Motif Discovery and Data Mining in Bioinformatics

Abstract

Bioinformatics analyses huge amounts of biological data that demands in-depth understanding. On the other hand, data mining research develops methods for discovering motifs in biosequences. Motif discovery involves benefits and challenges. We show bridge of the two fields, data mining and Bioinformatics, for successful mining of biological data. We found the motivation and justification factors lead to preferring naturalistic method research for Bioinformatics, because naturalistic method depends on real data. The method empowers Bioinformatics techniques to handle the true properties and reducing assumptions for un-modeled or uncover biodata phenomena. The empowerment comes from recognizing and understanding biodata properties and processes.

A survey of exact motif finding algorithms

“…consider the following example for protein motif: KVVVKMKMMMQ [9], [10], AVCCWWE [6], [8] [10] is a gap; the distance between two simple motifs, its lower number of unspecified bases is 9bases and its upper number of unspecified bases is 10 bases. - [6], [8] is the second gap; the distance between two simple motifs, its lower number of unspecified bases is 6 bases and its upper number of unspecified bases is 8 bases.…”

“…Motif discovery/motif mining in the sequences of biological data is defined as the process of finding one or more of sequence components, ('motifs') in nucleotide sequence which have shared biological operations and activities. It is interesting problem for researchers due to its importance in many bioinformatics applications such as transcription factor binding site (TFBS) [10]- [13]. Motif discovery process is usually divided into three modules/stages; data preprocessing, motif mining, and post-processing.…”