Flame -flexible and accurate motif detector for continuous pattern discovery in sequence data sets

The basis of bioinformatics is the extraction of motifs from the sequences. Pattern emerging continuously either over a string or inside the same string are the significant objects for identifying. The continuous patterns are referred to be motifs and their detection is referred as motif interference or motif extraction. The most significant problem in biology is the motif search. This issue normally needs a voluminous data for detecting the short patterns of interest. Basically, we look at the issue of mining structured motifs which can allow the variable length gaps between simple motif components. Here in this research paper, we present a novel algorithm which is mainly used to detect the continuous pattern with a diversity of definitions of motif model and it is called as FLAME (Flexible and Accurate motif Detector). It also detects the pattern if it is exist so far. By using both the synthetic and real dataset we demonstrate that the FLAME algorithm is scalable, fast and outperforms the present algorithms that are available.