Literature DB >> 24124688

Gene identification programs in bread wheat: a comparison study.

Jaber Nasiri1, Mohammadreza Naghavi, Sara Naseri Rad, Tahereh Yolmeh, Milaveh Shirazi, Ramin Naderi, Mojtaba Nasiri, Sayvan Ahmadi.   

Abstract

Seven ab initio web-based gene prediction programs (i.e., AUGUSTUS, BGF, Fgenesh, Fgenesh+, GeneID, Genemark.hmm, and HMMgene) were assessed to compare their prediction accuracy using protein-coding sequences of bread wheat. At both nucleotide and exon levels, Fgenesh+ was deduced as the superior program and BGF followed by Fgenesh were resided in the next positions, respectively. Conversely, at gene level, Fgenesh with the value of predicting more than 75% of all the genes precisely, concluded as the best ones. It was also found out that programs such as Fgenesh+, BGF, and Fgenesh, because of harboring the highest percentage of correct predictive exons appear to be much more applicable in achieving more trustworthy results, while using both GeneID and HMMgene the percentage of false negatives would be expected to enhance. Regarding initial exon, overall, the frequency of accurate recognition of 3' boundary was significantly higher than that of 5' and the reverse was true if terminal exon is taken into account. Lastly, HMMgene and Genemark.hmm, overall, presented independent tendency against GC content, while the others appear to be slightly more sensitive if GC-poor sequences are employed. Our results, overall, exhibited that to make adequate opportunity in acquiring remarkable results, gene finders still need additional improvements.

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Year:  2013        PMID: 24124688     DOI: 10.1080/15257770.2013.832773

Source DB:  PubMed          Journal:  Nucleosides Nucleotides Nucleic Acids        ISSN: 1525-7770            Impact factor:   1.381


  5 in total

1.  Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.

Authors:  Stepan Pachganov; Khalimat Murtazalieva; Alexei Zarubin; Tatiana Taran; Duane Chartier; Tatiana V Tatarinova
Journal:  Methods Mol Biol       Date:  2021

2.  Comparative genomics of chemosensory protein genes (CSPs) in twenty-two mosquito species (Diptera: Culicidae): Identification, characterization, and evolution.

Authors:  Ting Mei; Wen-Bo Fu; Bo Li; Zheng-Bo He; Bin Chen
Journal:  PLoS One       Date:  2018-01-05       Impact factor: 3.240

3.  OMGene: mutual improvement of gene models through optimisation of evolutionary conservation.

Authors:  Michael P Dunne; Steven Kelly
Journal:  BMC Genomics       Date:  2018-04-27       Impact factor: 3.969

4.  TransPrise: a novel machine learning approach for eukaryotic promoter prediction.

Authors:  Stepan Pachganov; Khalimat Murtazalieva; Aleksei Zarubin; Dmitry Sokolov; Duane R Chartier; Tatiana V Tatarinova
Journal:  PeerJ       Date:  2019-11-01       Impact factor: 2.984

5.  Double triage to identify poorly annotated genes in maize: The missing link in community curation.

Authors:  Marcela K Tello-Ruiz; Cristina F Marco; Fei-Man Hsu; Rajdeep S Khangura; Pengfei Qiao; Sirjan Sapkota; Michelle C Stitzer; Rachael Wasikowski; Hao Wu; Junpeng Zhan; Kapeel Chougule; Lindsay C Barone; Cornel Ghiban; Demitri Muna; Andrew C Olson; Liya Wang; Doreen Ware; David A Micklos
Journal:  PLoS One       Date:  2019-10-28       Impact factor: 3.240

  5 in total

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