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ChIPulate: A comprehensive ChIP-seq simulation pipeline.
- Source :
- PLoS Computational Biology; 3/22/2019, Vol. 15 Issue 3, p1-32, 32p, 1 Diagram, 5 Graphs
- Publication Year :
- 2019
-
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 . [ABSTRACT FROM AUTHOR]
- Subjects :
- CHROMATIN
IMMUNOPRECIPITATION
DNA-binding proteins
GENOMICS
TRANSCRIPTION factors
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X
- Volume :
- 15
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 135634190
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1006921