Koverage

Koverage: Read-coverage analysis for massive (meta)genomics datasets - Published in JOSS (2024)

https://github.com/beardymcjohnface/koverage

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Published in Journal of Open Source Software

Scientific Fields

Engineering Computer Science - 40% confidence

Last synced: 6 months ago · JSON representation ·

Repository

Quickly get coverage statistics given reads and an assembly

Basic Info

Host: GitHub
Owner: beardymcjohnface
License: mit
Language: Python
Default Branch: main
Size: 18.9 MB

Statistics

Stars: 16
Watchers: 4
Forks: 5
Open Issues: 0
Releases: 12

Created almost 3 years ago · Last pushed almost 2 years ago

Metadata Files

Readme Contributing License Code of conduct Citation

README.md

GitHub last commit (branch)

Quickly get coverage statistics given reads and an assembly.

Motivation

While there are tools that will calculate read-coverage statistics, they do not scale particularly well for large datasets, large sample numbers, or large reference FASTAs. Koverage is designed to place minimal burden on I/O and RAM to allow for maximum scalability.

Install

Koverage is available on PyPI and Bioconda.

Recommend create environment for installation:

shell conda create -n koverage python=3.11 conda activate koverage

Option 1: install with PyPI

shell pip install koverage

Option 2: install with Bioconda

shell conda install -c conda-forge -c bioconda koverage

Test the installation:

shell koverage test

Developer install:

shell git clone https://github.com/beardymcjohnface/Koverage.git cd Koverage pip install -e .

Usage

Get coverage statistics from mapped reads (default method).

shell koverage run --reads readDir --ref assembly.fasta

Get coverage statistics using kmers (scales much better for very large reference FASTAs).

shell koverage run --reads readDir --ref assembly.fasta kmer

Any unrecognised commands are passed onto Snakemake. Run Koverage on a HPC using a Snakemake profile.

shell koverage run --reads readDir --ref assembly.fasta --profile mySlurmProfile

Parsing samples with `--reads`

You can pass either a directory of reads or a TSV file to --reads. Note that Koverage expects your read file names to include R1 or R2 e.g. Tynes-BDA-rw-1S14L001R1001.fastq.gz or SRR7141305R2.fastq.gz. - _Directory:__ Koverage will infer sample names and _R1/_R2 pairs from the filenames. - TSV file: Koverage expects 2 or 3 columns, with column 1 being the sample name and columns 2 and 3 the reads files.

More information and examples are available here

Test

You can test the methods with the inbuilt dataset like so.

```shell

test default method

koverage test

test all methods

koverage test map kmer coverm ```

Coverage methods

Mapping-based (default)

```shell koverage run ...

or

koverage run ... map ```

This method will map reads using minimap2 and use the mapping coordinates to calculate coverage. This method is suitable for most applications.

Kmer-based

shell koverage run ... kmer

This method calculates Jellyfish databases of the sequencing reads. It samples kmers from all reference contigs and queries them from the Jellyfish DBs to calculate coverage statistics. This method is exceptionally fast for very large reference genomes.

CoverM

shell koverage run ... coverm

We've included a wrapper for CoverM which you may find useful. The wrapper manually runs minimap2 and then invokes CoverM on the sorted BAM file. It then combines the output from all samples like the other methods. If you have a large tempfs/ you'll probably find it faster to run CoverM directly on your reads. CoverM is not currently available for MacOS.

Outputs

Mapping-based

Default output files using fast estimations for mean, median, hitrate, and variance.

sample_coverage.tsv

Per sample and per contig counts. Column | description --- | --- Sample | Sample name derived from read file name Contig | Contig ID from assembly FASTA Count | Raw mapped read count RPM | Reads per million RPKM | Reads per kilobase million RPK | Reads per kilobase TPM | Transcripts per million Mean | _Estimated_ mean read depth Median | _Estimated_ median read depth Hitrate | _Estimated_ fraction of contig with depth > 0 Variance | _Estimated_ read depth variance

all_coverage.tsv

Per contig counts (all samples). Column | description --- | --- Contig | Contig ID from assembly FASTA Count | Raw mapped read count RPM | Reads per million RPKM | Reads per kilobase million RPK | Reads per kilobase TPM | Transcripts per million

Kmer-based

Outputs for kmer-based coverage metrics. Kmer outputs are gzipped as it is anticipated that this method will be used with very large reference FASTA files.

sample_kmer_coverage.NNmer.tsv.gz

Per sample and contig kmer coverage. Column | description --- | --- Sample | Sample name derived from read file name Contig | Contig ID from assembly FASTA Sum | Sum of sampled kmer depths Mean | Mean sampled kmer depth Median | Median sampled kmer depth Hitrate | Fraction of kmers with depth > 0 Variance | Variance of lowest 95 % of sampled kmer depths

all_kmer_coverage.NNmer.tsv.gz

Contig kmer coverage (all samples). Column | description --- | --- Contig | Contig ID from assembly FASTA Sum | Sum of sampled kmer depths Mean | Mean sampled kmer depth Median | Median sampled kmer depth

Owner

Name: Michael Roach
Login: beardymcjohnface
Kind: user
Company: Flinders University

Website: bioinf.cc
Twitter: beardymcface
Repositories: 6
Profile: https://github.com/beardymcjohnface

JOSS Publication

Koverage: Read-coverage analysis for massive (meta)genomics datasets

Published

February 27, 2024

DOI

10.21105/joss.06235

Volume 9, Issue 94, Page 6235

Authors

Michael J. Roach

Flinders Accelerator for Microbiome Exploration, Flinders University, Adelaide, SA, Australia, Adelaide Centre for Epigenetics and the South Australian Immunogenomics Cancer Institute, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia

Bradley J. Hart

Health and Biomedical Innovation, Clinical and Health Sciences, University of South Australia, SA, Australia

Sarah J. Beecroft

Pawsey Supercomputing Research Centre, Kensington, WA, Australia

Bhavya Papudeshi