Computational approach towards promoter sequence comparison via TF mapping using a new distance measure

• Published in: Interdisciplinary sciences : computational life sciences Vol. 3; no. 1; pp. 43 - 49
• Main Authors: Meera, A., Rangarajan, Lalitha, Bhat, Savithri
• Format: Journal Article
• Language: English
• Published: Springer International Association of Scientists in the Interdisciplinary Areas 01.03.2011; Springer Nature B.V
• Subjects: Algorithms; Amino acids; Binding Sites; Biomedical and Life Sciences; Biosynthesis; Citric Acid Cycle - genetics; Clusters; Computation; Computational Biology - methods; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Enzymes; Glycolysis - genetics; Health Sciences; Life Sciences; Mapping; Mathematical and Computational Physics; Medicine; Pathways; Promoter Regions, Genetic; Searching; Similarity; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Transcription Factors - metabolism; Transcription Initiation Site; similarity measure; cluster; database; pattern matching; central metabolic pathway; transcription factor binding sites (TFBS); phylogeny; promoter sequence; transcription factors (TFs)
• ISSN: 1913-2751; 1867-1462
• DOI: 10.1007/s12539-011-0057-x

We propose a method for identifying transcription factor binding sites (TFBS) in the given promoter sequence and mapping the transcription factors (TFs). The proposed algorithm searches the +1 transcription start site (TSS) for eukaryotic and prokaryotic sequences individually. The algorithm was tested with sequences from both eukaryotes and prokaryotes for at least 9 experimentally verified and validated functional TFs in promoter sequences. The order and type of TF binding to the promoter of genes encoding central metabolic pathway (CMP) enzyme was tabulated. A new similarity measure was devised for scoring the similarity between a pair of promoter sequences based on the number and order of motifs. Further, these were grouped in clusters considering the scores between them. The distance between each of the clusters in individual pathway was calculated and a phylogenetic tree was developed. This method is further applied to other pathways such as lipid and amino acid biosynthesis to retrieve and compare experimentally verified and conserved TFBS.