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2.7. ROBUST DETECTION OF CHIP-SEQ ENRICHMENT 45
2.7 Robust Detection of ChIP-Seq Enrichment
Investigation of protein-DNA interaction by ChIP-Seq can help to further our understanding of
cellular regulatory networks (section 1.3.2). Primary data analysis for ChIP-Seq experiments
requires software tools that can pinpoint hundreds to thousands of putative protein binding sites
from data sets of high-throughput sequencing read alignments (section 1.5.6).
This section discusses the peak calling pipeline SHORE peak integrated into the SHORE analysis
suite. The utility is able to discriminate true enrichment from common artifact patterns using
various robust heuristics, calculates false discovery rates (FDR) by interrogation of control experiments
and implements a unique joint detection, separate evaluation approach to handling of
replicate experiments. The program outputs locations of possible binding regions together with
their FDR and various additional features potentially useful for ranking, ltering or ne-tuning
the detection. It further enables users to generate graphical representations of peak regions,
put detected loci into the context of available genome annotation or to extract binding region
sequences for subsequent motif sampling and analysis.
Our pipeline is pre-congured for straightforward detection of dened loci of ChIP-Seq enrichment
as produced by transcription factor (TF) binding sites. Settings may however be adjusted
to support the detection of clusters of enrichment that occur e. g. in data representing states
of histone modication. Individual algorithms are implemented as pluggable modules and have
been brought over to multi-purpose tools SHORE coverage and SHORE count, allowing ne-grained
control and exible applicability of enrichment detection in diverse scenarios such as MNase-Seq
assays or assessment of copy number variation (CNV).
The SHORE peak pipeline has been outlined [58, 137] and further employed [138141] in multiple
biological publications.
2.7.1 Overview
Were sequencing reads randomly sampled from the genome, sequencing depth should be distributed
according to the Poisson distribution whose parameter equals the average depth [142].
Although high-throughput sequencing is known to be highly non-random, with biases that bring
about considerable deviation from the Poisson model (section 1.4), the distribution can still
present a powerful tool for the detection of irregularities in sequencing depth at high sensitivity.
Our peak calling algorithm consists of two major steps. The rst pass is a detection phase that
employs a relaxed Poisson assumption to the read alignment data generated through the ChIP-
Seq experiment to identify regions of putative enrichment at high sensitivity. In the subsequent
second pass implemented to improve specicity of the prediction, these candidate regions are
subjected to several robust empirical rules for artifact removal and further tested against control
experiment data to assess signicance of the enrichment.
2.7.2 Correcting for Duplicated Sequences
The ChIP-Seq procedure yields quantities of DNA that are typically orders of magnitude below
the amounts retrieved by other methods of DNA sampling. The excess PCR rounds required
to obtain a compliant sequencing library often bring about signicant amounts of duplicated
sequences, reducing library complexity. PCR may entail signicant sequence bias (section 1.4)
with erratic rates of fragment duplication.
Duplicate sequences represent the same fragment of DNA and therefore violate the assumption
that each sequencing read is sampled independently from the set of sequences under examination.
During detection of sequence enrichment, the presence of duplicate sequences must thus be
explicitly accounted for by the predictive model, or these sequences must be pruned from the