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Cloud-based solution to identify statistically significant MS peaks differentiating sample categories
BACKGROUND: Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional p...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621609/ https://www.ncbi.nlm.nih.gov/pubmed/23522030 http://dx.doi.org/10.1186/1756-0500-6-109 |
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author | Ji, Jun Ling, Jeffrey Jiang, Helen Wen, Qiaojun Whitin, John C Tian, Lu Cohen, Harvey J Ling, Xuefeng B |
author_facet | Ji, Jun Ling, Jeffrey Jiang, Helen Wen, Qiaojun Whitin, John C Tian, Lu Cohen, Harvey J Ling, Xuefeng B |
author_sort | Ji, Jun |
collection | PubMed |
description | BACKGROUND: Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). “Turnkey” solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation. FINDINGS: Here we present an efficient and effective solution, which provides experimental biologists easy access to “cloud” computing capabilities to analyze MS data. The web portal can be accessed at http://transmed.stanford.edu/ssa/. CONCLUSIONS: Presented web application supplies large scale MS data online uploading and analysis with a simple user interface. This bioinformatic tool will facilitate the discovery of the potential protein biomarkers using MS. |
format | Online Article Text |
id | pubmed-3621609 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36216092013-04-10 Cloud-based solution to identify statistically significant MS peaks differentiating sample categories Ji, Jun Ling, Jeffrey Jiang, Helen Wen, Qiaojun Whitin, John C Tian, Lu Cohen, Harvey J Ling, Xuefeng B BMC Res Notes Technical Note BACKGROUND: Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). “Turnkey” solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation. FINDINGS: Here we present an efficient and effective solution, which provides experimental biologists easy access to “cloud” computing capabilities to analyze MS data. The web portal can be accessed at http://transmed.stanford.edu/ssa/. CONCLUSIONS: Presented web application supplies large scale MS data online uploading and analysis with a simple user interface. This bioinformatic tool will facilitate the discovery of the potential protein biomarkers using MS. BioMed Central 2013-03-23 /pmc/articles/PMC3621609/ /pubmed/23522030 http://dx.doi.org/10.1186/1756-0500-6-109 Text en Copyright © 2013 Ji et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Technical Note Ji, Jun Ling, Jeffrey Jiang, Helen Wen, Qiaojun Whitin, John C Tian, Lu Cohen, Harvey J Ling, Xuefeng B Cloud-based solution to identify statistically significant MS peaks differentiating sample categories |
title | Cloud-based solution to identify statistically significant MS peaks differentiating sample categories |
title_full | Cloud-based solution to identify statistically significant MS peaks differentiating sample categories |
title_fullStr | Cloud-based solution to identify statistically significant MS peaks differentiating sample categories |
title_full_unstemmed | Cloud-based solution to identify statistically significant MS peaks differentiating sample categories |
title_short | Cloud-based solution to identify statistically significant MS peaks differentiating sample categories |
title_sort | cloud-based solution to identify statistically significant ms peaks differentiating sample categories |
topic | Technical Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3621609/ https://www.ncbi.nlm.nih.gov/pubmed/23522030 http://dx.doi.org/10.1186/1756-0500-6-109 |
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