Genomic object detection: An improved approach for transposable elements detection and classification using convolutional neural networks

Abstract

Analysis of eukaryotic genomes requires the detection and classification of transposable elements (TEs), a crucial but complex and time-consuming task. To improve the performance of tools that accomplish these tasks, Machine Learning approaches (ML) that leverage computer resources, such as GPUs (Graphical Processing Unit) and multiple CPU (Central Processing Unit) cores, have been adopted. However, until now, the use of ML techniques has mostly been limited to classification of TEs. Herein, a detection-classification strategy (named YORO) based on convolutional neural networks is adapted from computer vision (YOLO) to genomics. This approach enables the detection of genomic objects through the prediction of the position, length, and classification in large DNA sequences such as fully sequenced genomes. As a proof of concept, the internal protein-coding domains of LTR-retrotransposons are used to train the proposed neural network. Precision, recall, accuracy, F1-score, execution times and time ratios, as well as several graphical representations were used as metrics to measure performance. These promising results open the door for a new generation of Deep Learning tools for genomics. YORO architecture is available at https://github.com/simonorozcoarias/YORO.

Teaching transposon classification as a means to crowd source the curation of repeat annotation – a tardigrade perspective

“…However, it is hard for curation efforts to keep up with the sheer amount of genomes released every year as curation done by single laboratories may require months or even years for a single genome. Recent developments of machine learningbased tools to automatize the curation and classification processes are promising [29][30][31][32] and there are additional tools to facilitate the curation process like TEAid [13] and EarlGrey [33].…”