SOMEA: self-organizing map based extraction algorithm for DNA motif identification with heterogeneous model

• Main Authors: Lee, Nung Kion, Wang, Dianhui
• Format: E-Article
• Language: English
• Published: BioMed Central Ltd 2011
• Subjects: QA75 Electronic computers. Computer science; T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/11897/1/SOMEA_abstract.pdf; http://ir.unimas.my/id/eprint/11897/; http://download.springer.com/static/pdf/502/art%253A10.1186%252F1471-2105-12-S1-S16.pdf?originUrl=http%3A%2F%2Fbmcbioinformatics.biomedcentral.com%2Farticle%2F10.1186%2F1471-2105-12-S1-S16&token2=exp=1462430179~acl=%2Fstatic%2Fpdf%2F502%2Fart%25253A10.118

Background: Discrimination of transcription factor binding sites (TFBS) from background sequences plays a key role in computational motif discovery. Current clustering based algorithms employ homogeneous model for problem solving, which assumes that motifs and background signals can be equivalently characterized. This assumption has some limitations because both sequence signals have distinct properties. Results: This paper aims to develop a Self-Organizing Map (SOM) based clustering algorithm for extracting binding sites in DNA sequences. Our framework is based on a novel intra-node soft competitive procedure to achieve maximum discrimination of motifs from background signals in datasets. The intra-node competition is based on an adaptive weighting technique on two different signal models to better represent these two classes of signals. Using several real and artificial datasets, we compared our proposed method with several motif discovery tools. Compared to SOMBRERO, a state-of-the-art SOM based motif discovery tool, it is found that our algorithm can achieve significant improvements in the average precision rates (i.e., about 27%) on the real datasets without compromising its sensitivity. Our method also performed favourably comparing against other motif discovery tools. Conclusions: Motif discovery with model based clustering framework should consider the use of heterogeneous model to represent the two classes of signals in DNA sequences. Such heterogeneous model can achieve better signal discrimination compared to the homogeneous model