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Functional Characterization of Clinical Isolates of the Opportunistic Fungal Pathogen Aspergillus nidulans.

Author(s): Bastos RW, Valero C, Silva LP, Schoen T, Drott M, Brauer V, Silva-Rocha R, Lind A, Steenwyk JL, Rokas A, Rodrigues F, Resendiz-Sharpe A, Lag...

mSphere. 2020 Apr 08;5(2): Authors: Bastos RW, Valero C, Silva LP, Schoen T, Drott M, Brauer V, Silva-Rocha R, Lind A, Steenwyk JL, Rokas A, Rodrigues F, Resendiz-Sharpe A, Lagrou K, Marcet-Houben...

Article GUID: 32269156
SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models.

Author(s): Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

BMC Bioinformatics. 2014 Jul 01;15:229 Authors: Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

Article GUID: 24980894
Identification of Genes Involved in the Degradation of Lignocellulose Using Comparative Transcriptomics.

Author(s): Gruninger RJ, Reid I, Forster RJ, Tsang A, McAllister TA

Methods Mol Biol. 2017;1588:279-298 Authors: Gruninger RJ, Reid I, Forster RJ, Tsang A, McAllister TA

Article GUID: 28417376
Evaluating Programs for Predicting Genes and Transcripts with RNA-Seq Support in Fungal Genomes.

Author(s): Reid I

Methods Mol Biol. 2018;1775:209-227 Authors: Reid I

Article GUID: 29876820
Title:SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models.
Authors:Reid I,  O'Toole N,  Zabaneh O,  Nourzadeh R,  Dahdouli M,  Abdellateef M,  Gordon PM,  Soh J,  Butler G,  Sensen CW,  Tsang A
Link:https://www.ncbi.nlm.nih.gov/pubmed/24980894?dopt=Abstract
DOI:10.1186/1471-2105-15-229
Category:BMC Bioinformatics
PMID:24980894
Dept Affiliation: GENOMICS
1 Centre for Structural and Functional Genomics, Concordia University, 7141 Sherbrooke St, W, Montreal, QC H4B 1R6, Canada. ian.reid@concordia.ca.

Description:

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

BMC Bioinformatics. 2014 Jul 01;15:229

Authors: Reid I, O'Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, Gordon PM, Soh J, Butler G, Sensen CW, Tsang A

Abstract

BACKGROUND: Locating 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.

RESULTS: SnowyOwl 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.

CONCLUSIONS: SnowyOwl 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/.

PMID: 24980894 [PubMed - indexed for MEDLINE]