Abstract
Genomes computationally inferred from large metagenomic data sets are often incomplete and may be missing functionally important content and strain variation. We introduce an information retrieval system for large metagenomic data sets that exploits the sparsity of DNA assembly graphs to efficiently extract subgraphs surrounding an inferred genome. We apply this system to recover missing content from genome bins and show that substantial genomic sequence variation is present in a real metagenome. Our software implementation is available at https://github.com/spacegraphcats/spacegraphcatsunder the 3-Clause BSD License.
Highlights
Genomes computationally inferred from large metagenomic data sets are often incomplete and may be missing functionally important content and strain variation
We compute dominators so that the minimum distance from every vertex in the compact De Bruijn graph (cDBG) to some dominator is at most r using Algorithm 1, which is based on the linear-time approximation algorithm given by Dvorák and Reidl [24]
We extend previous work showing that assembly-based methods are fragile to strain variation, and provide an alternative workflow that substantially broadens our ability to characterize metagenome content
Summary
Genomes computationally inferred from large metagenomic data sets are often incomplete and may be missing functionally important content and strain variation. We introduce an information retrieval system for large metagenomic data sets that exploits the sparsity of DNA assembly graphs to efficiently extract subgraphs surrounding an inferred genome. We apply this system to recover missing content from genome bins and show that substantial genomic sequence variation is present in a real metagenome. Our software implementation is available at https://github.com/ spacegraphcats/spacegraphcats under the 3-Clause BSD License
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