Abstract
In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow, thereby preventing valuable information from being discarded in the identification stage. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. This reduces search time due to the data reduction. We can now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Quandenser+Triqler outperforms the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins for all tested datasets. Software is available for all major operating systems at https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.
Highlights
In shotgun proteomics, the analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data
We note that label-free quantification (LFQ) is sometimes seen as cumbersome, as contrary to, for instance, isobaric labeling, one is not guaranteed a readout for an identified peptide in each sample
We compared these quantificationfirst setups to three identification-first setups: (1) MaxQuant + Perseus with MBR9,25, (2) MaxQuant + empirical Bayesian random censoring threshold model (EBRCT) with MBR9,26, and (3) Tide followed by Triqler, without clustering on MS1 and MS2 levels nor MBR but with feature detection using Dinosaur[27]
Summary
The analysis of label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data This generally causes a low sensitivity in differential expression analysis. We introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities This reduces search time due to the data reduction. LFQ and quantitative proteomics in general are struggling to obtain sufficient coverage of the proteome[2] and suffer from low sensitivity for differentially abundant proteins at false discovery rate thresholds[3] While this can partially be attributed to inherent limitations in the methodology of mass spectrometry, it is, to a high degree, caused by the inadequacy of our current data analysis pipelines. Clustering of fragment spectra was recently demonstrated to give better sensitivity to LFQ experiments[15]
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