GENSCAN

In bioinformatics, GENSCAN is a program to identify complete gene structures in genomic DNA. It is a GHMM-based program that can be used to predict the location of genes and their exon-intron boundaries in genomic sequences from a variety of organisms. The GENSCAN Web server can be found at MIT.[1]

GENSCAN
Developer(s)Christopher Burge
Available inEnglish
TypeBioinformatics tool
Websitegenes.mit.edu/GENSCANinfo.html

GENSCAN was developed by Christopher Burge in the research group of Samuel Karlin at Stanford University.[2][3][4]

GENSCAN is shown to have higher accuracy than existing methods when tested on standardized sets of human and vertebrate genes. Additionally, the program is capable of indicating fairly accurately the reliability of each predicted exon.[3]

History

GENSCAN was first applied by Christopher Burge and Samuel Karlin to predict complete gene structures in human genomic DNA. The general probabilistic model of the gene structure of human genomic sequences was applied to the problem of gene identification using GENSCAN. At the time, the novel features of the program included the capacity to predict multiple genes in a sequence, to deal with partial and complete genes, and to predict consistent sets of genes occurring on the DNA strands.[3]

References
  1. http://genes.mit.edu/GENSCAN.html Archived 2013-09-06 at the Wayback Machine The GENSCAN Web Server at MIT
  2. Burge, C. B. (1998) Modeling dependencies in pre-mRNA splicing signals. In Salzberg, S., Searls, D. and Kasif, S., eds. Computational Methods in Molecular Biology, Elsevier Science, Amsterdam, pp. 127-163. ISBN 978-0-444-50204-9
  3. Burge, Christopher; Karlin, Samuel (1997). "Prediction of complete gene structures in human genomic DNA" (PDF). Journal of Molecular Biology. 268 (1): 78–94. CiteSeerX 10.1.1.115.3107. doi:10.1006/jmbi.1997.0951. PMID 9149143. Archived from the original (PDF) on 2015-06-20.
  4. Burge, C.; Karlin, S. (1998). "Finding the genes in genomic DNA". Current Opinion in Structural Biology. 8 (3): 346–354. doi:10.1016/S0959-440X(98)80069-9. PMID 9666331.
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