<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Shaw J</submitter><funding>Natural Sciences and Engineering Research Council of Canada</funding><pagination>151</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11323348</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>12(1)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Metagenomic binning, the clustering of assembled contigs that belong to the same genome, is a crucial step for recovering metagenome-assembled genomes (MAGs). Contigs are linked by exploiting consistent signatures along a genome, such as read coverage patterns. Using coverage from multiple samples leads to higher-quality MAGs; however, standard pipelines require all-to-all read alignments for multiple samples to compute coverage, becoming a key computational bottleneck.&lt;h4>Results&lt;/h4>We present fairy ( https://github.com/bluenote-1577/fairy ), an approximate coverage calculation method for metagenomic binning. Fairy is a fast k-mer-based alignment-free method. For multi-sample binning, fairy can be >250× faster than read alignment and accurate enough for binning. Fairy </pubmed_abstract><journal>Microbiome</journal><pubmed_title>Fairy: fast approximate coverage for multi-sample metagenomic binning.</pubmed_title><pmcid>PMC11323348</pmcid><funding_grant_id>CGS-D</funding_grant_id><funding_grant_id>RGPIN-2022-309 03074</funding_grant_id><pubmed_authors>Yu YW</pubmed_authors><pubmed_authors>Shaw J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Fairy: fast approximate coverage for multi-sample metagenomic binning.</name><description>&lt;h4>Background&lt;/h4>Metagenomic binning, the clustering of assembled contigs that belong to the same genome, is a crucial step for recovering metagenome-assembled genomes (MAGs). Contigs are linked by exploiting consistent signatures along a genome, such as read coverage patterns. Using coverage from multiple samples leads to higher-quality MAGs; however, standard pipelines require all-to-all read alignments for multiple samples to compute coverage, becoming a key computational bottleneck.&lt;h4>Results&lt;/h4>We present fairy ( https://github.com/bluenote-1577/fairy ), an approximate coverage calculation method for metagenomic binning. Fairy is a fast k-mer-based alignment-free method. For multi-sample binning, fairy can be >250× faster than read alignment and accurate enough for binning. Fairy </description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Aug</publication><modification>2025-05-18T12:13:03.576Z</modification><creation>2025-04-05T10:09:15.166Z</creation></dates><accession>S-EPMC11323348</accession><cross_references><pubmed>39143609</pubmed><doi>10.1186/s40168-024-01861-6</doi></cross_references></HashMap>