GAZE: A Generic Framework for the Integration of Gene-Prediction Data by Dynamic Programming
AUTOR(ES)
Howe, Kevin L.
FONTE
Cold Spring Harbor Laboratory Press
RESUMO
We describe a method (implemented in a program, GAZE) for assembling arbitrary evidence for individual gene components (features) into predictions of complete gene structures. Our system is generic in that both the features themselves, and the model of gene structure against which potential assemblies are validated and scored, are external to the system and supplied by the user. GAZE uses a dynamic programming algorithm to obtain the highest scoring gene structure according to the model and posterior probabilities that each input feature is part of a gene. A novel pruning strategy ensures that the algorithm has a run-time effectively linear in sequence length. To demonstrate the flexibility of our system in the incorporation of additional evidence into the gene prediction process, we show how it can be used to both represent nonstandard gene structures (in the form of trans-spliced genes in Caenorhabditis elegans), and make use of similarity information (in the form of Expressed Sequence Tag alignments), while requiring no change to the underlying software. GAZE is available at http://www.sanger.ac.uk/Software/analysis/GAZE.
ACESSO AO ARTIGO
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=186661Documentos Relacionados
- A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae)
- Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure
- GenFlow: generic flow for integration, management and analysis of molecular biology data
- Defining and Improving Data Quality in Medical Registries: A Literature Review, Case Study, and Generic Framework
- A dynamic programming algorithm for haplotype block partitioning