Dataset Information

MS-DAP platform for label-free proteomics uncovers optimal workflows in benchmark datasets and increased sensitivity in analysis of Alzheimer's biomarker data

ABSTRACT: In the rapidly moving proteomics field, a diverse patchwork of algorithms for data normalization and differential expression analysis is used by the community. We generated an all-inclusive mass spectrometry downstream analysis pipeline (MS-DAP) that integrates many algorithms for normalization and statistical analyses and produces standardized quality reporting with extensive data visualizations. Second, systematic evaluation of normalization and statistical algorithms on various benchmarking datasets, including additional data generated in this study, suggest best-practices for data analysis. Commonly used approaches for differential testing based on moderated t-statistics are consistently outperformed by more recent statistical models, all integrated in MS-DAP, and we encourage their adoption. Third, we introduced a novel normalization algorithm that rescues deficiencies observed in commonly used normalization methods. Finally, we used the MS-DAP platform to re-analyze a recently published large-scale proteomics dataset of CSF from AD patients. This revealed increased sensitivity, resulting in additional significant target proteins which improved overlap with results reported in related studies and includes a large set of new potential AD biomarkers in addition to previously reported.

INSTRUMENT(S): timsTOF Pro 2

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast)

TISSUE(S): Hela Cell

SUBMITTER: Frank Koopmans

LAB HEAD: August B. Smit

PROVIDER: PXD036134 | Pride | 2023-03-10

REPOSITORIES: Pride

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Publications

MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer's Biomarker Data.

Koopmans Frank F Li Ka Wan KW Klaassen Remco V RV Smit August B AB

Journal of proteome research 20221221 2

In the rapidly moving proteomics field, a diverse patchwork of data analysis pipelines and algorithms for data normalization and differential expression analysis is used by the community. We generated a mass spectrometry downstream analysis pipeline (MS-DAP) that integrates both popular and recently developed algorithms for normalization and statistical analyses. Additional algorithms can be easily added in the future as plugins. MS-DAP is open-source and facilitates transparent and reproducible ...[more]

PMID: 36541440

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Project description:Environmental conditions are quite eﬀective during propagation and industrial application of Baker’s yeast (Saccharomyces cerevisiae). The change of temperature is one of the most important conditions which aﬀects the dough-leaving capacity of yeast cells. Accordingly, heat changes play an essential role in the commercial and economic impact of the yeast. Recent technologies in genomics have been used for analysis of global gene expression proﬁles such as microarray and RNA sequencing to solve the problems related with industrial application of yeast. Hereby, the aim of this study is to explore the eﬀects of heat stresses on global gene expression proﬁles and to identify the candidate genes for the heat stress response in commercial baker’s yeast by using microarray technology and comparative statistical data analyses. Data from all hybridizations and array normalization were analyzed using the GeneSpringGX 12.1 (Agilent) and the R 2.15.2 program language. In the analysis of this dataset, all required statistical methods are performed comparatively in each step and the best performed ones are used in further computations. Hence, as the ﬁrst step of the analyses, diﬀerent background normalization algorithms such as MAS5.0, RMA, MBEI and GC-RMA were applied and the algorithm which gives the most accurate ﬁndings was chosen for the normalization procedure. As a result, expression values, computed from CEL ﬁles, were processed by Robust Multiarray Analysis (RMA) which is the selected procedure for the normalization of the systematic diﬀerences between samples. In order to determine diﬀerentially expressed genes under heat stress treatments, two diﬀerent methods, namely, the fold-change and the hypothesis testing approaches are executed. In the fold-change method, various cut-oﬀ values are implemented while diﬀerent multiple testing procedures are performed for the hypothesis testing. Under the heat shock and temperature-shift stress conditions, up/down regulated diﬀerentially expressed probes were functionally categorized into the diﬀerent groups via the cluster analyses. Transcriptome changes under the heat shock and temperature-shift stress treatments show that the number of diﬀerentially up-regulated genes among the heat shock proteins (HSPs) and transcription factors (TFs) changed signiﬁcantly. Consequently, the identiﬁcation of thousands of genes related with heat stress treatments via microarray technology and comparative statistical analysis resulted in the creation of big picture which provides the understanding of the importance for the baker’s yeast industrial applications.

Dataset Information

MS-DAP platform for label-free proteomics uncovers optimal workflows in benchmark datasets and increased sensitivity in analysis of Alzheimer's biomarker data

Publications

MS-DAP Platform for Downstream Data Analysis of Label-Free Proteomics Uncovers Optimal Workflows in Benchmark Data Sets and Increased Sensitivity in Analysis of Alzheimer's Biomarker Data.

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