![]() Understanding the impact of epigenetic architecture on the accessibility of gene promoters and its effect on gene expression patterns is therefore critical for linking chromatin biology to clinical indications. Epigenetic mechanisms have been demonstrated to be necessary for biological programs that are important for a variety of health and disease outcomes. Epigenetics plays a key role in human biology, and dysregulation in epigenetic processes is associated with the pathogenesis of cancer and many other diseases. The field of epigenetic research studies the process by which heritable changes in gene expression occur without underlying alterations in the DNA sequence. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the American Heart Association, National Institutes of Health, or Department of Defense. This work was also partially supported by the Stanford Training Program in Aging Research grant (NIH/NIA T32-AG0047126) and the Army Research Office (ARO), National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a - both awarded to BBK from 2014-2018. Grant information: This work was supported by the American Heart Association (AHA) Postdoctoral Fellowship grant #18POST34030375 (Khomtchouk). The geneXtendeR R/Bioconductor package (including detailed introductory vignettes) is available under the GPL-3 Open Source license and is freely available to download from Bioconductor at: We tested geneXtendeR on 547 human transcription factor ChIP-seq ENCODE datasets and 198 human histone modification ChIP-seq ENCODE datasets, providing the analysis results as case studies. , n th-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to investigate peak summary statistics for the first-closest gene, second-closest gene. geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. Thus, there exists an unmet need to develop a software program that can precisely tailor the computational analysis of a ChIP-seq dataset to the specific peak coordinates of the data and its surrounding genomic features. In addition, varying degrees of tissue-dependent broadness of an epigenetic mark can make it difficult to accurately and reliably link sequencing data to biological function. Functional genomic annotation of histone modification ChIP-seq data can be a particularly challenging task, as chromatin marks that have inherently broad peaks with a diffuse range of signal enrichment (e.g., H3K9me1, H3K27me3) differ significantly from narrow peaks that exhibit a compact and localized enrichment pattern (e.g., H3K4me3, H3K9ac). Since different peak callers are known to produce differentially enriched peaks with a large variance in peak length distribution and total peak count, accurately annotating peak lists with their nearest genes can be an arduous process. Different ChIP-seq peak callers often produce different output results from the same input.
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