swMATH ID: 23030
Software Authors: Schweikert G, Zien A, Zeller G, Behr J, Dieterich C, Ong CS, Philips P, De Bona F, Hartmann L, Bohlen A
Description: mGene: accurate SVM-based gene finding with an application to nematode genomes. We present a highly accurate gene-prediction system for eukaryotic genomes, called mGene. It combines in an unprecedented manner the flexibility of generalized hidden Markov models (gHMMs) with the predictive power of modern machine learning methods, such as Support Vector Machines (SVMs). Its excellent performance was proved in an objective competition based on the genome of the nematode Caenorhabditis elegans. Considering the average of sensitivity and specificity, the developmental version of mGene exhibited the best prediction performance on nucleotide, exon, and transcript level for ab initio and multiple-genome gene-prediction tasks. The fully developed version shows superior performance in 10 out of 12 evaluation criteria compared with the other participating gene finders, including Fgenesh++ and Augustus. An in-depth analysis of mGene’s genome-wide predictions revealed that approximately 2200 predicted genes were not contained in the current genome annotation. Testing a subset of 57 of these genes by RT-PCR and sequencing, we confirmed expression for 24 (42
Homepage: https://www.ncbi.nlm.nih.gov/pubmed/19564452
Related Software: LIBSVM; GroupSortFuse; PRALINE; MAKER; ZCURVE; UniProt; BEDTools; FIMO; GeneMarkS; RDP2; PhyloBayes 3; Partitionfinder; CRITICA; TICO; Ensembl REST; MOCAT; BioStar; ClustalW; SeqVis; GOLD
Cited in: 4 Documents

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