Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis.

Area of Science:

• Genomics
• Bioinformatics
• Systems Biology

Background:

• High-throughput technologies like microarrays and DNA sequencing generate vast amounts of gene expression data.
• Interpreting this data requires sophisticated computational tools for visualization and analysis.
• Understanding gene regulation is crucial for advancements in biology and medicine.

Purpose of the Study:

• To develop an interactive web server, SHOE, for exploring and interpreting gene expression data.
• To facilitate the visualization of transcriptional regulation and gene co-expression.
• To aid researchers in uncovering biological insights from complex genomic datasets.

Main Methods:

• SHOE integrates transcription factor binding preferences (PSSM libraries), evolutionary conservation, and gene co-expression data.
• It analyzes orthologous promoters in humans, mice, and rats.
• Gene co-expression data is derived from 1,714 normal human cell samples from the Gene Expression Omnibus Database.

Main Results:

• SHOE provides an interactive, visual presentation of transcriptional regulation and gene co-expression evidence.
• The server predicts transcription factor binding sites by combining PSSM libraries, evolutionary conservation, and co-expression data.
• It enables the exploration of potential transcription factor-target gene interactions.

Conclusions:

• SHOE facilitates the discovery of transcription factor binding motifs alongside gene co-expression patterns.
• Users can visualize gene regulatory networks using integrated tools like GeneViz, CellDesigner, and Reactome.
• The platform reliably identifies known interactions and predicts novel ones, offering new biological insights into gene regulatory mechanisms.