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

PeerJ

article

Simon Orozco-Arias¹  Mariana S. Candamil-Cortés¹  Paula A. Jaimes¹  Johan S. Piña¹  Reinel Tabares-Soto³  Romain Guyot³  Gustavo Isaza² 

[1] Department of Computer Science, Universidad Autónoma de Manizales;Department of Systems and Informatics, Universidad de Caldas;Department of Electronics and Automation, Universidad Autónoma de Manizales;Institut de Recherche pour le Développement

关键词: Transposable elements;    LTR retrotransposons;    Plant genomes;    Machine learning;    Classification;    Free-alignment approach;    k-mer based method;   

DOI  :  10.7717/peerj.11456

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY