Identification of transcription binding sites within promoter regions of genomic DNA is imperative for the understanding of the regulatory circuits that direct the expression of genes. Such sites may be identified through biochemical experimentation or computationally. Computational analysis performed on gene promoter regions usually attempts to identify specific patterns and conserved subsequences. The Pattern/ Island Detection Algorithm (PIDA) is designed to identify patterns having multiple islands (common subsequences) in addition to allowing flexibility for both the island size and the distance between islands. In contrast to the general technique, which relies on weight matrices and related information-scoring functions, frequency based criteria is used to estimate statistical significance of such patterns. In 2003, K. Kobayshi et al. estimated the minimal gene set required to sustain bacterial life in Bacillus subtilis to only include 271 of its ≈4100 genes. As an example, PIDA was implemented to run on the set of promoters for these essential genes and the remaining ≈3800 "non-essential" gene promoters. Consequently, several new transcription factor binding site candidates were identified.