SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: 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. Availability and Implementation: An R/Bioconductor package is available at http://bioconductor.org/packages/fCCAC/ . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.

This July, PLoS Computational Biology invited attendees of ISMB/ECCB 2011 (http://www.iscb.org/ismbeccb2011) to send us short reports of conference highlights in the guise of PLoS Conference Postcards. Philip E. Bourne, Editor-in-Chief, selected three Postcards, which we received from Poland, Germany, and the United States of America. If the reports below capture your interest, you can find Postcards from past conferences in our recent collection: http://collections.plos.org/ploscompbiol/conferencepostcards.