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

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 predicti...

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Bibliographic Details
Published in:BMC bioinformatics 2018-05, Vol.19 (1), p.189-189, Article 189
Main Authors: Keilwagen, Jens, Hartung, Frank, Paulini, Michael, Twardziok, Sven O, Grau, Jan
Format: Article
Language:English
Subjects:
Amino acid sequence
Animals
Annotations
Barley
Bioinformatics
Conservation
Conserved sequence
Consortia
Fungi
Gene expression
Gene Expression Profiling
Gene sequencing
Genes
Genes, Fungal
Genes, Plant
Genome annotation
Genomes
Genomics
Homology
Homology (Biology)
Homology-based gene prediction
Hordeum - genetics
Insects
Introns
Methodology
Molecular Sequence Annotation
Nematoda - genetics
Nucleotide sequence
Performance enhancement
Phylogenetics
Proteins
Ribonucleic acid
RNA
RNA sequencing
RNA-seq
Sequence Analysis, RNA
Sequence Homology, Amino Acid
Software
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Summary: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. 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. 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 .
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2203-5