Proteomics

Dataset Information

0

Proteomics Tool Spike-in dataset analyzed on a timsTOF Pro


ABSTRACT: Label-free proteomics enables the unbiased quantification of thousands of proteins across large sample cohorts. Commonly used mass spectrometry-based proteomic workflows rely on data dependent acquisition (DDA). However, its stochastic selection of peptide features for fragmentation-based identification inevitably results in high rates of missing values, which prohibits the integration of larger cohorts as the number of recurrently detected peptides is a limiting factor. Peptide identity propagation (PIP) can mitigate this challenge, allowing to transfer sequencing information between samples. However, despite the promise of these approaches, current methods remain limited either in sensitivity or reliability and there is a lack of robust and widely applicable software. Here we prepared a tool spike-in data set which can be used to evaluate PIP on timsTOF Pro data.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Homo Sapiens (human) Escherichia Coli

TISSUE(S): Permanent Cell Line Cell

DISEASE(S): Disease Free

SUBMITTER: Mathias Kalxdorf  

LAB HEAD: Jeroen Krijgsveld

PROVIDER: PXD021425 | Pride | 2021-07-10

REPOSITORIES: Pride

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Publications

IceR improves proteome coverage and data completeness in global and single-cell proteomics.

Kalxdorf Mathias M   Müller Torsten T   Stegle Oliver O   Krijgsveld Jeroen J  

Nature communications 20210809 1


Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates simila  ...[more]

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