GlimmerM, Exonomy and Unveil: Three ab initio eukaryotic genefinders

William H. Majoros; Mihaela Pertea; Corina Antonescu; Steven L. Salzberg

doi:10.1093/nar/gkg527

GlimmerM, Exonomy and Unveil: Three ab initio eukaryotic genefinders

William H. Majoros, Mihaela Pertea, Corina Antonescu, Steven L. Salzberg

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Abstract

We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and GlimmerM. Exonomy is a 23-state Generalized Hidden Markov Model (GHMM), Unveil is a 283-state standard Hidden Markov Model (HMM) and GlimmerM is a previously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs). All three are readily re-trainable for new organisms and have been found to perform well compared to other genefinders. Results are presented for Arabidopsis thaliana. Cases have been found where each of the genefinders outperforms each of the others, demonstrating the collective value of this ensemble of genefinders. These programs are all accessible through webservers at http://www.tigr.org/software.

Original language	English (US)
Pages (from-to)	3601-3604
Number of pages	4
Journal	Nucleic acids research
Volume	31
Issue number	13
DOIs	https://doi.org/10.1093/nar/gkg527
State	Published - Jul 1 2003
Externally published	Yes

ASJC Scopus subject areas

Genetics

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title = "GlimmerM, Exonomy and Unveil: Three ab initio eukaryotic genefinders",

abstract = "We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and GlimmerM. Exonomy is a 23-state Generalized Hidden Markov Model (GHMM), Unveil is a 283-state standard Hidden Markov Model (HMM) and GlimmerM is a previously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs). All three are readily re-trainable for new organisms and have been found to perform well compared to other genefinders. Results are presented for Arabidopsis thaliana. Cases have been found where each of the genefinders outperforms each of the others, demonstrating the collective value of this ensemble of genefinders. These programs are all accessible through webservers at http://www.tigr.org/software.",

author = "Majoros, {William H.} and Mihaela Pertea and Corina Antonescu and Salzberg, {Steven L.}",

note = "Funding Information: This work was supported in part by NSF grants MCB-0114792 and KDI-9980088, and by NIH grant R01-LM06845. The authors thank Jennifer Wortman, Mark Yandell and Art Delcher for helpful discussions and the anonymous reviewers for useful comments.",

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doi = "10.1093/nar/gkg527",

language = "English (US)",

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T2 - Three ab initio eukaryotic genefinders

AU - Majoros, William H.

AU - Pertea, Mihaela

AU - Antonescu, Corina

AU - Salzberg, Steven L.

N1 - Funding Information: This work was supported in part by NSF grants MCB-0114792 and KDI-9980088, and by NIH grant R01-LM06845. The authors thank Jennifer Wortman, Mark Yandell and Art Delcher for helpful discussions and the anonymous reviewers for useful comments.

PY - 2003/7/1

Y1 - 2003/7/1

N2 - We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and GlimmerM. Exonomy is a 23-state Generalized Hidden Markov Model (GHMM), Unveil is a 283-state standard Hidden Markov Model (HMM) and GlimmerM is a previously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs). All three are readily re-trainable for new organisms and have been found to perform well compared to other genefinders. Results are presented for Arabidopsis thaliana. Cases have been found where each of the genefinders outperforms each of the others, demonstrating the collective value of this ensemble of genefinders. These programs are all accessible through webservers at http://www.tigr.org/software.

AB - We present three programs for ab initio gene prediction in eukaryotes: Exonomy, Unveil and GlimmerM. Exonomy is a 23-state Generalized Hidden Markov Model (GHMM), Unveil is a 283-state standard Hidden Markov Model (HMM) and GlimmerM is a previously-described genefinder which utilizes decision trees and Interpolated Markov Models (IMMs). All three are readily re-trainable for new organisms and have been found to perform well compared to other genefinders. Results are presented for Arabidopsis thaliana. Cases have been found where each of the genefinders outperforms each of the others, demonstrating the collective value of this ensemble of genefinders. These programs are all accessible through webservers at http://www.tigr.org/software.

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GlimmerM, Exonomy and Unveil: Three ab initio eukaryotic genefinders

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