Developement of new tools to identify and annotate Transposable elements in plant genomes.

List of publications

Artificial Intelligence applied to Transposable element's annotation

Simon Orozco-Arias, Luis Humberto Lopez-Murillo, Mariana S Candamil-Cortés, Maradey Arias, Paula A Jaimes, Alexandre Rossi Paschoal, Reinel Tabares-Soto, Gustavo Isaza, Romain Guyot, Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes, Briefings in Bioinformatics, 2022;, bbac511, https://doi.org/10.1093/bib/bbac511

Orozco-Arias, Simon, Candamil-Cortes, Mariana S., Jaimes, Paula A., Valencia-Castrillon, Estiven, Tabares-Soto, Reinel, Isaza, Gustavo and Guyot, Romain. "Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning" Journal of Integrative Bioinformatics, vol. 19, no. 3, 2022, pp. 20210036. https://doi.org/10.1515/jib-2021-0036

S. Orozco-Arias et al., "SENMAP: A Convolutional Neural Network Architecture for Curation of LTR-RT Libraries from Plant Genomes," 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), 2021, pp. 1-4, doi: 10.1109/CI-IBBI54220.2021.9626130. https://ieeexplore.ieee.org/abstract/document/9626130

Deep Neural Network to Curate LTR Retrotransposon Libraries from Plant Genomes Orozco-Arias S. et al. (2022). In: Rocha M., Fdez-Riverola F., Mohamad M.S., Casado-Vara R. (eds) Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021). PACBB 2021. Lecture Notes in Networks and Systems, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-86258-9_9

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes Orozco-Arias S, Candamil-Cortés MS, Jaimes PA, Piña JS, Tabares-Soto R, Guyot R, Isaza G. 2021.. PeerJ 9:e11456 https://doi.org/10.7717/peerj.11456

InpactorDB: A Classified Lineage-Level Plant LTR Retrotransposon Reference Library for Free-Alignment Methods Based on Machine Learning. Orozco-Arias, S.; Jaimes, P.A.; Candamil, M.S.; Jiménez-Varón, C.F.; Tabares-Soto, R.; Isaza, G.; Guyot, R. Genes 2021, 12, 190. https://doi.org/10.3390/genes12020190

Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements. Processes 2020, 8, 638; doi:10.3390/pr8060638

A systematic review of the application of machine learning in the detection and classification of transposable elements. 2019 PeerJ 7:e8311 DOI 10.7717/peerj.8311

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning. Int. J. Mol. Sci. 2019, 20, 3837; doi:10.3390/ijms20153837

Development of tools applied to transposable element insertion polymorphisms

TIP_finder: An HPC Software to Detect Transposable Element Insertion Polymorphisms in Large Genomic Datasets. Biology 2020, 9(9), 281; https://doi.org/10.3390/biology9090281

Development of tools applied to LTR Retrotransposons classification

Orozco-Arias S, Liu J, Tabares-Soto R, Ceballos D, Silva Domingues D, Garavito A, Ming R, Guyot R. Inpactor, Integrated and Parallel Analyzer and Classifier of LTR Retrotransposons and Its Application for Pineapple LTR Retrotransposons Diversity and Dynamics. Biology (Basel). 2018 May 25;7(2):32. doi: 10.3390/biology7020032.