PuSH - Publikationsserver des Helmholtz Zentrums München

Keilwagen, J.* ; Hartung, F.* ; Paulini, M.* ; Twardziok, S.O. ; Grau, J.*

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi.

BMC Bioinformatics 19:189 (2018)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Background: Genome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction. Results: Here, we present an extension of the gene prediction program GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions. Conclusions: GeMoMa might be of great utility for annotating newly sequenced genomes but also for finding homologs of a specific gene or gene family. GeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Genome Annotation ; Homology-based Gene Prediction ; Rna-seq
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Zeitschrift BMC Bioinformatics
Quellenangaben Band: 19, Heft: 1, Seiten: , Artikelnummer: 189 Supplement: ,
Verlag BioMed Central
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Research Unit Plant Genome and Systems Biology (PGSB)