Ian Korf

BACKGROUND: Computational gene prediction continues to be an important problem, especially for genomes with little experimental data. RESULTS: I introduce the SNAP gene finder which has been designed to be easily adaptable to a variety of genomes. In novel genomes without an appropriate gene finder, I demonstrate that employing a foreign gene finder can produce highly inaccurate results, and that the most compatible parameters may not come from the nearest phylogenetic neighbor. I find that foreign gene finders are more usefully employed to bootstrap parameter estimation and that the resulting parameters can be highly accurate. CONCLUSION: Since gene prediction is sensitive to species-specific parameters, every genome needs a dedicated gene finder.

MOTIVATION: The numbers of finished and ongoing genome projects are increasing at a rapid rate, and providing the catalog of genes for these new genomes is a key challenge. Obtaining a set of well-characterized genes is a basic requirement in the initial steps of any genome annotation process. An accurate set of genes is needed in order to learn about species-specific properties, to train gene-finding programs, and to validate automatic predictions. Unfortunately, many new genome projects lack comprehensive experimental data to derive a reliable initial set of genes. RESULTS: In this study, we report a computational method, CEGMA (Core Eukaryotic Genes Mapping Approach), for building a highly reliable set of gene annotations in the absence of experimental data. We define a set of conserved protein families that occur in a wide range of eukaryotes, and present a mapping procedure that accurately identifies their exon-intron structures in a novel genomic sequence. CEGMA includes the use of profile-hidden Markov models to ensure the reliability of the gene structures. Our procedure allows one to build an initial set of reliable gene annotations in potentially any eukaryotic genome, even those in draft stages. AVAILABILITY: Software and data sets are available online at http://korflab.ucdavis.edu/Datasets.

We have developed a portable and easily configurable genome annotation pipeline called MAKER. Its purpose is to allow investigators to independently annotate eukaryotic genomes and create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab initio gene predictions, and automatically synthesizes these data into gene annotations having evidence-based quality indices. MAKER is also easily trainable: Outputs of preliminary runs are used to automatically retrain its gene-prediction algorithm, producing higher-quality gene-models on subsequent runs. MAKER's inputs are minimal, and its outputs can be used to create a GMOD database. Its outputs can also be viewed in the Apollo Genome browser; this feature of MAKER provides an easy means to annotate, view, and edit individual contigs and BACs without the overhead of a database. As proof of principle, we have used MAKER to annotate the genome of the planarian Schmidtea mediterranea and to create a new genome database, SmedGD. We have also compared MAKER's performance to other published annotation pipelines. Our results demonstrate that MAKER provides a simple and effective means to convert a genome sequence into a community-accessible genome database. MAKER should prove especially useful for emerging model organism genome projects for which extensive bioinformatics resources may not be readily available.

The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort. [Supplemental material is available online at www.genome.org . Bioperl is available as open-source software free of charge and is licensed under the Perl Artistic License ( http://www.perl.com/pub/a/language/misc/Artistic.html ). It is available for download at http://www.bioperl.org . Support inquiries should be addressed to bioperl-l@bioperl.org .]