Gene and alternative splicing annotation with AIR

Designing effective and accurate tools for identifying the functional and structural elements in a genome remains at the frontier of genome annotation owing to incompleteness and inaccuracy of the data, limitations in the computational models, and shifting paradigms in genomics, such as alternative...

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Bibliographic Details
Published in:Genome research 2005-01, Vol.15 (1), p.54-66
Main Authors: Florea, Liliana, Di Francesco, Valentina, Miller, Jason, Turner, Russell, Yao, Alison, Harris, Michael, Walenz, Brian, Mobarry, Clark, Merkulov, Gennady V, Charlab, Rosane, Dew, Ian, Deng, Zuoming, Istrail, Sorin, Li, Peter, Sutton, Granger
Format: Article
Language:English
Subjects:
Alternative Splicing - genetics
Animals
Cats
Cattle
Chickens - genetics
Computational Biology - methods
Dogs
Evolution, Molecular
Genes - genetics
Genome
Methods
Mice
Models, Genetic
Pan troglodytes - genetics
Papio - genetics
Predictive Value of Tests
Rats
Software - standards
Swine - genetics
Synteny - genetics
Takifugu - genetics
Zebrafish - genetics
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Summary:Designing effective and accurate tools for identifying the functional and structural elements in a genome remains at the frontier of genome annotation owing to incompleteness and inaccuracy of the data, limitations in the computational models, and shifting paradigms in genomics, such as alternative splicing. We present a methodology for the automated annotation of genes and their alternatively spliced mRNA transcripts based on existing cDNA and protein sequence evidence from the same species or projected from a related species using syntenic mapping information. At the core of the method is the splice graph, a compact representation of a gene, its exons, introns, and alternatively spliced isoforms. The putative transcripts are enumerated from the graph and assigned confidence scores based on the strength of sequence evidence, and a subset of the high-scoring candidates are selected and promoted into the annotation. The method is highly selective, eliminating the unlikely candidates while retaining 98% of the high-quality mRNA evidence in well-formed transcripts, and produces annotation that is measurably more accurate than some evidence-based gene sets. The process is fast, accurate, and fully automated, and combines the traditionally distinct gene annotation and alternative splicing detection processes in a comprehensive and systematic way, thus considerably aiding in the ensuing manual curation efforts.
ISSN:1088-9051
DOI:10.1101/gr.2889405