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Background: Microarray and DNA-sequencing based technologies continue to produce enormous amounts of data on gene expression. This data has great potential to illuminate our understanding of biology and medicine, but the data alone is of limited value without computational tools to allow human investigators to visualize and interpret it in the context of their problem of interest. Results: We created a web server called SHOE that provides an interactive, visual presentation of the available evidence of transcriptional regulation and gene co-expression to facilitate its exploration and interpretation. SHOE predicts the likely transcription factor binding sites in orthologous promoters of humans, mice, and rats using the combined information of 1) transcription factor binding preferences (position-specific scoring matrix (PSSM) libraries such as Transfac32, Jaspar, HOCOMOCO, ChIP-seq, SELEX, PBM, and iPS-reprogramming factor), 2) evolutionary conservation of putative binding sites in orthologous promoters, and 3) co-expression tendencies of gene pairs based on 1,714 normal human cells selected from the Gene Expression Omnibus Database. Conclusion: SHOE enables users to explore potential interactions between transcription factors and target genes via multiple data views, discover transcription factor binding motifs on top of gene co-expression, and visualize genes as a network of gene and transcription factors on its native gadget GeneViz, the CellDesigner pathway analyzer, and the Reactome database to search the pathways involved. As we demonstrate here when using the CREB1 and Nf-κB datasets, SHOE can reliably identify experimentally verified interactions and predict plausible novel ones, yielding new biological insights into the gene regulatory mechanisms involved. SHOE comes with a manual describing how to run it on a local PC or via the Garuda platform (www.garuda-alliance.org), where it joins other popular gadgets such as the CellDesigner pathway analyzer and the Reactome database, as part of analysis workflows to meet the growing needs of molecular biologists and medical researchers. SHOE is available from the following URL http://ec2-54-150-223-65.ap-northeast-1.compute.amazonaws.com A video demonstration of SHOE can be found here: https://www.youtube.com/watch?v=qARinNb9NtE.
Polouliakh, N., Horton, P., Shibanai, K., Takata, K., Ludwig, V., Ghosh, S., & Kitano, H. (2018). Sequence homology in eukaryotes (SHOE): Interactive visual tool for promoter analysis 06 Biological Sciences 0604 Genetics. BMC Genomics, 19(1). https://doi.org/10.1186/s12864-018-5101-3