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SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles
BACKGROUND: Molecular signatures are collections of genes characteristic of a particular cell type, tissue, disease, or perturbation. Signatures can also be used to interpret expression profiles generated from heterogeneous samples. Large collections of gene signatures have been previously developed...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5657101/ https://www.ncbi.nlm.nih.gov/pubmed/29070035 http://dx.doi.org/10.1186/s12864-017-4167-7 |
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author | Lopez, David Montoya, Dennis Ambrose, Michael Lam, Larry Briscoe, Leah Adams, Claire Modlin, Robert L. Pellegrini, Matteo |
author_facet | Lopez, David Montoya, Dennis Ambrose, Michael Lam, Larry Briscoe, Leah Adams, Claire Modlin, Robert L. Pellegrini, Matteo |
author_sort | Lopez, David |
collection | PubMed |
description | BACKGROUND: Molecular signatures are collections of genes characteristic of a particular cell type, tissue, disease, or perturbation. Signatures can also be used to interpret expression profiles generated from heterogeneous samples. Large collections of gene signatures have been previously developed and catalogued in the MSigDB database. In addition, several consortia and large-scale projects have systematically profiled broad collections of purified primary cells, molecular perturbations of cell types, and tissues from specific diseases, and the specificity and breadth of these datasets can be leveraged to create additional molecular signatures. However, to date there are few tools that allow the visualization of individual signatures across large numbers of expression profiles. Signature visualization of individual samples allows, for example, the identification of patient subcategories a priori on the basis of well-defined molecular signatures. RESULT: Here, we generate and compile 10,985 signatures (636 newly-generated and 10,349 previously available from MSigDB) and provide a web-based Signature Visualization Tool (SaVanT; http://newpathways.mcdb.ucla.edu/savant), to visualize these signatures in user-generated expression data. We show that using SaVanT, immune activation signatures can distinguish patients with different types of acute infections (influenza A and bacterial pneumonia). Furthermore, SaVanT is able to identify the prominent signatures within each patient group, and identify the primary cell types underlying different leukemias (acute myeloid and acute lymphoblastic) and skin disorders. CONCLUSIONS: The development of SaVanT facilitates large-scale analysis of gene expression profiles on a patient-level basis to identify patient subphenotypes, or potential therapeutic target pathways. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-017-4167-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5657101 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56571012017-10-31 SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles Lopez, David Montoya, Dennis Ambrose, Michael Lam, Larry Briscoe, Leah Adams, Claire Modlin, Robert L. Pellegrini, Matteo BMC Genomics Software BACKGROUND: Molecular signatures are collections of genes characteristic of a particular cell type, tissue, disease, or perturbation. Signatures can also be used to interpret expression profiles generated from heterogeneous samples. Large collections of gene signatures have been previously developed and catalogued in the MSigDB database. In addition, several consortia and large-scale projects have systematically profiled broad collections of purified primary cells, molecular perturbations of cell types, and tissues from specific diseases, and the specificity and breadth of these datasets can be leveraged to create additional molecular signatures. However, to date there are few tools that allow the visualization of individual signatures across large numbers of expression profiles. Signature visualization of individual samples allows, for example, the identification of patient subcategories a priori on the basis of well-defined molecular signatures. RESULT: Here, we generate and compile 10,985 signatures (636 newly-generated and 10,349 previously available from MSigDB) and provide a web-based Signature Visualization Tool (SaVanT; http://newpathways.mcdb.ucla.edu/savant), to visualize these signatures in user-generated expression data. We show that using SaVanT, immune activation signatures can distinguish patients with different types of acute infections (influenza A and bacterial pneumonia). Furthermore, SaVanT is able to identify the prominent signatures within each patient group, and identify the primary cell types underlying different leukemias (acute myeloid and acute lymphoblastic) and skin disorders. CONCLUSIONS: The development of SaVanT facilitates large-scale analysis of gene expression profiles on a patient-level basis to identify patient subphenotypes, or potential therapeutic target pathways. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-017-4167-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-10-25 /pmc/articles/PMC5657101/ /pubmed/29070035 http://dx.doi.org/10.1186/s12864-017-4167-7 Text en © The Author(s). 2017 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 Lopez, David Montoya, Dennis Ambrose, Michael Lam, Larry Briscoe, Leah Adams, Claire Modlin, Robert L. Pellegrini, Matteo SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles |
title | SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles |
title_full | SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles |
title_fullStr | SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles |
title_full_unstemmed | SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles |
title_short | SaVanT: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles |
title_sort | savant: a web-based tool for the sample-level visualization of molecular signatures in gene expression profiles |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5657101/ https://www.ncbi.nlm.nih.gov/pubmed/29070035 http://dx.doi.org/10.1186/s12864-017-4167-7 |
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