replicate numbers
More complex hypotheses.
- From simple epigenetic mapping -> Identifying significant changes in epigenetic states.

Borrowing RNA-seq methods for ChIP-seq data

Differential affinity/binding of epigenetic marks or transciption factors.

Methods implemented in RNA-seq now being used in ChIP-seq (DEseq2/limma/EdgeR/Diffbind)

ChIPQC integrates with Diffbind.

Visualising dynamics of epigenomic states over genomic interval sets

RNA-seq visualisation methods don't cover ChIP-seq

Require tools to investigate shapes of signal.

Simple tools designed for small sample numbers.

soGGi package

Summarising Over Grouped Genomic Intervals

Accepts multiple input types (BAMs/bigWigs/Motifs) to generate profiles.

Leverages GGplot2 to enable intuitive plotting grammar

Implements normalisation procedures and arithmetic operations across profiles.

Profile types require different techniques

soGGi allows for three types of plot
- Point (signal around a genomic location).
- Meta (signal over and around a genomic region normalised to that genomic regions length).
- Hybrid (Point plot over edges and Meta plot over central genomic region).

Integrating Arithmetic Operations

Profiles can be further normalised and operated on to visualise hypotheses.

Most common arithmetic operations and transformations can be applied between profiles.

Accounting for noise in ChIP-seq

Technical variation in ChIP-seq is typically considered very high.

Signal across artefact regions and duplication rates can be highly variable.

Such defined sources of noise can inform the models used in differential binding analysis.

pol2Rates package (unpublished)

Tools to evaluate quantitative changes in Pol2.

Calculates normalisation factors from Pol2 signal distribution.

Not available yet but hope for release before end of year

Comparison of Uncorrected vs Corrected

Correction significantly improves correlation with RNA-seq data and known targets.

A Reproducible Workflow

Created three packages to handle:
- Quality control
- Genome Browser integration.
- Visualisation of signal structure over genomic regions of interest.
Integrated with R's rMarkdown (Reporting) and packrat (Version control) packages.
Together allows for creation of customisable and reproducible reporting for ChIP-seq.

Acknowledgements