An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes.

ABSTRACT: For many disease-causing virus species, global diversity is clustered into a taxonomy of subtypes with clinical significance. In particular, the classification of infections among the subtypes of human immunodeficiency virus type 1 (HIV-1) is a routine component of clinical management, and there are now many classification algorithms available for this purpose. Although several of these algorithms are similar in accuracy and speed, the majority are proprietary and require laboratories to transmit HIV-1 sequence data over the network to remote servers. This potentially exposes sensitive patient data to unauthorized access, and makes it impossible to determine how classifications are made and to maintain the data provenance of clinical bioinformatic workflows. We propose an open-source super

SUBMITTER: Solis-Reyes S 

PROVIDER: S-EPMC6235296 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature