Frontier

Kashfeen, Anwica; McMillan, Leonard

doi:10.1145/3459930.3469545

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…In addition, DL architectures such as TERL [27], DeepTE [23], and Nakano [28] have been proposed for classification tasks, while Inpactor2 [22] integrated several neural networks for the LTR-RT detection and classification. Several ML-based tools have also been released, such as ClassifyTE [29], Transposon Ultimate [30], and Frontier [31] (for a more complete list see Table 1). Nevertheless, most of the ML and DL approaches used for TE analysis focus their core ML/DL algorithms on a single specific task (detection or classification).…”

Section: Plos Onementioning

confidence: 99%

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

Orozco-Arias,

Lopez-Murillo,

Piña

et al. 2023

PLoS ONE

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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.

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