SUMMARY: Modified nucleotides play a crucial role in gene expression regulation. Here, we describe methplotlib, a tool developed for the visualization of modified nucleotides detected from Oxford Nanopore Technologies sequencing platforms, together with additional scripts for statistical analysis of allele-specific modification within-subjects and differential modification frequency across subjects. AVAILABILITY AND IMPLEMENTATION: The methplotlib command-line tool is written in Python3, is compatible with Linux, Mac OS and the MS Windows 10 Subsystem for Linux and released under the MIT license. The source code can be found at https://github.com/wdecoster/methplotlib and can be installed from PyPI and bioconda. Our repository includes test data, and the tool is continuously tested at travis-ci.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Complex genomic analyses often use sequences of simple set operations like intersection, overlap and nearest on genomic intervals. These operations, coupled with some custom programming, allow a wide range of analyses to be performed. To this end, we have written PyRanges, a data structure for representing and manipulating genomic intervals and their associated data in Python. Run single threaded on binary set operations, PyRanges is in median 2.3-9.6 times faster than the popular R GenomicRanges library and is equally memory efficient; run multi-threaded on 8 cores, our library is up to 123 times faster. PyRanges is therefore ideally suited both for individual analyses and as a foundation for future genomic libraries in Python. AVAILABILITY AND IMPLEMENTATION: PyRanges is available as open source under the MIT license at https://github.com/biocore-NTNU/pyranges and the documentation exists at https://biocore-NTNU.github.io/pyranges/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Data from chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) generally contain either narrow peaks or broad and diffusely enriched domains. The SICER ChIP-seq caller has proven adept at finding diffuse domains in ChIP-seq data, but it is slow, requires much memory, needs manual installation steps and is hard to use. epic2 is a complete rewrite of SICER that is focused on speed, low memory overhead and ease-of-use. AVAILABILITY AND IMPLEMENTATION: The MIT-licensed code is available at https://github.com/biocore-ntnu/epic2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.