The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
Computational methods for the identification of genes in vertebrate genomic sequences
Abstract
Research into new methods to identify genes in anonymous genomic sequences has been going on for more than 15 years. Over this period of time, the field has evolved from the designing of programs to identify protein coding regions in compact mitochondrial or bacterial genomes, to the challenge of predicting the detailed organization of multi-exon vertebrate genes. The best program currently available perfectly locates more than 80% of the internal coding exons, and only 5% of the predictions do not overlap a real exon. Given such accuracy, computational methods are indeed very useful; however, they do not alleviate the need for experimental validation. If the performances are satisfactory for the identification of the coding moiety of genes (internal coding exons), the determination of the full extent of the transcript (5' and 3' extremities of the gene) and the location of promoter regions are still unreliable. As the human and mouse genome sequencing projects enter a production mode, the fully automated annotation of megabase-long anonymous genomic sequences is the next big challenge in bioinformatics.
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Cites
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Proteomics to study genes and genomes
Pandey, A., & Mann, M. (2000) · Nature · doi:10.1038/35015709 · Cited by 2,373
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Perspectives for mass spectrometry and functional proteomics
Godovac-Zimmermann, J., & Brown, L. R. (2001) · Mass Spectrometry Reviews · doi:10.1002/1098-2787(2001)20:1<1::aid-mas1001>3.0.co;2-j · Cited by 264
Metadata
- Authors
- Claverie, J.
- Venue
- Human Molecular Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 1997-09-01
- Volume
- 6
- Issue
- 10
- Pages
- 1735-1744
- ISSN
- 0964-6906 (ISSN-L)
- 1460-2083
- OpenAlex
- W2153632714
- Cited by
- 264
- References
- 72
- FWCI
- 7.42
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
