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Abstract
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.
Academic Publication CoverM: read alignment statistics for metagenomics
Research Abstract & Technology Focus
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What is the core focus of the research titled 'CoverM: read alignment statistics for metagenomics'?
This literature focuses on: Abstract 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 analys...
What other academic literature is closely related to 'CoverM: read alignment statistics for metagenomics'?
Yes, highly correlated activity was mapped. An entry titled 'CoverM: read alignment statistics for metagenomics' discusses this: Abstract Summary Genome-centric analysis of metagenomic samples is a powerful method for unders...
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