SUMMARY: The tidyCoverage R package provides a framework for intuitive investigation of collections of genomic tracks over genomic features, relying on the principle of tidy data manipulation. It defines two data structures, CoverageExperiment and AggregatedCoverage classes, directly extending the SummarizedExperiment fundamental class, and introduces a principled approach to exploring genome-wide data. This infrastructure facilitates the extraction and manipulation of genomic coverage track data across individual or multiple sets of thousands of genomic loci. This allows the end user to rapidly visualize track coverage at individual genomic loci or aggregated coverage profiles over sets of genomic loci. tidyCoverage seamlessly combines with the existing Bioconductor ecosystem to accelerate the integration of genome-wide track data in epigenomic analysis workflows. tidyCoverage emerges as a valuable tool, contributing to the advancement of epigenomics research by promoting consistency, reproducibility, and accessibility in data analysis. AVAILABILITY AND IMPLEMENTATION: tidyCoverage is an R package freely available from Bioconductor ≥ 3.19 (https://www.bioconductor.org/packages/tidyCoverage) for R ≥ 4.4. The software is distributed under the MIT License and is accompanied by example files and data.

MOTIVATION: Hi-C contact maps reflect the relative contact frequencies between pairs of genomic loci, quantified through deep sequencing. Differential analyses of these maps enable downstream biological interpretations. However, the multi-fractal nature of the chromatin polymer inside the cellular envelope results in contact frequency values spanning several orders of magnitude: contacts between loci pairs separated by large genomic distances are much sparser than closer pairs. The same is true for poorly covered regions, such as repeated sequences. Both distant and poorly covered regions translate into low signal-to-noise ratios. There is no clear consensus to address this limitation. RESULTS: We present Serpentine, a fast, flexible procedure operating on raw data, which considers the contacts in each region of a contact map. Binning is performed only when necessary on noisy regions, preserving informative ones. This results in high-quality, low-noise contact maps that can be conveniently visualized for rigorous comparative analyses. AVAILABILITY AND IMPLEMENTATION: Serpentine is available on the PyPI repository and https://github.com/koszullab/serpentine; documentation and tutorials are provided at https://serpentine.readthedocs.io/en/latest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATIONS: De novo sequencing of genomes is followed by annotation analyses aiming at identifying functional genomic features such as genes, non-coding RNAs or regulatory sequences, taking advantage of diverse datasets. These steps sometimes fail at detecting non-coding functional sequences: for example, origins of replication, centromeres and rDNA positions have proven difficult to annotate with high confidence. Here, we demonstrate an unconventional application of Chromosome Conformation Capture (3C) technique, which typically aims at deciphering the average 3D organization of genomes, by showing how functional information about the sequence can be extracted solely from the chromosome contact map. RESULTS: Specifically, we describe a combined experimental and bioinformatic procedure that determines the genomic positions of centromeres and ribosomal DNA clusters in yeasts, including species where classical computational approaches fail. For instance, we determined the centromere positions in Naumovozyma castellii, where these coordinates could not be obtained previously. Although computed centromere positions were characterized by conserved synteny with neighboring species, no consensus sequences could be found, suggesting that centromeric binding proteins or mechanisms have significantly diverged. We also used our approach to refine centromere positions in Kuraishia capsulata and to identify rDNA positions in Debaryomyces hansenii. Our study demonstrates how 3C data can be used to complete the functional annotation of eukaryotic genomes. AVAILABILITY AND IMPLEMENTATION: The source code is provided in the Supplementary Material. This includes a zipped file with the Python code and a contact matrix of Saccharomyces cerevisiae. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.