Using mRNAs lengths to accurately predict the alternatively spliced gene products in Caenorhabditis elegans.
BACKGROUND
Computational gene prediction methods are an important component of whole genome analyses. While ab initio gene finders have demonstrated major improvements in accuracy, the most reliable methods are evidence-based gene predictors. These algorithms can rely on several different sources of evidence including predictions from multiple ab initio gene finders, matches to known proteins, sequence conservation and partial cDNAs to predict the final product. Despite the success of these algorithms, prediction of complete gene structures, especially for alternatively spliced products, remains a difficult task.


RESULTS
LOCUS (Length Optimized Characterization of Unknown Spliceforms) is a new evidence-based gene finding algorithm which integrates a length-constraint into a dynamic programming-based framework for prediction of gene products. On a Caenorhabditis elegans test set of alternatively spliced internal exons, its performance exceeds that of current ab initio gene finders and in most cases can accurately predict the correct form of all the alternative products. As the length information used by the algorithm can be obtained in a high-throughput fashion, we propose that integration of such information into a gene-prediction pipeline is feasible and doing so may improve our ability to fully characterize the complete set of mRNAs for a genome.


BACKGROUND
LOCUS is available from http://ural.wustl.edu/software.html
Agrawal
Ritesh
R
Department of Genetics, Washington University School of Medicine 660 S. Euclid, Campus Box 8232, St. Louis, MO 63110, USA.
Stormo
Gary D
GD
D000465:Algorithms; D017398:Alternative Splicing / Q000235:genetics*; D000818:Animals; D001483:Base Sequence; D017173:Caenorhabditis elegans / Q000235:genetics*; D029742:Caenorhabditis elegans Proteins / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D015870:Gene Expression / Q000235:genetics*; D008957:Models, Genetic; D008969:Molecular Sequence Data; D012333:RNA, Messenger / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*
D029742:Caenorhabditis elegans Proteins; D012333:RNA, Messenger