K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes
Autor: | Reinel Tabares-Soto, Mariana S. Candamil-Cortes, Gustavo Isaza, Romain Guyot, Johan S. Piña, Simon Orozco-Arias, Paula A. Jaimes |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Transposable element
Bioinformatics LTR retrotransposons Data Mining and Machine Learning Repetitive Sequences Retrotransposon Plant Science Biology Machine learning computer.software_genre General Biochemistry Genetics and Molecular Biology Computational Science 03 medical and health sciences 0302 clinical medicine k-mer based method 030304 developmental biology Sequence (medicine) Plant genomes 0303 health sciences Free-alignment approach business.industry General Neuroscience Data Science food and beverages General Medicine Classification k-mer Medicine Genomic information Artificial intelligence General Agricultural and Biological Sciences business Transposable elements computer 030217 neurology & neurosurgery |
Zdroj: | PeerJ PeerJ, Vol 9, p e11456 (2021) |
ISSN: | 2167-8359 |
Popis: | 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. |
Databáze: | OpenAIRE |
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