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Extracting Signature Motifs from Promoter Sets of Differentially Expressed Genes
"... ABSTRACT: There is a critical need for new and efficient computational methods aimed at discovering putative transcription factor binding sites (TFBSs) in promoter sequences. Among the existing methods, two families can be distinguished: statistical or stochastic approaches, and combinatorial approa ..."
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ABSTRACT: There is a critical need for new and efficient computational methods aimed at discovering putative transcription factor binding sites (TFBSs) in promoter sequences. Among the existing methods, two families can be distinguished: statistical or stochastic approaches, and combinatorial approaches. Here we focus on a complete approach incorporating a combinatorial exhaustive motif extraction, together with a statistical Twilight Zone Indicator (TZI), in two datasets: a positive set and a negative one, which represents the result of a classical differential expression experiment. Our approach relies on the existence of prior biological information in the form of two sets of promoters of differentially expressed genes. We describe the complete procedure used for extracting either exact or degenerated motifs, ranking these motifs, and finding their known related TFBSs. We exemplify this approach using two different sets of promoters. The first set consists in promoters of genes either repressed or not by the transforming form of the v-erbA oncogene. The second set consists in genes the expression of which varies between self-renewing and differentiating progenitors. The biological meaning of the found TFBSs is discussed and, for one TF, its biological involvement is demonstrated. This study therefore illustrates the power of using relevant biological information, in the form of a set of differentially expressed genes that is a classical outcome in most of transcriptomics studies. This allows to severely reduce the search space and to design an adapted statistical indicator. Taken together, this allows the biologist to
2008 IEEE International Conference on Data Mining Workshops Parameter Tuning for Differential Mining of String Patterns
"... Constraint-based mining has been proven to be extremely useful for supporting actionable pattern discovery. However, useful conjunctions of constraints that support domain driven mining tasks generally need to set several parameter values and how to tune these parameters remains fairly open. We stud ..."
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Constraint-based mining has been proven to be extremely useful for supporting actionable pattern discovery. However, useful conjunctions of constraints that support domain driven mining tasks generally need to set several parameter values and how to tune these parameters remains fairly open. We study this problem for substring pattern discovery, when using a conjunction of maximal frequency, minimal frequency and size constraints. We propose a method, based on pattern space sampling, to estimate the number of patterns that satisfy such conjunctions. This permits the user to probe the parameter space in many points, and then to choose some initial promising parameter settings. Our empirical validation confirms that we efficiently obtain good approximations of the number of patterns that will be extracted. 1.
