MISAE: a new approach for regulatory motif extraction.
The recognition of regulatory motifs of co-regulated genes is essential for understanding the regulatory mechanisms. However, the automatic extraction of regulatory motifs from a given data set of the upstream non-coding DNA sequences of a family of co-regulated genes is difficult because regulatory motifs are often subtle and inexact. This problem is further complicated by the corruption of the data sets. In this paper, a new approach called Mismatch-allowed Probabilistic Suffix Tree Motif Extraction (MISAE) is proposed. It combines the mismatch-allowed probabilistic suffix tree that is a probabilistic model and local prediction for the extraction of regulatory motifs. The proposed approach is tested on 15 co-regulated gene families and compares favorably with other state-of-the-art approaches. Moreover, MISAE performs well on "corrupted" data sets. It is able to extract the motif from a "corrupted" data set with less than one fourth of the sequences containing the real motif.
Sun
Zhaohui
Z
Department of Computer Science and Engineering, University of Nebraska at Lincoln, 68588-0115, USA. zsun@cse.unl.edu
Yang
Jingyi
J
Deogun
Jitender S
JS
D000465:Algorithms; D020816:Amino Acid Motifs; D016000:Cluster Analysis; D003198:Computer Simulation; D004247:DNA / Q000235:genetics*; D019277:Entropy; D015870:Gene Expression / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
D004247:DNA