Format

Send to

Choose Destination
PLoS Comput Biol. 2019 Mar 21;15(3):e1006921. doi: 10.1371/journal.pcbi.1006921. eCollection 2019 Mar.

ChIPulate: A comprehensive ChIP-seq simulation pipeline.

Author information

1
Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, TIFR, Bengaluru, Karnataka, India.
2
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America.
3
The Institute of Mathematical Sciences/HBNI, Taramani, Chennai, India.

Abstract

ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) is a high-throughput technique to identify genomic regions that are bound in vivo by a particular protein, e.g., a transcription factor (TF). Biological factors, such as chromatin state, indirect and cooperative binding, as well as experimental factors, such as antibody quality, cross-linking, and PCR biases, are known to affect the outcome of ChIP-seq experiments. However, the relative impact of these factors on inferences made from ChIP-seq data is not entirely clear. Here, via a detailed ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength. We find that the TF motif can be recovered despite poor and non-uniform extraction and PCR amplification efficiencies. The recovery of the motif is, however, affected to a larger extent by the fraction of sites that are either cooperatively or indirectly bound. Importantly, our simulations reveal that the number of ChIP-seq replicates needed to accurately measure in vivo occupancy at high-affinity sites is larger than the recommended community standards. Our results establish statistical limits on the accuracy of inferences of protein-DNA binding from ChIP-seq and suggest that increasing the mean extraction efficiency, rather than amplification efficiency, would better improve sensitivity. The source code and instructions for running ChIPulate can be found at https://github.com/vishakad/chipulate.

PMID:
30897079
PMCID:
PMC6445533
DOI:
10.1371/journal.pcbi.1006921
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center