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Wild coffea species db.

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

List of publications 

Orozco-Arias S, Lopez-Murillo LH, Piña JS, Valencia-Castrillon E, Tabares-Soto R, Castillo-Ossa L, et al. (2023) Genomic object detection: An improved approach for transposable elements detection and classification using convolutional neural networks. PLoS ONE 18(9): e0291925.

Orozco-Arias, S., Gaviria-Orrego, S., Tabares-Soto, R., Isaza, G., Guyot, R. (2023). InpactorDB: A Plant LTR Retrotransposon Reference Library. In: Garcia, S., Nualart, N. (eds) Plant Genomic and Cytogenetic Databases. Methods in Molecular Biology, vol 2703. Humana, New York, NY. 

Gonzalez-García, L. N.,  Lozano-Arce, D.,  Londoño, J. P.,  Guyot, R., and  Duitama, J..  2023.  Efficient homology-based annotation of transposable elements using minimizers. Applications in Plant Sciences11(4): e11520.

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,

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.

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.

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. 

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

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.

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;

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.