Mismatch-tolerant, alignment-free sequence classification using multiple spaced seeds and multiindex Bloom filters.

ABSTRACT: Alignment-free classification tools have enabled high-throughput processing of sequencing data in many bioinformatics analysis pipelines primarily due to their computational efficiency. Originally k-mer based, such tools often lack sensitivity when faced with sequencing errors and polymorphisms. In response, some tools have been augmented with spaced seeds, which are capable of tolerating mismatches. However, spaced seeds have seen little practical use in classification because they bring increased computational and memory costs compared to methods that use k-mers. These limitations have also caused the design and length of practical spaced seeds to be constrained, since storing spaced seeds can be costly. To address these challenges, we have designed a probabilistic data structure called

SUBMITTER: Chu J 

PROVIDER: S-EPMC7382288 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature