Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 221

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

De novo sequencing of peptides by MS/MS

Seidler, Joerg, Zinn, Nico, Boehm, Martin E., Lehmann, Wolf D.·2010·PROTEOMICS

Abstract

The current status of de novo sequencing of peptides by MS/MS is reviewed with focus on collision cell MS/MS spectra. The relation between peptide structure and observed fragment ion series is discussed and the exhaustive extraction of sequence information from CID spectra of protonated peptide ions is described. The partial redundancy of the extracted sequence information and a high mass accuracy are recognized as key parameters for dependable de novo sequencing by MS. In addition, the benefits of special techniques enhancing the generation of long uninterrupted fragment ion series for de novo peptide sequencing are highlighted. Among these are terminal 18 O labeling, MS n of sodiated peptide ions, N‐terminal derivatization, the use of special proteases, and time‐delayed fragmentation. The emerging electron transfer dissociation technique and the recent progress of MALDI techniques for intact protein sequencing are covered. Finally, the integration of bioinformatic tools into peptide de novo sequencing is demonstrated.

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.200900459

Cites

5 of 5 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 5 references

Cited by

See all 221

Metadata

Authors
Seidler, J., Zinn, N., Boehm, M. E., & Lehmann, W. D.
Venue
PROTEOMICS
Publisher
Wiley
Published
2010-02
Volume
10
Issue
4
Pages
634-649
ISSN
  • 1615-9853 (ISSN-L)
  • 1615-9861
OpenAlex
W2073649272
Cited by
221
References
5
FWCI
6.22
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: De novo sequencing of peptides by MS/MS. https://app.cochat.ai/papers/10.1002/pmic.200900459
The paper
Seidler, J., Zinn, N., Boehm, M. E., & Lehmann, W. D. (2010). De novo sequencing of peptides by MS/MS. PROTEOMICS. https://doi.org/10.1002/pmic.200900459

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.