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

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• Transcription factor binding sites (TFBS) regulate gene expression by controlling transcription.
• Understanding the precise location and order of TFBS is crucial for deciphering gene regulatory networks.
• Current methods may lack the specificity or scope to comprehensively analyze TFBS across diverse organisms.

Purpose of the Study:

• To develop and validate a computational method for identifying and mapping transcription factor binding sites (TFBS) in eukaryotic and prokaryotic promoter sequences.
• To analyze the order and type of transcription factor (TF) binding for genes involved in central metabolic pathways (CMP).
• To establish a novel similarity measure for promoter sequences and construct phylogenetic trees based on TFBS to compare conserved regulatory elements.

Main Methods:

• An algorithm was developed to search for TFBS around the transcription start site (+1 TSS) in both eukaryotic and prokaryotic sequences.
• The method was tested using promoter sequences from eukaryotes and prokaryotes with at least 9 experimentally validated TFs.
• A new similarity score based on motif number and order was devised, leading to sequence clustering and phylogenetic tree construction.

Main Results:

• The algorithm successfully identified and mapped TFBS and TFs in tested promoter sequences.
• The order and type of TF binding to CMP enzyme-encoding genes were systematically tabulated.
• A novel similarity measure enabled effective clustering of promoter sequences and the development of phylogenetic trees, revealing conserved TFBS patterns.

Conclusions:

• The proposed method provides an effective approach for identifying and mapping TFBS and TFs across different species.
• The analysis of TF binding order and sequence similarity offers new insights into gene regulation and evolutionary conservation of regulatory elements.
• This approach can be applied to various biological pathways, including lipid and amino acid biosynthesis, to compare experimentally verified and conserved TFBS.