Orozco-Arias S. auteur aut IRD Candamil-Cortes M. S. auteur aut IRD Jaimes P. A. auteur aut IRD Pina J. S. auteur aut IRD Tabares-Soto R. auteur aut IRD Guyot Romain auteur aut IRD Isaza G. auteur aut IRD text journalArticle eng text/pdf reformatted digital access Every day more plant genomes are available in public databases and additional massive sequencing projects (i.e., that aim to sequence thousands of individuals) are formulated and released. Nevertheless, there are not enough automatic tools to analyze this large amount of genomic information. LTR retrotransposons are the most frequent repetitive sequences in plant genomes; however, their detection and classification are commonly performed using semi-automatic and time-consuming programs. Despite the availability of several bioinformatic tools that follow different approaches to detect and classify them, none of these tools can individually obtain accurate results. Here, we used Machine Learning algorithms based on k-mer counts to classify LTR retrotransposons from other genomic sequences and into lineages/families with an F1-Score of 95%, contributing to develop a free-alignment and automatic method to analyze these sequences. specialized Transposable elements LTR retrotransposons Plant genomes Machine learning Classification Free-alignment approach k-mer based method 076 020 122 9 e11456 [20 p.] 2021 2167-8359 https://www.documentation.ird.fr/hor/fdi:010081522 10.7717/peerj.11456 2167-8359 [F B010081522] https://www.documentation.ird.fr/hor/fdi:010081522 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2021-07/010081522.pdf IRD - Base Horizon / Pleins textes 2021-07-02 2021-07-02 fdi:010081522 fre