TY - JOUR T1 - fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets JF - bioRxiv DO - 10.1101/060780 SP - 060780 AU - Pedro Madrigal Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/06/27/060780.abstract N2 - 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 ER -