MNBC: a multithreaded Minimizer-based Naive Bayes Classifier for improved metagenomic sequence classification.

ABSTRACT:

Motivation

State-of-the-art tools for classifying metagenomic sequencing reads provide both rapid and accurate options, although the combination of both in a single tool is a constantly improving area of research. The machine learning-based Naïve Bayes Classifier (NBC) approach provides a theoretical basis for accurate classification of all reads in a sample.

Results

We developed the multithreaded Minimizer-based Naïve Bayes Classifier (MNBC) tool to improve the NBC approach by applying minimizers, as well as plurality voting for closely related classification scores. A standard reference- and test-sequence framework using simulated variable-length reads benchmarked MNBC with six other state-of-the-art tools: MetaMaps, Ganon, Kraken2, KrakenUniq, CLARK, and Centrifuge. We al

SUBMITTER: Lu R 

PROVIDER: S-EPMC11522871 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature