Algorithms and software for support of gene identification experiments

Motivation: Gene annotation is the final goal of gene prediction algorithms?. However these algorithms frequently make mistakes and therefore the use of gene predictions for sequence annotation is hardly possible. As a result, biologists ma forced to conduct time-consuming gene identification experiments by designing appropriate PCR primers to test cDNA libraries or applying RT-PCR, exon trapping/amplification, or other techniques. This process frequently amounts to 'guessing' PCR primers on top of unreliable gene predictions and frequently leads to wasting of experimental efforts. Results: The present paper proposes a simple and reliable algorithm for experimental gene identification which by passes the unreliable gene prediction step. Studies of the performance of the algorithm on a sample of human genes indicate that an experimental protocol based on the algorithms predictions achieves at? accurate gene identification with relatively few PCR primers. Predictions of PCR primers may be used for exon amplification in preliminary! mutation analysis during ai? attempt to identify a gene responsible for a disease. We propose a simple approach to find a short region from a genomic sequence that with high probability overlaps with some exon of the gene. The algorithm is enhanced to find one or more segments that al-e probably contained hilt the translated region of the gene and can be used as PCR primers to select appropriate clones in cDNA libraries by selective amplification. The algorithm is further extended to locate a set of PCR primers that uniformly: cover ail translated regions anti can be used Soi RT-PCR and further sequencing of (unknown) mRNA. Availability: The programs are implemented as Web servers (GenePrimer and CASSANDRA) and can be reached at http://www-hto.usc.edu/software/procrustes/ Contact: ssze@hto.usc.edu.