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

ABSTRACT:

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 pi

SUBMITTER: Keilwagen J 

PROVIDER: S-EPMC5975413 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature