SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

Abstract

BackgroundLocating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them.ResultsSnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data.ConclusionsSnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/.

Sequence Assembly of Yarrowia lipolytica Strain W29/CLIB89 Shows Transposable Element Diversity

“…lipolytica database (http://www.ncbi.nlm.nih.gov/); 2) Yeast Genome Annotation Pipeline (YGAP) [21]; and 3) SnowyOwl fungal genome analysis [22] (Materials and Methods, Fig 2). The results of this analysis are summarized in Fig 3 and Table 4.…”

A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms

“…The ab initio gene prediction programs included in the benchmark study are based on statistical models that are trained using known proteins and genes, and typically perform well at predicting conserved or well-studied genes [33,38]. However, ab initio prediction accuracy has been previously shown to decrease in some special cases, such as small proteins [39], organism-specific genes or other unusual genes [40][41][42]. Our goal was therefore to identify the strengths and weaknesses of the programs, but also to highlight genomic and protein characteristics that could be incorporated to improve the prediction models.…”

Current methods for automated annotation of protein-coding genes

“…The sister tool GeneMark-ES requires only the genome itself for training [13] but may be less accurate. SnowyOwl [14] is a training and annotation pipeline that was tested on fungi and that combines initial transcript models from assembled RNA-Seq data and GeneMark-ES to train AUGUSTUS, subsequently, genes are predicted with AUGUSTUS and RNA-Seq hints and the resulting gene models are combined with GeneMark-ES predictions.…”