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fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets

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Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomic science, as it allows both to evaluate reproducibility of biological or technical replicates, and to compare different datasets to identify their potential correlations. Here we 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). We show how this method differs from other measures of correlation, and exemplify how it 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.



Chromatin, Chromatin Immunoprecipitation, Epigenomics, High-Throughput Nucleotide Sequencing, Histones, Humans, Methylation, Protein Processing, Post-Translational, Reproducibility of Results, Sequence Analysis, DNA, Software

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Oxford University Press
Medical Research Council (MC_PC_12009)
This work was supported by the ERC starting grant Relieve-IMDs and core support grant from the Wellcome Trust and MRC to the Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute.