InpactorDB: A Classified Lineage-Level Plant LTR Retrotransposon Reference Library for Free-Alignment Methods Based on Machine Learning

Genes

Simon Orozco-Arias¹  MarianaS. Candamil¹  PaulaA. Jaimes¹  Romain Guyot²  Reinel Tabares-Soto²  CristianFelipe Jiménez-Varón³  Gustavo Isaza⁴ 

[1] Department of Computer Science, Universidad Autónoma de Manizales, 170002 Manizales, Colombia;Department of Electronics and Automation, Universidad Autónoma de Manizales, 170002 Manizales, Colombia;Department of Physics and Mathematics, Universidad Autónoma de Manizales, 170002 Manizales, Colombia;Department of Systems and Informatics, Universidad de Caldas, 170002 Manizales, Colombia;

关键词: LTR retrotransposons;    machine learning;    deep neural networks;    bioinformatics;    plant genomes;    genomics;   

DOI  :  10.3390/genes12020190

来源: DOAJ

【 摘 要 】

Long terminal repeat (LTR) retrotransposons are mobile elements that constitute the major fraction of most plant genomes. The identification and annotation of these elements via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in genome size variation, LTR retrotransposons are also associated with the function and structure of different chromosomal regions and can alter the function of coding regions, among others. Several sequence databases of plant LTR retrotransposons are available for public access, such as PGSB and RepetDB, or restricted access such as Repbase. Although these databases are useful to identify LTR-RTs in new genomes by similarity, the elements of these databases are not fully classified to the lineage (also called family) level. Here, we present InpactorDB, a semi-curated dataset composed of 130,439 elements from 195 plant genomes (belonging to 108 plant species) classified to the lineage level. This dataset has been used to train two deep neural networks (i.e., one fully connected and one convolutional) for the rapid classification of these elements. In lineage-level classification approaches, we obtain up to 98% performance, indicated by the F1-score, precision and recall scores.

【 授权许可】

Unknown