ChIPulate: A comprehensive ChIP-seq simulation pipeline.
Title | ChIPulate: A comprehensive ChIP-seq simulation pipeline. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Datta V, Hannenhalli S, Siddharthan R |
Journal | PLoS Comput Biol |
Volume | 15 |
Issue | 3 |
Pagination | e1006921 |
Date Published | 2019 Mar 21 |
ISSN | 1553-7358 |
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. |
DOI | 10.1371/journal.pcbi.1006921 |
Alternate Journal | PLoS Comput. Biol. |
PubMed ID | 30897079 |