MassIVE MSV000087859

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Democratizing Data-Independent Acquisition Proteomics Analysis on Public Cloud Infrastructures Via The Galaxy Framework

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Description

Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging due to the high complexity and large data and sample size, which require specialized software and large computing infrastructures. Most available open-source DIA software necessitate basic programming skills and cover only a fraction of the analysis steps, often yielding a complex of multiple software tools, severely limiting usability and reproducibility. To overcome this hurdle, we have integrated a suite of DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their usage on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate usability of the all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided, to further increase access for the proteomics community. Here we provide the five representative E.coli:HEK ratio data-dependent acquisition measurements, that were used for the generation of a spectral library (for more details see https://zenodo.org/record/4293493#.YPcm6-gzat8). Furthermore, we provide the twenty DIA measurements of the different E.coli:HEK ratio samples (for more details see https://zenodo.org/record/4307762#.YPcnKOgzat8). [doi:10.25345/C51G0N] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: DIA ; Proteomics ; Galaxy ; Mass spectrometry ; Computational workflows

Contact

Principal Investigators:
(in alphabetical order) 		Oliver Schilling, University of Freiburg, N/A
Submitting User: MatthiasF

Publications

Fahrner M, Föll MC, Grüning BA, Bernt M, Röst H, Schilling O.
Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework.
Gigascience.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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