ArticleBMC bioinformatics2006
Automatic generation of gene finders for eukaryotic species.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A la carte bioprospecting of substrate-selective laccases via high-throughput computational enzyme-substrate interaction profiling.Protein science : a publication of the Protein Society · 2026Article
- Tokenization and deep learning architectures in genomics: A comprehensive review.Computational and structural biotechnology journal · 2025Review
- Optimized splitting of mixed-species RNA sequencing data.Journal of bioinformatics and computational biology · 2022Article
- Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models.PloS one · 2018Article
- A review of three different studies on hidden markov models for epigenetic problems: a computational perspective.Genomics & informatics · 2014Review
- Spliceosomal introns in the 5' untranslated region of plant BTL RING-H2 ubiquitin ligases are evolutionary conserved and required for gene expression.BMC plant biology · 2013Article
- Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.Bioinformatics (Oxford, England) · 2012Article
- Hidden Markov Models and their Applications in Biological Sequence Analysis.Current genomics · 2009Article
- nGASP--the nematode genome annotation assessment project.BMC bioinformatics · 2008Article
- SCGPred: a score-based method for gene structure prediction by combining multiple sources of evidence.Genomics, proteomics & bioinformatics · 2008Article
- Generic eukaryotic core promoter prediction using structural features of DNA.Genome research · 2008Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.