<i>K</i>-mer-based machine learning method to classify LTR-retrotransposons in plant genomes

Orozco-Arias, Simón; Candamil-Cortés, Mariana S.; Jaimes, Paula A.; Piña, Johan S.; Tabares-Soto, Reinel; Guyot, Romain; Isaza, Gustavo

doi:10.7717/peerj.11456

Cited by 17 publications

(6 citation statements)

References 66 publications

(95 reference statements)

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“…Basically, a bunch of LTR-RT taken from InpactorDB [ 15 ] was randomly placed inside an entire DNA sequence with a fixed length of 50, 000 bp. The nucleotides filling the space between one LTR-RT and another corresponded to sequences that are known to not contain LTR-RT (negative data set taken from [ 45 ] DOI: 10.5281/zenodo.4543904 , See Methodology section). After the synthetic creation of DNA sequences, they were transformed into a one-hot 2D representation and they were used as features for training the CNN.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…Create a synthetic DNA sequence of 50, 000 bp by concatenating sequences known to not include any LTR-RT (i.e coding sequences, different types of RNA like mRNA, tRNA, non-coding RNA, and other types of TEs such as TEs Class II) from [ 45 ] DOI: 10.5281/zenodo.4543904 . These sequences are called “negative background”.…”

Section: Methodsmentioning

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

Self Cite

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“…Due to the categorical nature of genomic data, this activity is crucial to be able to use ML models [ 36 ]. K -mers frequencies were used as features using 1 ≤ k ≤ 6 due to this approach seems to be useful for machine learning algorithms [ 37 ]. To this converted data set, scaling and dimension reduction techniques were applied using principal component analysis (PCA) with an explained variance of 96% (reduction of the initial number of features from 5460 to 2254).…”

Section: Methodsmentioning

confidence: 99%

Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning

Orozco-Arias

Candamil-Cortés

Jaimes

et al. 2022

Journal of Integrative Bioinformatics

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Transposable elements are mobile sequences that can move and insert themselves into chromosomes, activating under internal or external stimuli, giving the organism the ability to adapt to the environment. Annotating transposable elements in genomic data is currently considered a crucial task to understand key aspects of organisms such as phenotype variability, species evolution, and genome size, among others. Because of the way they replicate, LTR retrotransposons are the most common transposable elements in plants, accounting in some cases for up to 80% of all DNA information. To annotate these elements, a reference library is usually created, a curation process is performed, eliminating TE fragments and false positives and then annotated in the genome using the homology method. However, the curation process can take weeks, requires extensive manual work and the execution of multiple time-consuming bioinformatics software. Here, we propose a machine learning-based approach to perform this process automatically on plant genomes, obtaining up to 91.18% F1-score. This approach was tested with four plant species, obtaining up to 93.6% F1-score (Oryza granulata) in only 22.61 s, where bioinformatics methods took approximately 6 h. This acceleration demonstrates that the ML-based approach is efficient and could be used in massive sequencing projects.

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“…As a simple and effective feature extraction method, K-mer has been widely used in a variety of prediction models [43,44]. The feature extraction principle of K-mer is to count the number of occurrences of k consecutive nucleotides in the RNA sequence.…”

Section: K Monomeric Units (K-mer)mentioning

confidence: 99%

m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier

Liu

Lan

Liu

et al. 2023

IJMS

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As one of the most important post-transcriptional modifications, m6Am plays a fairly important role in conferring the m6Am stability and in the progression of cancers. The accurate identification of m6Am sites is critical for explaining its biological significance and developing its application in the medical field. However, conventional experimental approaches are time-consuming and expensive, making them unsuitable for the large-scale identification of the m6Am sites. To address this challenge, we exploit a CatBoost-based method, m6Aminer, to identify the m6Am sites on mRNA. For feature extraction, nine different feature-encoding schemes (pseudo electron–ion interaction potential, hash decimal conversion method, dinucleotide binary encoding, nucleotide chemical properties, pseudo k-tuple composition, dinucleotide numerical mapping, K monomeric units, series correlation pseudo trinucleotide composition, and K-spaced nucleotide pair frequency) were utilized to form the initial feature space. To obtain the optimized feature subset, the ExtraTreesClassifier algorithm was adopted to perform feature importance ranking, and the top 300 features were selected as the optimal feature subset. With different performance assessment methods, 10-fold cross-validation and independent test, m6Aminer achieved average AUC of 0.913 and 0.754, demonstrating a competitive performance with the state-of-the-art models m6AmPred (0.905 and 0.735) and DLm6Am (0.897 and 0.730). The prediction model developed in this study can be used to identify the m6Am sites in the whole transcriptome, laying a foundation for the functional research of m6Am.

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

Cited by 17 publications

References 66 publications

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

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

Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning

m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier

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