Dataset Information

A semi-automated workflow for DIA-based global discovery to pathway-driven PRM analysis

ABSTRACT: Initial discovery-based analysis using data independent acquisition (DIA) can obtain deep proteome coverage with high data completeness; however, the development of targeted PRM assays based on subsequent bioinformatic predictions can be tedious and time-consuming because of the complexity of the output. We address this limitation with a Python script that rapidly generates a PRM method for the TIMS-QTOF platform using DIA data and a user-defined target list.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Stanley Stevens

LAB HEAD: Stanley M. Stevens, Ph.D.

PROVIDER: PXD049405 | Pride | 2024-09-06

REPOSITORIES: pride

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Dataset's files

Source:

			Action	DRS
	200ngHeLa_DIA_45m_5_Slot1-2_10-31-2023_7460.d.zip	Other
	200ngHeLa_DIA_45m_6_Slot1-2_10-31-2023_7462.d.zip	Other
	200ngHeLa_DIA_45m_7_Slot1-2_10-31-2023_7464.d.zip	Other
	200ngHeLa_top2000_apop_PRM_45m_5_Slot1-2_11-1-2023_7477.d.zip	Other
	200ngHeLa_top2000_apop_PRM_45m_6_Slot1-2_11-1-2023_7479.d.zip	Other

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Publications

A semi-automated workflow for DIA-based global discovery to pathway-driven PRM analysis.

Guergues Jennifer J Wohlfahrt Jessica J Koomen John M JM Krieger Jonathan R JR Varma Sameer S Stevens Stanley M SM

Proteomics 20240905 3

Targeted proteomics, which includes parallel reaction monitoring (PRM), is typically utilized for more precise detection and quantitation of key proteins and/or pathways derived from complex discovery proteomics datasets. Initial discovery-based analysis using data independent acquisition (DIA) can obtain deep proteome coverage with low data missingness while targeted PRM assays can provide additional benefits in further eliminating missing data and optimizing measurement precision. However, PRM ...[more]

PMID: 39235396

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