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):

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