{"doi":"10.21105/joss.06235","title":"Koverage: Read-coverage analysis for massive(meta)genomics datasets","abstract":"Genomes of organisms are constructed by assembling sequence reads from whole genome sequencing.It is useful to determine sequence read-coverage of genome assemblies, for instance identifying duplication or deletion events, identifying related contigs for binning metagenomes (Mallawaarachchi et al., 2021;Mallawaarachchi & Lin, 2022), or analysing taxonomic compositions of metagenomes (Wu et al., 2023).Although calculating readcoverage is a routine task, it typically involves several complete read and write operations (I/O operations).This is not a problem for small datasets, but can be a significant bottleneck for very large datasets.Koverage reduces I/O burden as much as possible to enable maximum scalability.Koverage includes a kmer-based method that significantly reduces the computational complexity for very large reference genomes.Koverage uses Snakemake (Mölder et al., 2021), providing out-of-the-box support for HPC and cloud environments.It utilises the Snaketool (Roach, Pierce-Ward, et al., 2022) command line interface, and is installable with PIP or Conda for maximum ease of use.","journal":"The Journal of Open Source Software","year":2024,"id":441970,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8881,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1254993,"name":"Bradley J. Hart","orcid":"0000-0001-8110-2460","position":1,"is_corresponding":false},{"id":242870,"name":"Sarah J. Beecroft","orcid":"0000-0002-3935-2279","position":2,"is_corresponding":false},{"id":877855,"name":"Bhavya Papudeshi","orcid":"0000-0001-5359-3100","position":3,"is_corresponding":false},{"id":768979,"name":"Laura K. Inglis","orcid":"0000-0001-7919-8563","position":4,"is_corresponding":false},{"id":974674,"name":"Susanna R. Grigson","orcid":"0000-0003-4738-3451","position":5,"is_corresponding":false},{"id":877854,"name":"Vijini Mallawaarachchi","orcid":"0000-0002-2651-8719","position":6,"is_corresponding":false},{"id":881962,"name":"George Bouras","orcid":"0000-0002-5885-4186","position":7,"is_corresponding":false},{"id":42081,"name":"Robert A. Edwards","orcid":"0000-0001-8383-8949","position":8,"is_corresponding":false},{"id":763109,"name":"Michael J. Roach","orcid":"0000-0003-1488-5148","position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-19T02:01:11.152920Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}