Comparative Analysis of DNA Structural Parameters and the Corresponding Computational Tools to Differentiate Regulatory DNA Motifs and Promoters

Analyzing and distinguishing functionally distinct DNA regions is crucial for various applications, including predicting DNA motifs and promoters, and exploring the mechanisms of gene expression regulation in disease conditions. Our understanding of mammalian promoters, particularly those associated with differentially expressed genes (DEGs), particularly remains limited. However, existing methods for such analysis require refinement. Despite the value of DNA Structural Parameters (DSPs), users often struggle to objectively select parameters and tools, especially given the limited options available. This study addresses this challenge by thoroughly investigating DSP-tool combinations - particularly the local structural parameters that can be analyzed via web-interfaces, with a goal to discern human DNA motifs and promoters. What sets this study apart are the following aspects: a) examination of disease-associated promoters; b) attention to regulatory specific DNA motifs; c) compilation and comparison of all publicly available online tools and parameters for analyzing DNA structures, and test all available DSP-tool combinations. Through the execution of over half a million queries, the study identified DSP-tool combinations that consistently outperformed others in differentiating DNA sequences across various types of analyses. Notably, the propeller twist emerged as a standout DSP, while DNAshape, complemented by DNAshapeR scripts, demonstrated exceptional performance among the tools across four distinct types of analyses: testing motifs, sequences post motif insertion, comparing promoters with control sequences, and analyzing promoters of genes either up- or down-regulated under disease conditions. Significant alterations were observed in the values of multiple DSPs for 100-nucleotide-long promoter and control sequences following the insertion of single motifs such as triplex target sites (TTS), quadruplex-forming structural motifs (QSFM), and transcription factor binding sites (TFBS). However, no single combination proved universally successful. For instance, when analyzing promoters associated with differential gene expression, different combinations proved effective across various human diseases. These findings provide valuable insights for making informed selections among available options for sequence-based queries.