Abstract
Motivation : A new approach to the prediction of eukaryotic PolII
promoters from DNA sequence takesadvantage of a combination of
elements similar to neural networks and genetic algorithms to
recognize a set ofdiscrete subpatterns with variable separation as
one pattern: a promoter. The neural networks use as input a
smallwindow of DNA sequence, as well as the output of other neural
networks. Through the use of geneticalgorithms, the weights in the
neural networks are optimized to discriminate maximally between
promoters andnon-promoters. Results : After several thousand
generations of optimization, the algorithm was able todiscriminate
between vertebrate promoter and non-promoter sequences in a test
set with a correlationcoefficient of 0.63. In addition, all five
known transcription start sites on the plus strand of the
completeadenovirus genome were within 161 bp of 35 predicted
transcription start sites. On standardized test setsconsisting of
human genomic DNA, the performance of Promoter2.0 compares well
with other softwaredeveloped for the same purpose. Availability :
Promoter2.0 is available as a Web server at
http://www.cbs.dtu.dk/services/promoter/ Contact : [email protected]
Original language | English |
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Journal | Bioinformatics |
Volume | 15 |
Issue number | 5 |
Pages (from-to) | 356-61 |
ISSN | 1367-4803 |
Publication status | Published - 1999 |