In silico pooling of ChIP-seq control experiments.
As next generation sequencing technologies are becoming more economical, large-scale ChIP-seq studies are enabling the investigation of the roles of transcription factor binding and epigenome on phenotypic variation. Studying such variation requires individual level ChIP-seq experiments. Standard designs for ChIP-seq experiments employ a paired control per ChIP-seq sample. Genomic coverage for control experiments is often sacrificed to increase the resources for ChIP samples. However, the quality of ChIP-enriched regions identifiable from a ChIP-seq experiment depends on the quality and the coverage of the control experiments. Insufficient coverage leads to loss of power in detecting enrichment. We investigate the effect of in silico pooling of control samples within multiple biological replicates, multiple treatment conditions, and multiple cell lines and tissues across multiple datasets with varying levels of genomic coverage. Our computational studies suggest guidelines for performing in silico pooling of control experiments. Using vast amounts of ENCODE data, we show that pairwise correlations between control samples originating from multiple biological replicates, treatments, and cell lines/tissues can be grouped into two classes representing whether or not in silico pooling leads to power gain in detecting enrichment between the ChIP and the control samples. Our findings have important implications for multiplexing samples.
Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America.
Guannan
G
Sun
Waisman Center, Department of Comparative Biosciences, University of Wisconsin, Madison, Wisconsin, United States of America.
Rajini
R
Srinivasan
Waisman Center, Department of Comparative Biosciences, University of Wisconsin, Madison, Wisconsin, United States of America.
Camila
C
Lopez-Anido
Waisman Center, Department of Comparative Biosciences, University of Wisconsin, Madison, Wisconsin, United States of America.
Holly A
HA
Hung
Waisman Center, Department of Comparative Biosciences, University of Wisconsin, Madison, Wisconsin, United States of America.
John
J
Svaren
Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America.
Sündüz
S
Keleş
D000818:Animals; D000076246:CCCTC-Binding Factor; D047369:Chromatin Immunoprecipitation; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D059014:High-Throughput Nucleotide Sequencing; D006655:Histone Deacetylases / Q000235:genetics; D006657:Histones / Q000235:genetics; D006801:Humans; D048031:JNK Mitogen-Activated Protein Kinases / Q000235:genetics; D020014:K562 Cells; D051381:Rats; D012097:Repressor Proteins / Q000235:genetics
D000076246:CCCTC-Binding Factor; C000619697:CTCF protein, human; D006657:Histones; D012097:Repressor Proteins; D048031:JNK Mitogen-Activated Protein Kinases; C409470:HDAC8 protein, human; D006655:Histone Deacetylases