Title
Genetic algorithm and optimized weight matrix application for peroxisome proliferator response elements recognition: Prerequisites of accuracy growth for wide genome research
Abstract
Development of reliable transcription factor binding site (TFBS) recognition methods is an important step in the large-scale genome analysis. The most of currently applied methods to predict functional TFBSs are hampered by the high false-positive rates that occur when too few functionally characterised sequences are available and only sequence conservation within a site core is considered. We propose two methods to search for binding sites (BSs) of peroxisome proliferator-activated receptor (PPAR) (peroxisome proliferator response elements, PPREs). The first method is the optimized dinucleotide position weight matrix (PWM) model, the second method represented by SiteGA model that used genetic algorithm with a discriminant function of locally positioned dinucleotides to infer the most important positions and dinucleotides. We used in our analysis two PPRE datasets, consisting of 37 and 98 BSs, correspondingly. We showed that dataset extension improved the accuracy of SiteGA, but not PWM model. Finally we combined both models (PWM and SiteGA) to the dataset of annotated human promoters (EPD). We demonstrated that the larger dataset and the longer window length supported notable growth of accuracies for PWM and SiteGA models. Consequently, a combined PWM and SiteGA application may better restrict the number of potential targets in the EPD promoter dataset.
Year
DOI
Venue
2008
10.3233/IDA-2008-12506
Intell. Data Anal.
Keywords
Field
DocType
discriminant analysis,peroxisome proliferator activated receptor,position weight matrix,genetic algorithm
Genome,Promoter,DNA binding site,Computer science,Matrix (mathematics),Position weight matrix,Linear discriminant analysis,Bioinformatics,Genetic algorithm,Discriminant function analysis
Journal
Volume
Issue
ISSN
12
5
1088-467X
Citations 
PageRank 
References 
1
0.36
12
Authors
6
Name
Order
Citations
PageRank
Victor G. Levitsky1577.52
Elena V. Ignatieva210425.66
Eugenia Aman310.36
Tatyana I. Merkulova411412.70
Nikolay A. Kolchanov5470105.75
T. Charles Hodgman6960187.36