Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 390

The DOI matches its Crossref record.

Analysis of Quantitative Proteomic Data Generated via Multidimensional Protein Identification Technology

Washburn, Michael P., Ulaszek, Ryan, Deciu, Cosmin, Schieltz, David M., Yates, John R.·2002·Analytical Chemistry

Abstract

We describe the analysis of quantitative proteomic samples via multidimensional protein identification technology (MudPIT). Ratio amounts of the soluble portion of the S. cerevisiae proteome from cultures of S. cerevisiae strain S288C grown in either 14N minimal media or 15N-enriched minimal media were mixed and digested into a complex peptide mixture. A 1 x 14N/1 x 15N complex peptide mixture was analyzed by single-dimensional reversed-phase chromatography and electrospray ionization quadrapole time-of-flight mass spectrometry in order to demonstrate the replacement of 14N by 15N under the growth conditions used. After conformation of the incorporation of 15N into the labeled sample, three separate samples consisting of a 1 x 14N/1 x 15N complex peptide mixture, a 5 x 14N/1 x 15N complex peptide mixture, and a 10 x 14N/1 x 15N complex peptide mixture were analyzed via MudPIT. We demonstrate the dynamic range of the system by analyzing a 1:1, 5:1, and 10:1 data set using the soluble portion from S. cerevisiae grown in either 14N or 15N-enriched minimal media. The method described provides an accurate way to undertake a large-scale quantitative proteomic study.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/ac015704l

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 390

Metadata

Authors
Washburn, M. P., Ulaszek, R., Deciu, C., Schieltz, D. M., & Yates, J. R.
Venue
Analytical Chemistry
Published
2002-03-05
Volume
74
Issue
7
Pages
1650-1657
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
OpenAlex
W2088392539
Cited by
390
References
0
FWCI
26.10
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Analysis of Quantitative Proteomic Data Generated via Multidimensional Protein Identification Technology. https://app.cochat.ai/papers/10.1021/ac015704l
The paper
Washburn, M. P., Ulaszek, R., Deciu, C., Schieltz, D. M., & Yates, J. R. (2002). Analysis of Quantitative Proteomic Data Generated via Multidimensional Protein Identification Technology. Analytical Chemistry. https://doi.org/10.1021/ac015704l

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.