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

Publishing date: 2018-12-10

Published on: PLOS ONE

summary: The classification of infections is a routine component of clinical management. Kameris is an open source toolkit for a supervised and alignment-free subtyping method which operates on HIV-1 sequences. Kameris is a software that has been written following best practices, is well tested and is released under the MIT License (MIT). In this article from Solis-Reyes et al, the accuracy and speed of the software is compared to four other well known pieces of software using a series of experiments. Both the experimental data and the software have been archived and are available on Github, making the work described in this academic paper 100% reproducible.

authors: Stephen Solis-Reyes, Mariano Avino, Art Poon, Lila Kari

link to paper: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206409

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