GAZE: a generic framework for the integration of gene-prediction data by dynamic programming.

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.

[1]  David Hirsh,et al.  A trans-spliced leader sequence on actin mRNA in C. elegans , 1987, Cell.

[2]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[3]  Temple F. Smith,et al.  Prediction of gene structure. , 1992, Journal of molecular biology.

[4]  David Haussler,et al.  Optimally Parsing a Sequence into Different Classes Based on Multiple Types of Evidence , 1994, ISMB.

[5]  Ying Xu,et al.  Constructing gene models from accurately predicted exons: an application of dynamic programming , 1994, Comput. Appl. Biosci..

[6]  Victor V. Solovyev,et al.  Identification of Human Gene Structure Using Linear Discriminant Functions and Dynamic Programming , 1995, ISMB.

[7]  David Haussler,et al.  A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA , 1996, ISMB.

[8]  S. Karlin,et al.  Prediction of complete gene structures in human genomic DNA. , 1997, Journal of molecular biology.

[9]  Anders Krogh,et al.  Two Methods for Improving Performance of a HMM and their Application for Gene Finding , 1997, ISMB.

[10]  Richard Mott,et al.  EST_GENOME: a program to align spliced DNA sequences to unspliced genomic DNA , 1997, Comput. Appl. Biosci..

[11]  Thomas Blumenthal,et al.  RNA Processing and Gene Structure , 1997 .

[12]  S. Karlin,et al.  Finding the genes in genomic DNA. , 1998, Current opinion in structural biology.

[13]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[14]  Roderic Guigó,et al.  Assembling Genes from Predicted Exons In Linear Time with Dynamic Programming , 1998, J. Comput. Biol..

[15]  S. Lewis,et al.  Genome annotation assessment in Drosophila melanogaster. , 2000, Genome research.

[16]  R. Guigó,et al.  An assessment of gene prediction accuracy in large DNA sequences. , 2000, Genome research.

[17]  A. Krogh,et al.  Using database matches with for HMMGene for automated gene detection in Drosophila. , 2000, Genome research.

[18]  D. Haussler,et al.  Genie--gene finding in Drosophila melanogaster. , 2000, Genome research.

[19]  G. Stormo.  Gene-finding approaches for eukaryotes. , 2000, Genome research.

[20]  V. Solovyev,et al.  Ab initio gene finding in Drosophila genomic DNA. , 2000, Genome research.

[21]  C. Burge,et al.  Computational inference of homologous gene structures in the human genome. , 2001, Genome research.