SUMMARY: Genome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses "Mosdepth arrays" for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results. AVAILABILITY AND IMPLEMENTATION: CoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces.

SUMMARY: Here, we present CompareM2, a genomes-to-report pipeline for comparative analysis of bacterial and archaeal genomes derived from isolates and metagenomic assemblies. CompareM2 is easy to install and operate, designed in such a way that the user can install the complete software in one step and launch all analyses on a set of microbial genomes (bacterial and archaeal) in a single action. The central results generated via the CompareM2 workflow are emphasized in a portable dynamic report document. AVAILABILITY AND IMPLEMENTATION: CompareM2 is a free software that is scalable to a range of project sizes, and welcomes modifications and pull requests from the community on its Git repository at https://github.com/cmkobel/comparem2.

UNLABELLED: Finding and translating stretches of DNA lacking stop codons is a task common in the analysis of sequence data. However, the computational tools for finding open reading frames are sufficiently slow that they are becoming a bottleneck as the volume of sequence data grows. This computational bottleneck is especially problematic in metagenomics when searching unassembled reads, or screening assembled contigs for genes of interest. Here, we present OrfM, a tool to rapidly identify open reading frames (ORFs) in sequence data by applying the Aho-Corasick algorithm to find regions uninterrupted by stop codons. Benchmarking revealed that OrfM finds identical ORFs to similar tools ('GetOrf' and 'Translate') but is four-five times faster. While OrfM is sequencing platform-agnostic, it is best suited to large, high quality datasets such as those produced by Illumina sequencers. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at http://github.com/wwood/OrfM or through GNU Guix under the LGPL 3+ license. OrfM is implemented in C and supported on GNU/Linux and OSX. CONTACTS: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices. Biogem, with its targeted modular and decentralized approach, software generator, tools and tight web integration, is an improved general model for scaling up collaborative open source software development in bioinformatics. AVAILABILITY: Biogem and modules are free and are OSS. Biogem runs on all systems that support recent versions of Ruby, including Linux, Mac OS X and Windows. Further information at http://www.biogems.info. A tutorial is available at http://www.biogems.info/howto.html CONTACT: [email protected].

Background The malarial parasite Plasmodium falciparum remains a leading international cause of mortality, with almost a million deaths each year. Determination of protein subcellular localisation remains a challenge in Plasmodium parasites due to their evolutionary distance from wellstudied model organisms, and limited efficiency of appropriate molecular tools. However, abundant large scale systems biology information exist for several Plasmodium species as well as other apicomplexan parasites, including full genomic DNA sequences, plus data sets relating to the transcriptome, protein expression and interactions, polymorphisms and phyletic profiles. To date, most bioinformatic predictors of sub-cellular localisation use sequence information exclusively without consideration for other data sets.