Bioinformatics, Vol 14, 14-19, Copyright © 1998 by Oxford University Press
SH Sze, MA Roytberg, MS Gelfand, AA Mironov, TV Astakhova and PA Pevzner
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 are 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 bypasses 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 algorithm's predictions achieves an accurate gene
identification with relatively few PCR primers. Predictions of PCR primers
may be used for exon amplification in preliminary mutation analysis during
an 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 are probably contained in 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 all translated regions and can be used for RT-PCR and further
sequencing of (unknown) mRNA.
ARTICLES
Algorithms and software for support of gene identification experiments
Department of Computer Science, University of Southern California, Los Angeles 90089-1113, USA.
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