Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 497

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The detection, correlation, and comparison of peptide precursor and product ions from data independent LC‐MS with data dependant LC‐MS/MS

Geromanos, Scott J., Vissers, Johannes P. C., Silva, Jeffrey C., Dorschel, Craig A., Li, Guo‐Zhong, Gorenstein, Marc V., Bateman, Robert H., Langridge, James I.·2009·PROTEOMICS

Abstract

The detection, correlation, and comparison of peptide and product ions from a data independent LC‐MS acquisition strategy with data dependent LC‐MS/MS is described. The data independent mode of acquisition differs from an LC‐MS/MS data acquisition since no ion transmission window is applied with the first mass analyzer prior to collision induced disassociation. Alternating the energy applied to the collision cell, between low and elevated energy, on a scan‐to‐scan basis, provides accurate mass precursor and associated product ion spectra from every ion above the LOD of the mass spectrometer. The method therefore provides a near 100% duty cycle, with an inherent increase in signal intensity due to the fact that both precursor and product ion data are collected on all isotopes of every charge‐state across the entire chromatographic peak width. The correlation of product to precursor ions, after deconvolution, is achieved by using reconstructed retention time apices and chromatographic peak shapes. Presented are the results from the comparison of a simple four protein mixture, in the presence and absence of an enzymatically digested protein extract from Escherichia coli. The samples were run in triplicate by both data dependant analysis (DDA) LC‐MS/MS and data‐independent, alternate scanning LC‐MS. The detection and identification of precursor and product ions from the combined DDA search results of the four protein mixture were used for comparison to all other data. Each individual set of data‐independent LC‐MS data provides a more comprehensive set of detected ions than the combined peptide identifications from the DDA LC‐MS/MS experiments. In the presence of the complex E. coli background, over 90% of the monoisotopic masses from the combined LC‐MS/MS identifications were detected at the appropriate retention time. Moreover, the fragmentation pattern and number of associated elevated energy product ions in each replicate experiment was found to be very similar to the DDA identifications. In the case of the corresponding individual DDA LC‐MS/MS experiment, 43% of the possible detectable peptides of interest were identified. The presented data illustrates that the time‐aligned data from data‐independent alternate scanning LC‐MS experiments is highly comparable to the data obtained via DDA. The obtained information can therefore be effectively and correctly deconvolved to correlate product ions with parent precursor ions. The ability to generate precursor‐product ion tables from this information and subsequently identify the correct parent precursor peptide will be illustrated in a companion manuscript.

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 · 10.1002/pmic.200800562

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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

Cites

Show all 2 references

Cited by

See all 497

Metadata

Authors
Geromanos, S. J., Vissers, J. P. C., Silva, J. C., Dorschel, C. A., Li, G., Gorenstein, M. V., Bateman, R. H., & Langridge, J. I.
Venue
PROTEOMICS
Publisher
Wiley
Published
2009-03
Volume
9
Issue
6
Pages
1683-1695
ISSN
  • 1615-9853 (ISSN-L)
  • 1615-9861
OpenAlex
W1981536086
Cited by
497
References
2
FWCI
14.61
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: The detection, correlation, and comparison of peptide precursor and product ions from data independent LC‐MS with data dependant LC‐MS/MS. https://app.cochat.ai/papers/10.1002/pmic.200800562
The paper
Geromanos, S. J., Vissers, J. P. C., Silva, J. C., Dorschel, C. A., Li, G., Gorenstein, M. V., Bateman, R. H., & Langridge, J. I. (2009). The detection, correlation, and comparison of peptide precursor and product ions from data independent LC‐MS with data dependant LC‐MS/MS. PROTEOMICS. https://doi.org/10.1002/pmic.200800562

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.