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Aryee, M.

Publications and source records attributed to Aryee, M..

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hichipper: A preprocessing pipeline for assessing library quality and DNA loops from HiChIP data

Mumbach et al. recently described HiChIP, a novel protein-mediated chromatin conformation assay that lowers cellular input requirements while simultaneously increasing the yield of informative reads compared to previous methods (1). To facilitate the dissemination and adoption of this assay, we introduce hichipper (http://aryeelab.org/hichipper), an open-source HiChIP data preprocessing tool, with features that include bias-corrected peak calling, library quality control, DNA loop calling, and output of processed data for downstream analysis and visualization (Figure 1a).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC=\"FIGDIR/small/192302_fig1.gif\" ALT=\"Figure 1\">\nView larger version (23K):\norg.highwire.dtl.DTLVardef@1d3d143org.highwire.dtl.DTLVardef@14fb9eforg.highwire.dtl.DTLVardef@1381541org.highwire.dtl.DTLVardef@fb6114_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO a) Overview of the hichipper analysis pipeline. hichipper requires aligned and annotated in-teraction files from preprocessing tools such as Hi-C Pro (2) as well as a .bed file of restriction sites. The hichipper pipeline identifies loop anchors using a background model that accounts for the effect of restriction site proximity on read density. The output of hichipper can be used for quality control, visualization, and downstream topology analysis of HiChIP data. b) Ratio of per-base coverage to local MACS-estimated local window background signal as a function of distance to nearest MboI cutsite for a HiChIP sample (blue) and a ChIP-seq sample (red). Both samples represent published mouse ESC with SMC1 (cohesin) ChIP. C_FIG

bioinformatics

diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data

The three-dimensional architecture of DNA within the nucleus is a key determinant of interactions between genes, regulatory elements, and transcriptional machinery. As a result, differences in loop structure are associated with differences in gene expression and cell state. Here, we introduce diffloop, an R/Bioconductor package for identifying differential DNA looping between samples. The package additionally provides a suite of functions for the quality control, statistical testing, annotation and visualization of DNA loops. We demonstrate this functionality by detecting differences in DNA loops between ENCODE ChIA-PET datasets and relate looping to differences in epigenetic state and gene expression.

bioinformatics