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

BioRxiv : the Preprint Server for Biology
Pedro Madrigal

Abstract

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. R code is publicly available at http://github.com/pmb59/fCCAC/.

Related Concepts

DNA
DNA-Binding Proteins
Nucleic Acids
RNA
Genomics
Analysis
Nucleic Acid Sequencing
Chromatin Immunoprecipitation
Epigenetic Process
Deep Sequencing

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