Article

False positive peaks in ChIP-seq and other sequencing-based functional assays caused by unannotated high copy number regions

Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
Bioinformatics (Impact Factor: 4.62). 06/2011; 27(15):2144-6. DOI: 10.1093/bioinformatics/btr354
Source: PubMed

ABSTRACT Sequencing-based assays such as ChIP-seq, DNase-seq and MNase-seq have become important tools for genome annotation. In these assays, short sequence reads enriched for loci of interest are mapped to a reference genome to determine their origin. Here, we consider whether false positive peak calls can be caused by particular type of error in the reference genome: multicopy sequences which have been incorrectly assembled and collapsed into a single copy.
Using sequencing data from the 1000 Genomes Project, we systematically scanned the human genome for regions of high sequencing depth. These regions are highly enriched for erroneously inferred transcription factor binding sites, positions of nucleosomes and regions of open chromatin. We suggest a simple masking procedure to remove these regions and reduce false positive calls.
Files for masking out these regions are available at eqtl.uchicago.edu

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    • "In the literature, this effect has been appreciated in peak calling (Pickrell et al., 2011; Rashid et al., 2011; Ashoor et al., 2013), and in differential epigenome (ChIP-seq and RNA-seq) analyses (Robinson et al., 2012). As of today, none of the multi-read mapping methods has considered the potential effect of CNV on multi-read allocation and the power it might provide for discriminating the mapping locations of multi-reads. "
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    ABSTRACT: In chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) and other short read sequencing experiments, a considerable fraction of the short reads align to multiple locations on the reference genome (multi-reads). Inferring the origin of multi-reads is critical for accurately mapping reads to repetitive regions. Current state-of-the-art multi-read allocation algorithms rely on the read counts in the local neighbourhood of the alignment locations and ignore the variation in the copy-numbers of these regions. Copy-number variation (CNV) can directly affect the read densities and, therefore, bias allocation of multi-reads.
    Bioinformatics 06/2014; 30(20). DOI:10.1093/bioinformatics/btu402 · 4.62 Impact Factor
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    • "And it is well appreciated that characteristics of the reference genome influence the mapping results, for example, some sequences in the genome are present in multiple copies, leading to ambiguity when determining the origin of sequencing reads [12]. Some sequences which present in a single copy on the available reference genome are present in multiple copies in all or some individuals in reality [13]. So the sequence structure of human centromeres may have an important impact on the generation of EHPs. "
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