There is a newer version of this record available.

Dataset Open Access

InpactorDB: A Plant classified lineage-level LTR retrotransposon reference library for free-alignment methods based on Machine Learning

Orozco-Arias Simon; Jaimes Paula A.; Candamil Mariana; Jiménez-Varón Cristian Felipe; Tabares-Soto Reinel; Isaza Gustavo; Guyot Romain

DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="" xmlns="" xsi:schemaLocation="">
  <identifier identifierType="DOI">10.5281/zenodo.4386317</identifier>
      <creatorName>Orozco-Arias Simon</creatorName>
      <affiliation>Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Colombia</affiliation>
      <creatorName>Jaimes Paula A.</creatorName>
      <affiliation>Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Colombia</affiliation>
      <creatorName>Candamil Mariana</creatorName>
      <affiliation>Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Colombia</affiliation>
      <creatorName>Jiménez-Varón Cristian Felipe</creatorName>
      <affiliation>Department of Physics and Mathematics, Universidad Autónoma de Manizales, Manizales 170001, Colombia</affiliation>
      <creatorName>Tabares-Soto  Reinel</creatorName>
      <affiliation>Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Colombia</affiliation>
      <creatorName>Isaza Gustavo</creatorName>
      <affiliation>Department of Systems and Informatics, Universidad de Caldas, Manizales, Colombia</affiliation>
      <creatorName>Guyot Romain</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-7016-7485</nameIdentifier>
    <title>InpactorDB: A Plant classified lineage-level LTR retrotransposon reference library for free-alignment methods based on Machine Learning</title>
    <subject>LTR retrotransposons, machine learning, deep neural networks, bioinformatics, plant genomes, genomics, InpactorDB</subject>
    <date dateType="Issued">2020-12-22</date>
  <resourceType resourceTypeGeneral="Dataset"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4386316</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;LTR retrotransposons are mobile elements that make up the major part of most plant genomes. Their identification and annotation via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in the variation in genome size, these elements are also associated in the function and structure of different chromosomal regions and in the alteration of the function of coding regions, among others. Several plant retrotransposon sequence databases of LTR retrotransposons are available with public access such as PGSB, RepetDB or restricted access such as Repbase. Although they are useful for approaches to identify LTR-RTs in new genomes by similarity, the elements of these databases are not classified down to the lineage/family level. with great depth.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;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 data set 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.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;In order to classify elements of the &amp;lsquo;LTR_STRUC&amp;rsquo; and &amp;lsquo;EDTA&amp;rsquo; 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 &amp;nbsp;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 remove keep only intact elements:&lt;/p&gt;

&lt;p&gt;1) to remove predicted elements with domains from two different superfamilies (i.e. Gypsy and Copia),&lt;/p&gt;

&lt;p&gt;2) or elements with domains belonging to two or more different lineages,&lt;/p&gt;

&lt;p&gt;3) to remove elements with lengths different than those reported by Gypsy Database (GyDB) with a tolerance of 20%,&lt;/p&gt;

&lt;p&gt;4) to delete incomplete elements which has less than three identified domains, and&lt;/p&gt;

&lt;p&gt;5) to remove elements with insertions of TE class II (reported in Repbase).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;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&amp;nbsp;format in which sequences have identifiers with the following general&amp;nbsp;Identification code:&lt;/p&gt;


&lt;p&gt;Where Superfamily&amp;nbsp;can&amp;nbsp;is either RLC (for Copia) or RLG (for&amp;nbsp;Gypsy), Lineage/family&amp;nbsp;follows&amp;nbsp;following&amp;nbsp;the RexDB nomenclature, source&amp;nbsp;(can be&amp;nbsp;Repbase, RepetDB, PGSB, LTR_STRUC or EDTA&amp;nbsp;datasets), length, and ID,&amp;nbsp;is&amp;nbsp;a unique number which identify each element inside&amp;nbsp;the&amp;nbsp;InpactorDB.&lt;/p&gt;</description>
All versions This version
Views 544260
Downloads 13434
Data volume 29.8 GB7.1 GB
Unique views 438239
Unique downloads 8422


Cite as