Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis
Autor: | Polouliakh, Natalia, Horton, Paul, Shibanai, Kazuhiro, Takata, Kodai, Ludwig, Vanessa, Ghosh, Samik, Kitano, Hiroaki |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
0301 basic medicine
lcsh:QH426-470 lcsh:Biotechnology Gene regulatory network Context (language use) Computational biology Biology Evolution Molecular 03 medical and health sciences Mice lcsh:TP248.13-248.65 Sequence Homology Nucleic Acid Genetics Animals Humans Position-Specific Scoring Matrices Promoter Regions Genetic Transcription factor Gene Comparative genomics Transcription regulation gene network Internet Binding Sites Computational Biology Promoter DNA Gene network Rats DNA binding site lcsh:Genetics 030104 developmental biology Gene Expression Regulation DNA microarray Software Biotechnology Transcription Factors |
Zdroj: | BMC Genomics BMC Genomics, 19 BMC Genomics, Vol 19, Iss 1, Pp 1-11 (2018) |
ISSN: | 1471-2164 |
Popis: | 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 BMC Genomics, 19 |
Databáze: | OpenAIRE |
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