BASIS

• Verfasst von: Veselkov, Kirill [VerfasserIn]
• Verfasst von: Sleeman, Jonathan P. [VerfasserIn]
• Titel: BASIS
• Titelzusatz: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology
• Verf.angabe: Kirill Veselkov, Jonathan Sleeman, Emmanuelle Claude, Johannes P. C. Vissers, Dieter Galea, Anna Mroz, Ivan Laponogov, Mark Towers, Robert Tonge, Reza Mirnezami, Zoltan Takats, Jeremy K. Nicholson and James I. Langridge
• E-Jahr: 2018
• Jahr: 06 March 2018
• Umfang: 11 S.
• Fussnoten: Gesehen am 22.07.2020
• Titel Quelle: Enthalten in: Scientific reports
• Ort Quelle: [London] : Springer Nature, 2011
• Jahr Quelle: 2018
• Band/Heft Quelle: 8(2018) Artikel-Nummer 4053, 11 Seiten
• ISSN Quelle: 2045-2322
• DOI: doi:10.1038/s41598-018-22499-z
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1725301172

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.