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+2' language.