CombTEs: combining predictions from the search for transposable elements
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Data
2022-01-01
Autores
Fischer, Carlos Norberto [UNESP]
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Resumo
Several tools, using different approaches, are available nowadays to identify transposable elements (TEs) in a query sequence. Normally, a same set of TEs can be predicted by many of these tools. However, for other TEs, only a few tools are able to predict them due to their particular characteristics. In both cases, combining predictions produced by two or more tools can be an interesting approach to increasing the number of correct results and, at the same time, to further improve the confidence about the predicted TEs. Taking this into account, this work presents an auxiliary tool, CombTEs, that combines predictions produced by other programs and pipelines used to identify TEs in a genome sequence. The basic idea is that, after running only once the tools of interest, the same sets of initial predictions are used in several combining processes, each one considering different values for the parameters used by CombTEs (for example, filters and distance between predictions), in a very fast way, making the annotation step easier and more reliable.
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bioinformatics tools, combining predictions, LTR retrotransposons, pHMMs, prediction combination, profile hidden Markov models, similarity method, transposable element classification, transposable element searches, transposable elements
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International Journal of Bioinformatics Research and Applications, v. 18, n. 5, p. 496-504, 2022.