Fairy: fast approximate coverage for multi-sample metagenomic binning
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Background
Metagenomic binning, the clustering of assembled contigs that belong to the same genome, is a crucial step for recovering metagenomeassembled genomes (MAGs). Contigs are linked by exploiting consistent read coverage patterns across a genome. 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.
Results
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 is compatible with several existing binners on host and non-host-associated datasets. Using MetaBAT2, fairy recovers 98.5% of MAGs with > 50% completeness and < 5% incompleteness relative to alignment with BWA. Notably, multi-sample binning with fairy is always better than single-sample binning using BWA ( > 1.5 × more > 50% complete MAGs on average) while still being faster. For a public sediment metagenome project, we demonstrate that multisample binning recovers higher quality Asgard archaea MAGs than single-sample binning and that fairy’s results are indistinguishable from read alignment.
Conclusions
Fairy is a new tool for approximately and quickly calculating multi-sample coverage for binning, resolving a longstanding computational bottleneck for metagenomics.
