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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-31214
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dc.contributor.authorFröhlich, Klemens-
dc.contributor.authorBrombacher, Eva-
dc.contributor.authorFahrner, Matthias-
dc.contributor.authorVogele, Daniel-
dc.contributor.authorKook, Lucas-
dc.contributor.authorPinter, Niko-
dc.contributor.authorBronsert, Peter-
dc.contributor.authorTimme-Bronsert, Sylvia-
dc.contributor.authorSchmidt, Alexander-
dc.contributor.authorBärenfaller, Katja-
dc.contributor.authorKreutz, Clemens-
dc.contributor.authorSchilling, Oliver-
dc.date.accessioned2024-08-02T07:29:22Z-
dc.date.available2024-08-02T07:29:22Z-
dc.date.issued2022-05-12-
dc.identifier.issn2041-1723de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/31214-
dc.description.abstractNumerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.de_CH
dc.language.isoende_CH
dc.publisherNature Publishing Groupde_CH
dc.relation.ispartofNature Communicationsde_CH
dc.rightshttps://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectHumande_CH
dc.subjectProteomede_CH
dc.subjectSoftwarede_CH
dc.subjectWorkflowde_CH
dc.subjectBenchmarkingde_CH
dc.subjectProteomicsde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleBenchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneityde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1038/s41467-022-30094-0de_CH
dc.identifier.doi10.21256/zhaw-31214-
dc.identifier.pmid35551187de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start2622de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume13de_CH
zhaw.publication.reviewOpen peer reviewde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Fröhlich, K., Brombacher, E., Fahrner, M., Vogele, D., Kook, L., Pinter, N., Bronsert, P., Timme-Bronsert, S., Schmidt, A., Bärenfaller, K., Kreutz, C., & Schilling, O. (2022). Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nature Communications, 13(1), 2622. https://doi.org/10.1038/s41467-022-30094-0
Fröhlich, K. et al. (2022) ‘Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity’, Nature Communications, 13(1), p. 2622. Available at: https://doi.org/10.1038/s41467-022-30094-0.
K. Fröhlich et al., “Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity,” Nature Communications, vol. 13, no. 1, p. 2622, May 2022, doi: 10.1038/s41467-022-30094-0.
FRÖHLICH, Klemens, Eva BROMBACHER, Matthias FAHRNER, Daniel VOGELE, Lucas KOOK, Niko PINTER, Peter BRONSERT, Sylvia TIMME-BRONSERT, Alexander SCHMIDT, Katja BÄRENFALLER, Clemens KREUTZ und Oliver SCHILLING, 2022. Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nature Communications. 12 Mai 2022. Bd. 13, Nr. 1, S. 2622. DOI 10.1038/s41467-022-30094-0
Fröhlich, Klemens, Eva Brombacher, Matthias Fahrner, Daniel Vogele, Lucas Kook, Niko Pinter, Peter Bronsert, et al. 2022. “Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity.” Nature Communications 13 (1): 2622. https://doi.org/10.1038/s41467-022-30094-0.
Fröhlich, Klemens, et al. “Benchmarking of Analysis Strategies for Data-Independent Acquisition Proteomics Using a Large-Scale Dataset Comprising Inter-Patient Heterogeneity.” Nature Communications, vol. 13, no. 1, May 2022, p. 2622, https://doi.org/10.1038/s41467-022-30094-0.


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