Evaluation of gene prediction software using a genomic data set: application to Arabidopsis thalianasequences

Abstract

Abstract Motivation: The annotation of the Arabidopsis thalianagenome remains a problem in terms of time and quality. To improve the annotation process, we want to choose the most appropriate tools to use inside a computer-assisted annotation platform. We therefore need evaluation of prediction programs with Arabidopsis sequences containing multiple genes. Results: We have developed AraSet, a data set of contigs of validated genes, enabling the evaluation of multi-gene models for the Arabidopsis genome. Besides conventional metrics to evaluate gene prediction at the site and the exon levels, new measures were introduced for the prediction at the protein sequence level as well as for the evaluation of gene models. This evaluation method is of general interest and could apply to any new gene prediction software and to any eukaryotic genome. The GeneMark.hmm program appears to be the most accurate software at all three levels for the Arabidopsis genomic sequences. Gene modeling could be further improved by combination of prediction software. Availability: The AraSet sequence set, the Perl programs and complementary results and notes are available at http://sphinx.rug.ac.be:8080/biocomp/napav/. Contact: Pierre.Rouze@gengenp.rug.ac.be

Keywords

AnnotationGene predictionArabidopsisPerlComputer scienceSoftwareGenomeComputational biologyGene AnnotationGenome projectSet (abstract data type)GenomicsArabidopsis thalianaGeneData miningGeneticsBiologyArtificial intelligenceProgramming language

Affiliated Institutions

Related Publications

An inventory of 1152 expressed sequence tags obtained by partial sequencing of cDNAs from <i>Arabidopsis thaliana</i><sup>†</sup>

Hermanus Höfte , Thierry T. Desprez , J. Anselem +22 more

As part of the goal to generate a detailed transcript map for Arabidopsis thaliana , 1152 single run sequences (expressed sequence tags or ESTs) have been determined from cDNA c...

1993 The Plant Journal 235 citations

GeneMark.hmm: new solutions for gene finding

Alex Lukashin

The number of completely sequenced bacterial genomes has been growing fast. There are computer methods available for finding genes but yet there is a need for more accurate algo...

1998 Nucleic Acids Research 1534 citations

eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale

Carlos P. Cantalapiedra , Ana Hernández-Plaza , Ivica Letunić +2 more

Abstract Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. He...

2021 Molecular Biology and Evolution 3673 citations

NetAffx: Affymetrix probesets and annotations

G. Liu

NetAffx (http://www.affymetrix.com) details and annotates probesets on Affymetrix GeneChip microarrays. These annotations include (i) static information specific to the probeset...

2003 Nucleic Acids Research 486 citations

GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions

J Besemer

Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence ...

2001 Nucleic Acids Research 2290 citations

Publication Info

Year: 1999
Type: article
Volume: 15
Issue: 11
Pages: 887-899
Citations: 107
Access: Closed

External Links

View on DOI.org

Social Impact

Altmetric

Evaluation of gene prediction software using a genomic data set: application to Arabidopsis thalianasequences

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

107

OpenAlex

Cite This

APA Style

                            
                                
                                    Nathalie Pavy, 
                                
                                    Stéphane Rombauts, 
                                
                                    Patrice Déhais
                                
                                et al.
                            
                            (1999). 
                            Evaluation of gene prediction software using a genomic data set: application to Arabidopsis thalianasequences. 
                            Bioinformatics
                            , 15
                            (11)
                            , 887-899.
                            https://doi.org/10.1093/bioinformatics/15.11.887
                        

Identifiers

DOI: 10.1093/bioinformatics/15.11.887