PromFD 1.0: a computer program that predicts eukaryotic pol II promoters using strings and IMD matrices

Abstract

Motivation: A large number of new DNA sequences with virtually unknown functions are generated as the Human Genome Project progresses. Therefore, it is essential to develop computer algorithms that can predict the functionality of DNA segments according to their primary sequences, including algorithms that can predict promoters. Although several promoter-predicting algorithms are available, they have high false-positive detections and the rate of promoter detection needs to be improved further. Results: In this research, PromFD, a computer program to recognize vertebrate RNA polymerase II promoters, has been developed. Both vertebrate promoters and non-promoter sequences are used in the analysis. The promoters are obtained from the Eukaryotic Promoter Database. Promoters are divided into a training set and a test set. Non-promoter sequences are obtained from the GenBank sequence databank, and are also divided into a training set and a test set. The first step is to search out, among all possible permutations, patterns of strings 5-10 bp long, that are significantly over-represented in the promoter set. The program also searches IMD (Information Matrix Database) matrices that have a significantly higher presence in the promoter set. The results of the searches are stored in the PromFD database, and the program PromFD scores input DNA sequences according to their content of the database entries. PromFD predicts promoters-their locations and the location of potential TATA boxes, if found. The program can detect 71% of promoters in the training set with a false-positive rate of under 1 in every 13 000 bp, and 47% of promoters in the test set with a false-positive rate of under 1 in every 9800 bp. PromFD uses a new approach and its false-positive identification rate is better compared with other available promoter recognition algorithms. The source code for PromFD is in the 'c++' language. Availability: PromFD is available for Unix platforms by anonymous ftp to: beagle.colorado.edu, cdpub, get promFD.tar.

Eukaryotic Promoter Recognition

“…In sequences with complete genes, GRAIL performed better than the other algorithms (data not shown), but it is difficult to judge how well this reflects the performance of the promoter module per se. The program of Chen et al (1997) also makes predictions that are not comparable with the others, being non-strand-specific. The method of Crowley et al (1997) was published after the benchmarking here had been carried out.…”

Using TESS to Predict Transcription Factor Binding Sites in DNA Sequence

“…The TESS Web site offers facilities for browsing and querying the data from TRANSFAC (v. 6.0;Wingender, 2001), JASPAR (downloaded 2006/03/01; Vlieghe et al, 2006), IMD (v. 1.1; Chen, 1997), and CBIL-GibbsMat (v. 1.1; Schug and Overton, 1997) and searching for binding sites in DNA sequences. Results of sequence searches are presented in various formats and can be downloaded as an Excel spreadsheet.…”

A Bayesian method for finding regulatory segments in DNA

“…However, a later study noted that this approach does not resolve the problem of redundancy. 7 In addition, sequences that are extracted from TFD do not necessarily define specific control elements (words) in DNA.…”