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

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 inves...

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Autores principales: Polouliakh, Natalia, Horton, Paul, Shibanai, Kazuhiro, Takata, Kodai, Ludwig, Vanessa, Ghosh, Samik, Kitano, Hiroaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6161448/
https://www.ncbi.nlm.nih.gov/pubmed/30261835
http://dx.doi.org/10.1186/s12864-018-5101-3
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author Polouliakh, Natalia
Horton, Paul
Shibanai, Kazuhiro
Takata, Kodai
Ludwig, Vanessa
Ghosh, Samik
Kitano, Hiroaki
author_facet Polouliakh, Natalia
Horton, Paul
Shibanai, Kazuhiro
Takata, Kodai
Ludwig, Vanessa
Ghosh, Samik
Kitano, Hiroaki
author_sort Polouliakh, Natalia
collection PubMed
description 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 ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5101-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-61614482018-10-01 Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis Polouliakh, Natalia Horton, Paul Shibanai, Kazuhiro Takata, Kodai Ludwig, Vanessa Ghosh, Samik Kitano, Hiroaki BMC Genomics Software 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 ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5101-3) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-27 /pmc/articles/PMC6161448/ /pubmed/30261835 http://dx.doi.org/10.1186/s12864-018-5101-3 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Polouliakh, Natalia
Horton, Paul
Shibanai, Kazuhiro
Takata, Kodai
Ludwig, Vanessa
Ghosh, Samik
Kitano, Hiroaki
Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis
title Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis
title_full Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis
title_fullStr Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis
title_full_unstemmed Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis
title_short Sequence homology in eukaryotes (SHOE): interactive visual tool for promoter analysis
title_sort sequence homology in eukaryotes (shoe): interactive visual tool for promoter analysis
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6161448/
https://www.ncbi.nlm.nih.gov/pubmed/30261835
http://dx.doi.org/10.1186/s12864-018-5101-3
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