A survey of DNA motif finding algorithms

Background: Unraveling the mechanisms that regulate gene expression is a major challenge in biology. An important task in this challenge is to identify regulatory elements, especially the binding sites in deoxyribonucleic acid (DNA) for transcription factors. These binding sites are short DNA segments that are called motifs. Recent advances in genome sequence availability and in high-throughput gene expression analysis technologies have allowed for the development of computational methods for motif finding. As a result, a large number of motif finding algorithms have been implemented and applied to various motif models over the past decade. This survey reviews the latest developments in DNA motif finding algorithms.Results: Earlier algorithms use promoter sequences of coregulated genes from single genome and search for statistically overrepresented motifs. Recent algorithms are designed to use phylogenetic footprinting or orthologous sequences and also an integrated approach where promoter sequences of coregulated genes and phylogenetic footprinting are used. All the algorithms studied have been reported to correctly detect the motifs that have been previously detected by laboratory experimental approaches, and some algorithms were able to find novel motifs. However, most of these motif finding algorithms have been shown to work successfully in yeast and other lower organisms, but perform significantly worse in higher organisms.Conclusion: Despite considerable efforts to date, DNA motif finding remains a complex challenge for biologists and computer scientists. Researchers have taken many different approaches in developing motif discovery tools and the progress made in this area of research is very encouraging. Performance comparison of different motif finding tools and identification of the best tools have proven to be a difficult task because tools are designed based on algorithms and motif models that are diverse and complex and our incomplete understanding of the biology of regulatory mechanism does not always provide adequate evaluation of underlying algorithms over motif models.Peer reviewedComputer Scienc

Knowledge discovery in biological databases : a neural network approach

Knowledge discovery, in databases, also known as data mining, is aimed to find significant information from a set of data. The knowledge to be mined from the dataset may refer to patterns, association rules, classification and clustering rules, and so forth. In this dissertation, we present a neural network approach to finding knowledge in biological databases. Specifically, we propose new methods to process biological sequences in two case studies: the classification of protein sequences and the prediction of E. Coli promoters in DNA sequences. Our proposed methods, based oil neural network architectures combine techniques ranging from Bayesian inference, coding theory, feature selection, dimensionality reduction, to dynamic programming and machine learning algorithms. Empirical studies show that the proposed methods outperform previously published methods and have excellent performance on the latest dataset. We have implemented the proposed algorithms into an infrastructure, called Genome Mining, developed for biosequence classification and recognition