ArticleProceedings of the National Academy of Sciences of the United States of America1996
Gene recognition via spliced sequence alignment.
Article in Proceedings of the National Academy of Sciences of the United States of America, 1996. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed, 309 citations in OpenAlex.
- Article
- An overview of computational methods for gene prediction in eukaryotes: strengths, limitations, and future directions.Bioinformatics advances · 2025Review
- ORFograph: search for novel insecticidal protein genes in genomic and metagenomic assembly graphs.Microbiome · 2021Article
- BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database.NAR genomics and bioinformatics · 2021Article
- Cooperation of Spaln and Prrn5 for Construction of Gene-Structure-Aware Multiple Sequence Alignment.Methods in molecular biology (Clifton, N.J.) · 2021Article
- MetaEuk-sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics.Microbiome · 2020Article
- Whole-Genome Alignment and Comparative Annotation.Annual review of animal biosciences · 2019Review
- An optimized approach for annotation of large eukaryotic genomic sequences using genetic algorithm.BMC bioinformatics · 2017Article
- A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra.Bioinformatics (Oxford, England) · 2017Article
- Physico-chemical fingerprinting of RNA genes.Nucleic acids research · 2017Article
- A fragmented alignment method detects a putative phosphorylation site and a putative BRC repeat in the Drosophila melanogaster BRCA2 protein.F1000Research · 2013Article
- Benchmarking spliced alignment programs including Spaln2, an extended version of Spaln that incorporates additional species-specific features.Nucleic acids research · 2012Article
- Stitching gene fragments with a network matching algorithm improves gene assembly for metagenomics.Bioinformatics (Oxford, England) · 2012Article
- DNA-energetics-based analyses suggest additional genes in prokaryotes.Journal of biosciences · 2012Article
- Article
- Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and "resurrected" pseudogenes in the mouse genome.Genome research · 2011Article
- Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data.Computational systems bioinformatics. Computational Systems Bioinformatics Conference · 2010Article
- Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.Genome research · 2008Article
- Reranking candidate gene models with cross-species comparison for improved gene prediction.BMC bioinformatics · 2008Article
- Improving the specificity of exon prediction using comparative genomics.BMC genomics · 2008Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene recognition is one of the most important problems in computational molecular biology. Previous attempts to solve this problem were based on statistics, and applications of combinatorial methods for gene recognition were almost unexplored. Recent advances in large-scale cDNA sequencing open a way toward a new approach to gene recognition that uses previously sequenced genes as a clue for recognition of newly sequenced genes. This paper describes a spliced alignment algorithm and software tool that explores all possible exon assemblies in polynomial time and finds the multiexon structure with the best fit to a related protein. Unlike other existing methods, the algorithm successfully recognizes genes even in the case of short exons or exons with unusual codon usage; we also report correct assemblies for genes with more than 10 exons. On a test sample of human genes with known mammalian relatives, the average correlation between the predicted and actual proteins was 99%. The algorithm correctly reconstructed 87% of genes and the rare discrepancies between the predicted and real exon-intron structures were caused either by short (less than 5 amino acids) initial/terminal exons or by alternative splicing. Moreover, the algorithm predicts human genes reasonably well when the homologous protein is nonvertebrate or even prokaryotic. The surprisingly good performance of the method was confirmed by extensive simulations: in particular, with target proteins at 160 accepted point mutations (PAM) (25% similarity), the correlation between the predicted and actual genes was still as high as 95%.
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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.