Abstract
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | BMC Bioinformatics |
| Vol/bind | 7 |
| Udgave nummer | 263 |
| ISSN | 1471-2105 |
| DOI | |
| Status | Udgivet - 2006 |
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I: BMC Bioinformatics, Bind 7, Nr. 263, 2006.
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
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TY - JOUR
T1 - Automatic generation of gene finders for eukaryotic species
AU - Terkelsen, Kasper Munch
AU - Krogh, A.
PY - 2006
Y1 - 2006
N2 - BackgroundThe number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. This calls for procedures to automatically generate species-specific gene finders and to re-train them as the quantity and quality of reliable gene annotation grows.ResultsWe present a procedure, Agene, that automatically generates a species-specific gene predictor from a set of reliable mRNA sequences and a genome. We apply a Hidden Markov model (HMM) that implements explicit length distribution modelling for all gene structure blocks using acyclic discrete phase type distributions. The state structure of the each HMM is generated dynamically from an array of sub-models to include only gene features represented in the training set.ConclusionAcyclic discrete phase type distributions are well suited to model sequence length distributions. The performance of each individual gene predictor on each individual genome is comparable to the best of the manually optimised species-specific gene finders. It is shown that species-specific gene finders are superior to gene finders trained on other species.
AB - BackgroundThe number of sequenced eukaryotic genomes is rapidly increasing. This means that over time it will be hard to keep supplying customised gene finders for each genome. This calls for procedures to automatically generate species-specific gene finders and to re-train them as the quantity and quality of reliable gene annotation grows.ResultsWe present a procedure, Agene, that automatically generates a species-specific gene predictor from a set of reliable mRNA sequences and a genome. We apply a Hidden Markov model (HMM) that implements explicit length distribution modelling for all gene structure blocks using acyclic discrete phase type distributions. The state structure of the each HMM is generated dynamically from an array of sub-models to include only gene features represented in the training set.ConclusionAcyclic discrete phase type distributions are well suited to model sequence length distributions. The performance of each individual gene predictor on each individual genome is comparable to the best of the manually optimised species-specific gene finders. It is shown that species-specific gene finders are superior to gene finders trained on other species.
U2 - 10.1186/1471-2105-7-263
DO - 10.1186/1471-2105-7-263
M3 - Journal article
C2 - 16712739
SN - 1471-2105
VL - 7
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - 263
ER -