For the past decade, research on identifying regulatory elements, notably the binding sites for transcription factors, has been very intense. The problem, usually abstracted as a search problem, takes as the input a set of sequences, which encode the regulatory regions of genes that are putatively co-regulated. The output consists of the regulatory elements (short words in the input sequences) and a motif model that profiles them.
Numerous computational tools have been developed for this task. Natually, evaluation of these tools is becoming vital in this area. Recently, Tompa et al. [1] report the results of one such assessment. In this assessment, some popular tools are tested on datasets of four species: human, mouse, fly and yeast. Each dataset contains a set of sequences planted with binding sites of one transcription factor. The binding sites are provided in the TRANSFAC database [2]. Details of the datasets are explained in [1].
Besides the result of the assessment, this work also raises questions about the approaches used by these tools. We discuss some interesting questions that arise from further analysis of the assessment in [1]. We believe that techniques that have been adopted in search are very powerful, as proven by these eminent tools. But the definition of the search problem, especially the formulation of objective functions, leaves space for substantial improvement in the performance of the motif discovery tool.