PT - JOURNAL ARTICLE AU - Pedro Madrigal TI - fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets AID - 10.1101/060780 DP - 2016 Jan 01 TA - bioRxiv PG - 060780 4099 - http://biorxiv.org/content/early/2016/06/27/060780.short 4100 - http://biorxiv.org/content/early/2016/06/27/060780.full AB - Summary Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomic science, as it allows both to evaluate the reproducibility across biological or technical replicates, and to compare different datasets to identify their potential correlations. Here I present fCCAC, an application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). I exemplify how this method can reveal shared covariance between histone modifications and DNA binding proteins, such as the relationship between the H3K4me3 chromatin mark and its epigenetic writers and readers.Availability R code is publicly available at http://github.com/pmb59/fCCAC/.Contact pm12@sanger.ac.uk