CONSULT: accurate contamination removal using locality-sensitive hashing.

ABSTRACT: A fundamental question appears in many bioinformatics applications: Does a sequencing read belong to a large dataset of genomes from some broad taxonomic group, even when the closest match in the set is evolutionarily divergent from the query? For example, low-coverage genome sequencing (skimming) projects either assemble the organelle genome or compute genomic distances directly from unassembled reads. Using unassembled reads needs contamination detection because samples often include reads from unintended groups of species. Similarly, assembling the organelle genome needs distinguishing organelle and nuclear reads. While k-mer-based methods have shown promise in read-matching, prior studies have shown that existing methods are insufficiently sensitive for contamination detection. Here, w

SUBMITTER: Rachtman E 

PROVIDER: S-EPMC8340999 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature