Accounting for immunoprecipitation efficiencies in the statistical analysis of ChIP-seq data
1 School of Information Systems, Computing and Mathematics, Brunel University, London, UK
2 Institute of Mathematics and Computing Science, University of Groningen, Groningen, The Netherlands
3 Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
4 Netherlands Bioinformatics Centre, Nijmegen, The Netherlands
BMC Bioinformatics 2013, 14:169 doi:10.1186/1471-2105-14-169Published: 30 May 2013
ImmunoPrecipitation (IP) efficiencies may vary largely between different antibodies and between repeated experiments with the same antibody. These differences have a large impact on the quality of ChIP-seq data: a more efficient experiment will necessarily lead to a higher signal to background ratio, and therefore to an apparent larger number of enriched regions, compared to a less efficient experiment. In this paper, we show how IP efficiencies can be explicitly accounted for in the joint statistical modelling of ChIP-seq data.
We fit a latent mixture model to eight experiments on two proteins, from two laboratories where different antibodies are used for the two proteins. We use the model parameters to estimate the efficiencies of individual experiments, and find that these are clearly different for the different laboratories, and amongst technical replicates from the same lab. When we account for ChIP efficiency, we find more regions bound in the more efficient experiments than in the less efficient ones, at the same false discovery rate. A priori knowledge of the same number of binding sites across experiments can also be included in the model for a more robust detection of differentially bound regions among two different proteins.
We propose a statistical model for the detection of enriched and differentially bound regions from multiple ChIP-seq data sets. The framework that we present accounts explicitly for IP efficiencies in ChIP-seq data, and allows to model jointly, rather than individually, replicates and experiments from different proteins, leading to more robust biological conclusions.