Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources
Open accessAbstract
Background: In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence. Results: We present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+ . In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly. Conclusion: Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.
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For six high-quality bat genomes, annotations integrating available transcriptomics data, ab initio gene predictions, aligned proteins from closely related bats, and comparative gene predictions contained 87.7% to 95.4% of the genes in the mammalian BUSCO odb10 set.
“For the six bats, these annotations contained 87.7% to 95.4% of the genes in the mammalian BUSCO odb10 set (Fig. 3C, Table S9).”
From Integrating gene annotation with orthology inference at scale (2023) · cites this paper as [24]
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Research works printed but not linked: 25
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- Authors
- Stanke, M., Schöffmann, O., Morgenstern, B., & Waack, S.
- Affiliations
- University of Göttingen (DE)
- Corresponding author
- Stanke, Mario
- Venue
- BMC Bioinformatics
- Published
- 2006-02-09
- Volume
- 7
- Issue
- 1
- Pages
- 62-62
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC1409804
- OpenAlex
- W1487554690
- Cited by
- 1,304
- References
- 0
- Citations by year
- 2026: 123 · 2025: 136 · 2024: 137 · 2023: 142 · 2022: 121 · 2021: 122 · 2020: 90 · 2019: 66 · 2018: 75 · 2017: 61 · 2016: 40 · 2015: 40 · 2014: 36 · 2013: 30 · 2012: 13
- FWCI
- 6.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
- GRO (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (01AK803G)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- gene prediction, generalized hidden Markov models, extrinsic evidence, Augustus, ab initio gene prediction, exon prediction, chromosome 22, stochastic modeling
- MeSH terms
- Algorithms, Animals, Artificial Intelligence, Base Sequence, Chromosome Mapping, Computer Simulation, Humans, Markov Chains, Models, Genetic, Molecular Sequence Data, Pattern Recognition, Automated, Stochastic Processes, Genetic Variation, Models, Statistical, Information Storage and Retrieval, Sequence Alignment, Sequence Analysis, DNA, Databases, Genetic
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