The DOI matches its Crossref record; 135 of the 135 references it cites were found in a registry or an index.
Current methods of gene prediction, their strengths and weaknesses
Open accessAbstract
While the genomes of many organisms have been sequenced over the last few years, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed that try to address one part of this problem, which consists of locating the genes along a genome. This paper reviews the existing approaches to predicting genes in eukaryotic genomes and underlines their intrinsic advantages and limitations. The main mathematical models and computational algorithms adopted are also briefly described and the resulting software classified according to both the method and the type of evidence used. Finally, the several difficulties and pitfalls encountered by the programs are detailed, showing that improvements are needed and that new directions must be considered.
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Cites
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Cited by
Gene prediction with a hidden Markov model and a new intron submodel
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Counting the Zinc-Proteins Encoded in the Human Genome
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Machine learning in bioinformatics
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Metadata
- Authors
- Mathe, C.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-10-01
- Volume
- 30
- Issue
- 19
- Pages
- 4103-4117
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2159833474
- Cited by
- 476
- References
- 135
- FWCI
- 13.18
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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