Roded Sharan1, Eugene W Myers. 1. School of Computer Science, Tel-Aviv University Tel-Aviv 69978, Israel. roded@tau.ac.il
Abstract
MOTIVATION: Many signals in biological sequences are based on the presence or absence of base signals and their spatial combinations. One of the best known examples of this is the signal identifying a core promoter--the site at which the basal transcription machinery starts the transcription of a gene. Our goal is a fully automatic pattern recognition system for a family of sequences, which simultaneously discovers the base signals, their spatial relationships and a classifier based upon them. RESULTS: In this paper we present a general method for characterizing a set of sequences by their recurrent motifs. Our approach relies on novel probabilistic models for DNA binding sites and modules of binding sites, on algorithms to study them from the data and on a support vector machine that uses the models studied to classify a set of sequences. We demonstrate the applicability of our approach to diverse instances, ranging from families of promoter sequences to a dataset of intronic sequences flanking alternatively spliced exons. On a core promoter dataset our results are comparable with the state-of-the-art McPromoter. On a dataset of alternatively spliced exons we outperform a previous approach. We also achieve high success rates in recognizing cell cycle regulated genes. These results demonstrate that a fully automatic pattern recognition algorithm can meet or exceed the performance of hand-crafted approaches. AVAILABILITY: The software and datasets are available from the authors upon request.
MOTIVATION: Many signals in biological sequences are based on the presence or absence of base signals and their spatial combinations. One of the best known examples of this is the signal identifying a core promoter--the site at which the basal transcription machinery starts the transcription of a gene. Our goal is a fully automatic pattern recognition system for a family of sequences, which simultaneously discovers the base signals, their spatial relationships and a classifier based upon them. RESULTS: In this paper we present a general method for characterizing a set of sequences by their recurrent motifs. Our approach relies on novel probabilistic models for DNA binding sites and modules of binding sites, on algorithms to study them from the data and on a support vector machine that uses the models studied to classify a set of sequences. We demonstrate the applicability of our approach to diverse instances, ranging from families of promoter sequences to a dataset of intronic sequences flanking alternatively spliced exons. On a core promoter dataset our results are comparable with the state-of-the-art McPromoter. On a dataset of alternatively spliced exons we outperform a previous approach. We also achieve high success rates in recognizing cell cycle regulated genes. These results demonstrate that a fully automatic pattern recognition algorithm can meet or exceed the performance of hand-crafted approaches. AVAILABILITY: The software and datasets are available from the authors upon request.