Normalization of ChIP-seq data with control
1 Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
2 Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA
3 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
BMC Bioinformatics 2012, 13:199 doi:10.1186/1471-2105-13-199Published: 10 August 2012
ChIP-seq has become an important tool for identifying genome-wide protein-DNA interactions, including transcription factor binding and histone modifications. In ChIP-seq experiments, ChIP samples are usually coupled with their matching control samples. Proper normalization between the ChIP and control samples is an essential aspect of ChIP-seq data analysis.
We have developed a novel method for estimating the normalization factor between the ChIP and the control samples. Our method, named as NCIS (Normalization of ChIP-seq) can accommodate both low and high sequencing depth datasets. We compare statistical properties of NCIS against existing methods in a set of diverse simulation settings, where NCIS enjoys the best estimation precision. In addition, we illustrate the impact of the normalization factor in FDR control and show that NCIS leads to more power among methods that control FDR at nominal levels.
Our results indicate that the proper normalization between the ChIP and control samples is an important step in ChIP-seq analysis in terms of power and error rate control. Our proposed method shows excellent statistical properties and is useful in the full range of ChIP-seq applications, especially with deeply sequenced data.