Literature DB >> 33525408

InpactorDB: A Classified Lineage-Level Plant LTR Retrotransposon Reference Library for Free-Alignment Methods Based on Machine Learning.

Simon Orozco-Arias1,2, Paula A Jaimes1, Mariana S Candamil1, Cristian Felipe Jiménez-Varón3, Reinel Tabares-Soto4, Gustavo Isaza2, Romain Guyot4,5.   

Abstract

Long terminal repeat (LTR) retrotransposons are mobile elements that constitute the major fraction of most plant genomes. The identification and annotation of these elements via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in genome size variation, LTR retrotransposons are also associated with the function and structure of different chromosomal regions and can alter the function of coding regions, among others. Several sequence databases of plant LTR retrotransposons are available for public access, such as PGSB and RepetDB, or restricted access such as Repbase. Although these databases are useful to identify LTR-RTs in new genomes by similarity, the elements of these databases are not fully classified to the lineage (also called family) level. Here, we present InpactorDB, a semi-curated dataset composed of 130,439 elements from 195 plant genomes (belonging to 108 plant species) classified to the lineage level. This dataset has been used to train two deep neural networks (i.e., one fully connected and one convolutional) for the rapid classification of these elements. In lineage-level classification approaches, we obtain up to 98% performance, indicated by the F1-score, precision and recall scores.

Entities:  

Keywords:  InpactorDB; LTR retrotransposons; bioinformatics; deep neural networks; genomics; machine learning; plant genomes

Mesh:

Substances:

Year:  2021        PMID: 33525408      PMCID: PMC7910972          DOI: 10.3390/genes12020190

Source DB:  PubMed          Journal:  Genes (Basel)        ISSN: 2073-4425            Impact factor:   4.096


  70 in total

1.  LTR_STRUC: a novel search and identification program for LTR retrotransposons.

Authors:  Eugene M McCarthy; John F McDonald
Journal:  Bioinformatics       Date:  2003-02-12       Impact factor: 6.937

2.  Basic local alignment search tool.

Authors:  S F Altschul; W Gish; W Miller; E W Myers; D J Lipman
Journal:  J Mol Biol       Date:  1990-10-05       Impact factor: 5.469

3.  Evolutionary dynamics of an ancient retrotransposon family provides insights into evolution of genome size in the genus Oryza.

Authors:  Jetty S S Ammiraju; Andrea Zuccolo; Yeisoo Yu; Xiang Song; Benoit Piegu; Frederic Chevalier; Jason G Walling; Jianxin Ma; Jayson Talag; Darshan S Brar; Phillip J SanMiguel; Ning Jiang; Scott A Jackson; Olivier Panaud; Rod A Wing
Journal:  Plant J       Date:  2007-08-30       Impact factor: 6.417

4.  The future of transposable element annotation and their classification in the light of functional genomics - what we can learn from the fables of Jean de la Fontaine?

Authors:  Peter Arensburger; Benoît Piégu; Yves Bigot
Journal:  Mob Genet Elements       Date:  2016-11-04

5.  LTR retrotransposons, handy hitchhikers of plant regulation and stress response.

Authors:  Marie-Angèle Grandbastien
Journal:  Biochim Biophys Acta       Date:  2014-07-30

6.  Hybrid Dysgenesis in DROSOPHILA MELANOGASTER: A Syndrome of Aberrant Traits Including Mutation, Sterility and Male Recombination.

Authors:  M G Kidwell; J F Kidwell; J A Sved
Journal:  Genetics       Date:  1977-08       Impact factor: 4.562

7.  Assessing genome assembly quality using the LTR Assembly Index (LAI).

Authors:  Shujun Ou; Jinfeng Chen; Ning Jiang
Journal:  Nucleic Acids Res       Date:  2018-11-30       Impact factor: 16.971

8.  A machine learning based framework to identify and classify long terminal repeat retrotransposons.

Authors:  Leander Schietgat; Celine Vens; Ricardo Cerri; Carlos N Fischer; Eduardo Costa; Jan Ramon; Claudia M A Carareto; Hendrik Blockeel
Journal:  PLoS Comput Biol       Date:  2018-04-23       Impact factor: 4.475

9.  LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons.

Authors:  David Ellinghaus; Stefan Kurtz; Ute Willhoeft
Journal:  BMC Bioinformatics       Date:  2008-01-14       Impact factor: 3.169

10.  LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons.

Authors:  Zhao Xu; Hao Wang
Journal:  Nucleic Acids Res       Date:  2007-05-07       Impact factor: 16.971

View more
  4 in total

1.  An Atlas of Plant Transposable Elements.

Authors:  Daniel Longhi Fernandes Pedro; Tharcisio Soares Amorim; Alessandro Varani; Romain Guyot; Douglas Silva Domingues; Alexandre Rossi Paschoal
Journal:  F1000Res       Date:  2021-11-24

2.  Impact of LTR-Retrotransposons on Genome Structure, Evolution, and Function in Curcurbitaceae Species.

Authors:  Shu-Fen Li; Hong-Bing She; Long-Long Yang; Li-Na Lan; Xin-Yu Zhang; Li-Ying Wang; Yu-Lan Zhang; Ning Li; Chuan-Liang Deng; Wei Qian; Wu-Jun Gao
Journal:  Int J Mol Sci       Date:  2022-09-05       Impact factor: 6.208

3.  Parasitic plant small RNA analyses unveil parasite-specific signatures of microRNA retention, loss, and gain.

Authors:  Zahra Zangishei; Maria Luz Annacondia; Heidrun Gundlach; Alena Didriksen; Julien Bruckmüller; Hooman Salari; Kirsten Krause; German Martinez
Journal:  Plant Physiol       Date:  2022-09-28       Impact factor: 8.005

4.  K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes.

Authors:  Simon Orozco-Arias; Mariana S Candamil-Cortés; Paula A Jaimes; Johan S Piña; Reinel Tabares-Soto; Romain Guyot; Gustavo Isaza
Journal:  PeerJ       Date:  2021-05-19       Impact factor: 2.984

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.