Using intron position conservation for homology-based gene prediction
By Jens Keilwagen, Michael Wenk, J. Erickson, M. Schattat, Jan Grau, F. Hartung. Nucleic Acids Research, 2016
654 citations recorded by the source corpus.
Annotation of protein-coding genes is very important in bioinformatics and biology and has a decisive influence on many downstream analyses. Homology-based gene prediction programs allow for transferring knowledge about protein-coding genes from an annotated organism to an organism of interest. Here, we present a homology-based gene prediction program called GeMoMa. GeMoMa utilizes the conservation of intron positions within genes to predict related genes in other organisms. We assess the performance of GeMoMa and compare it with state-of-the-art competitors on plant and animal genomes using an extended best reciprocal hit approach. We find that GeMoMa often makes more precise predictions than its competitors yielding a substantially increased number of correct transcripts. Subsequently, we exemplarily validate GeMoMa predictions using Sanger sequencing. Finally, we use RNA-seq data to compare the predictions of homology-based gene prediction programs, and find again that GeMoMa performs well. Hence, we conclude that exploiting intron position conservation improves homology-based gene prediction, and we make GeMoMa freely available as command-line tool and Galaxy integration.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/1197e7e5918813b5e6da629cfc63db0b4966d745
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In Molecular Biology
- Mark Borodovsky
- Tomás Bruna
- Alexandre Lomsadze
- Jan Grau
- F. Hartung
- Jens Keilwagen
- Sven Twardziok
- Michael Paulini
Citations in the corpus
- BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database (2020, neutral): “In addition, GeMoMa requires gene coordinates in reference genomes.”
- Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi (2017, methodology): “Despite no or only partial RNA-seq support, we find that 833 are partially supported by best BLAT matches of ESTs to the genome.”
- GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins (2020, positive): “Information about intron position, conserved in protein primary structures of multiple homologs, was used in another tool, GeMoMa (11).”