Bioinformatics pipeline for ChIP-seq analyses
Miklós Laczik, Jan Hendrickx, Céline Sabatel, Irina Panteleeva, Hélène Pendeville, Dominique Poncelet
Epigenetics R&D Department, Diagenode SA, Liège, Belgium

Diagenode sa, LIEGE Science Park, Rue du Bois Saint-Jean, 3 4102 Seraing Belgium

Introduction
As the role of the changes in the epigenome is becoming more and more revealed there area growing number of scientific papers reporting epigenetic/epigenomic studies. Malicious
epigenomic alterations can be the reason behind widespread social problems like differenttypes of cancers (eg. breast cancer), as well as rare diseases like ICF (Immunodeficiency,Centromeric region instability, Facial anomalies) syndrome, the like of which often not interest
the medical industry therefore the diagnostic/therapeutic products are underdeveloped or
non-existent at all. Such chromatin diseases (Rett syndrome, ATRX syndrome, FSHD, ICF
syndrome) are the subject of the DisChrom project (FP7 project under the grant agreement
PTN-GA-2009-238242) in which we participate.
Chromatin ImmunoPrecipitation (ChIP) coupled with high-throughput massively parallelsequencing as a detection method (ChIP-seq) has become one of the primary methods for
epigenomic researches, namely to investigate protein-DNA interaction on a genome-widescale. While ChIP-seq applications and the subsequent data analysis is well described fortranscription factor studies, the histone modifications are far less documented: we need to
set up standards and find or create the appropriate bioinformatics tools to analyse such data.
It is especially important since histone modifications and the correlated chromatin structurechanges could be responsible for the development of chromatin diseases like the ones
mentioned above.

The principal work in our laboratory involves antibody production and quality control, andmany of the candidates are also tested by ChIP-seq. We mainly focus on histone marks andbesides the antibody production we regularly obtain ChIP-seq data from other projects , either
from outsourced sequencing or from our own IonTorrent PGM sequencer. Thus we deal witha lot of ChIP-seq data on a regular basis, coming from various sequencing platforms and alarge range of antibodies. With this heavy demand it is natural that we need an automatedbioinformatics solution that is able to cope with the deluge of ChIP-seq data.
In this poster we will present a modular bioinformatics pipeline which was developed
specifically for the analysis of ChIP-seq data, but can be easily modified and extended toaccommodate other high-throughput sequencing and epigenetic applications, such as RNAseq or methylation analysis and other custom data processes. We will introduce the modules
and the workflow as well as the standards we set up for data quality (based on requirementsof the scientific community and our own experience and research), and we will demonstrate
the usage and the potential of it analysing actual ChIP-seq data from our latest experiments.

Criteria system and pipeline structure123

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Diagenodetranscription
factorsorted.bam

We first check the quality of the sequencing, using the manufacturer provided software of
the sequencer or a dedicated software. Then we check the peak qualities (numbers, size,significance, read fraction in peaks, genomic feature association), and compare them toreference sets and/or replicates. For overlap analysis we use the Encode guideline: we expect
at least 80% of the top 40% of the peaks to have a match in the other dataset. Note that theexpected value ranges and characteristics depend heavily on the histone mark (or transcription
factor) in question (and of course other sample features, like the organism, cell/tissue type,treatment etc.).
Our pipeline has five core modules that cover the whole process of ChIP-seq analysis, withthe option of adding further modules for custom analyses. It takes advantage of configuration
files, which enable you to save and load your pipeline with your custom settings and modules.
1. Alignment module: this module starts with read files and align the reads to a reference
genome (must be indexed previously). It utilizes the BWA aligner, which is a fast tool optimizable
to either short or long NGS reads, which is important for us, since we use both Illumina
sequencers and the IonTorrent PGM. It is also possible to skip this module if you start thepipeline with alignment files (in SAM, BAM or BED format).
Optional module: it is possible to run the command line version of FastQC automatically after
the alignment to assess the read quality.
2. Peak calling module: here we utilize our own scripts and scripts from the Sicer software
package, which is a genome partitioning peak caller developed specifically to detect (evenlong) histone marks. At this stage it is very important to use the correct settings, which arepredefined for the most frequently used histone marks (but can be freely adjusted if the dataset
needs it).

3. Statistics module: after the peak calling we use custom scripts and scripts from the Homerand BedTools software packages to describe the dataset statistically. We count the reads, theduplicate read ratio, the number of peaks, the reads that are in peaks and their ratio against
the background (signal-to-noise ratio), we measure the average size of peaks and their averagesignificance, and the genome coverage. Also we do a general annotation to identify whether
the peaks have an affinity to a genomic feature.
4. Comparison module: the aim of this module is to compare the analysed peak set to one
or more reference datasets, utilizing Homer and BedTools, as well as custom scripts. The
reference datasets could be the predefined Encode/modEncode datasets, or any other customdatasets, replicates. The overlaps are checked in terms of the total peak set as well as thetop40% of the peaks.
Optional modules for 3. and 4.: because the aim of the analysis can differ from project toproject, and we cannot prepare for every possible case, there are no predefined optional
modules; although it is easy to add a custom script for further analyses (eg. the annotation inmodule 3. can be exploited to perform a pathway analysis). Note however that the results ofthese custom modifications won’t be involved in the final report, unless you modify module 5.
as well, so the best practice is to prepare your scripts to produce their own reports too.
5. Reporting module: this is the final module, designed to retrieve all the necessary informationfrom the outputs of the previous modules and create a report file in XLS spreadsheet format,
which is user friendly and easy to review and understand. Naturally you can open it in eg. Excelof Microsoft Office or Calc of OpenOffice to produce further charts, tables and analyses.

Optional module: instead of Sicer, MACS can be loaded as the second module. It is preferable
for short enrichments, like transcription factors.
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1. We successfully use
the pipeline on a weekly
basis in the assessment
and comparison of thequalities of antibody
candidates.
Thisiscrucial to produce highquality antibodies thatare proven to be effective
for ChIP-seq. The picture
shows the antibody
validating process.

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2. We also use the pipelinein various research anddevelopment projects.
With this pipeline wemanaged to prove thatour proprietary ChIPseq kit and protocol for
extremely low amount ofcells is able to produce
high quality results fromas low starting amountas only a few picograms.

3. The flexibility of thepipeline enables us to
use it for a wide range ofanalyses. The histogramand box-and-whiskers
plot are excerpts fromthe report file of a study
where we examined
the ChIP-seq results
generated with a novel
shearing method, to see
if we could manage toproduce even fragment
and read distribution.

Future directions
The pipeline is still in an alpha phase, it needs testing, bugfixing, refactoring and some minordevelopments. Later we plan to make it publicly available either as a Galaxy pipeline or as anR package, to make it accessible and user friendly. Also we are always trying the improve thepipeline and we are actively looking for further quality control methods to implement,suchas the IDR software tool which estimates the consistency of peaks between replicates (andpseudo-replicates).

For more information, please contact dominique.poncelet@diagenode.com • For Europe, contact Orders@diagenode.com • For the US and Canada, contact Orders.NA@diagenode.com

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