Proteomics

Dataset Information

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InterBatch Benchmarking Experiment for Isobaric Proteomics


ABSTRACT: We developed a unique multi-batch benchmarking dataset. The design is based on a standard two-species dilution model3,18,19 with mouse plasma used to create a background of potentially interfering peptides at 1:1 ratios while yeast cultures are mixed across a range of known ratios. For the purposes of investigating how best to combine isobaric batches the experiment is designed with two main goals. First, we needed multiple batches of data containing a wide range of known changes, with some large enough to test the dynamic range of our instrument, and others small enough to probe our capacity for detecting small perturbations. Second, we need a wide variety of batch compositions to better reflect the full set of patterns that we might observe when studying a random assortment of genetically diverse samples. To this end, yeast proteomes were diluted at eleven different levels of known changes with a maximum dilution of 1/32 by the use of an automated liquid handler. To generate a diversity of batch compositions across the proteome, we cultured yeast in various carbon and nitrogen source combinations known to substantially alter the yeast proteome20. Both media groups and dilution levels were randomly assigned throughout six batches of TMT-labeled samples

INSTRUMENT(S): Orbitrap Eclipse

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast) Mus Musculus (mouse)

TISSUE(S): Blood Plasma

SUBMITTER: Jonathon O'Brien  

LAB HEAD: Fiona McAllister

PROVIDER: PXD036799 | Pride | 2024-03-18

REPOSITORIES: Pride

Dataset's files

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Publications

A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.

O'Brien Jonathon J JJ   Raj Anil A   Gaun Aleksandr A   Waite Adam A   Li Wenzhou W   Hendrickson David G DG   Olsson Niclas N   McAllister Fiona E FE  

Nature methods 20231218 2


We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across  ...[more]

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