Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/94793
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Type: Journal article
Title: APP: an Automated Proteomics Pipeline for the analysis of mass spectrometry data based on multiple open access tools
Author: Malm, E.
Srivastava, V.
Sundqvist, G.
Bulone, V.
Citation: BMC Bioinformatics, 2014; 15(1):441-1-441-8
Publisher: BioMed Central
Issue Date: 2014
ISSN: 1471-2105
1471-2105
Statement of
Responsibility: 
Erik K Malm, Vaibhav Srivastava, Gustav Sundqvist and Vincent Bulone
Abstract: BACKGROUND: Mass spectrometry analyses of complex protein samples yield large amounts of data and specific expertise is needed for data analysis, in addition to a dedicated computer infrastructure. Furthermore, the identification of proteins and their specific properties require the use of multiple independent bioinformatics tools and several database search algorithms to process the same datasets. In order to facilitate and increase the speed of data analysis, there is a need for an integrated platform that would allow a comprehensive profiling of thousands of peptides and proteins in a single process through the simultaneous exploitation of multiple complementary algorithms. RESULTS: We have established a new proteomics pipeline designated as APP that fulfills these objectives using a complete series of tools freely available from open sources. APP automates the processing of proteomics tasks such as peptide identification, validation and quantitation from LC-MS/MS data and allows easy integration of many separate proteomics tools. Distributed processing is at the core of APP, allowing the processing of very large datasets using any combination of Windows/Linux physical or virtual computing resources. CONCLUSIONS: APP provides distributed computing nodes that are simple to set up, greatly relieving the need for separate IT competence when handling large datasets. The modular nature of APP allows complex workflows to be managed and distributed, speeding up throughput and setup. Additionally, APP logs execution information on all executed tasks and generated results, simplifying information management and validation.
Keywords: Proteomics; Automation; Validation; Distributed processing
Description: Published: 30 December 2014
Rights: © 2014 Malm et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.
RMID: 0030021057
DOI: 10.1186/s12859-014-0441-8
Appears in Collections:Agriculture, Food and Wine publications

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