CONSULT-II: accurate taxonomic identification and profiling using locality-sensitive hashing.

ABSTRACT:

Motivation

Taxonomic classification of short reads and taxonomic profiling of metagenomic samples are well-studied yet challenging problems. The presence of species belonging to groups without close representation in a reference dataset is particularly challenging. While k-mer-based methods have performed well in terms of running time and accuracy, they tend to have reduced accuracy for such novel species. Thus, there is a growing need for methods that combine the scalability of k-mers with increased sensitivity.

Results

Here, we show that using locality-sensitive hashing (LSH) can increase the sensitivity of the k-mer-based search. Our method, which combines LSH with several heuristics techniques including soft lowest common ancestor labeling and voting, is more accurate th

SUBMITTER: Sapcı AOB 

PROVIDER: S-EPMC10985673 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature