Résumé
Here, we present InpactorDB a semi-curated dataset composed of 130,511 elements from 195 plant genomes belonging to 108 plant species, classified down to the lineage level. This dataset has been used to train two deep neural networks (one fully connected and one convolutional) for fast classification of elements. Used in lineage-level classification approaches, we obtain a score above 98% of F1-score, precision and recall. In order to classify elements of the ‘LTR_STRUC’ and ‘EDTA’ datasets, we used the methodology proposed by Inpactor, which uses homology-based strategy with known coding domains belonging to LTR-RTs. We utilized the RexDB domain library as reference. LTR-RTs were classified into superfamilies, Gypsy (RLG) or Copia (RLC) and sub-classified into lineages according to the similarities of five different amino acid reference domains (GAG, AP, RT, RNAseH, and INT domains). In addition, we applied filters to keep only intact elements: to remove predicted elements with domains from two different superfamilies (i.e. Gypsy and Copia), or elements with domains belonging to two or more different lineages, to remove elements with lengths different than those reported by Gypsy Database (GyDB) with a tolerance of 20%, to delete incomplete elements which has less than three identified domains, and to remove elements with insertions of TE class II (reported in Repbase). The final non-redundant version of InpactorDB consists of 67,305 LTR retrotransposons. Both redundant and non-redundant versions of InpactorDB are available in Fasta format in which sequences have identifiers with the following general Identification code: >Superfamily-Lineage-plant_family-specie-source-length-ID where Superfamily can is either RLC (for Copia) or RLG (for Gypsy), Lineage/family following the RexDB nomenclature, source (can be Repbase, RepetDB, PGSB, LTR_STRUC or EDTA datasets), length, and ID, is a unique number which identify each element inside the InpactorDB.