K -mer-based machine learning method to classify LTR-retrotransposons in plant genomes

• Published in: PeerJ (San Francisco, CA) Vol. 9; p. e11456
• Main Authors: Orozco-Arias, Simon, Candamil-Cortés, Mariana S, Jaimes, Paula A, Piña, Johan S, Tabares-Soto, Reinel, Guyot, Romain, Isaza, Gustavo
• Format: Journal Article
• Language: English
• Published: United States PeerJ, Inc 19.05.2021; PeerJ Inc
• Subjects: Algorithms; Bioinformatics; Classification; Computational Science; Data Mining and Machine Learning; Data Science; Datasets; Discriminant analysis; Flowers & plants; Free-alignment approach; Genomes; Genomics; Learning algorithms; LTR retrotransposons; Machine learning; Neural networks; Plant genomes; Plant Science; Principal components analysis; Transposable elements; LTR retrotransposons; Classification; Machine learning; Plant genomes; k-mer based method; Free-alignment approach; Transposable elements
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.11456

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 -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.