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BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology

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Title: BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology
Authors: Veselkov, KA
Sleeman, J
Claude, E
Vissers, J
Galea, D
Mroz, A
Laponogov, I
Towers, M
Tonge, R
Mirnezami, R
Takats, Z
Nicholson, J
Langridge, J
Item Type: Journal Article
Abstract: Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI.
Issue Date: 6-Mar-2018
Date of Acceptance: 23-Feb-2018
URI: http://hdl.handle.net/10044/1/57529
DOI: https://dx.doi.org/10.1038/s41598-018-22499-z
ISSN: 2045-2322
Publisher: Nature Publishing Group
Journal / Book Title: Scientific Reports
Volume: 8
Copyright Statement: © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Sponsor/Funder: Commission of the European Communities
Biotechnology and Biological Sciences Research Council (BBSRC)
Commission of the European Communities
Funder's Grant Number: 305259
BB/L020858/1
634402
Article Number: ARTN 4053
Appears in Collections:Division of Surgery
Faculty of Medicine



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