The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
PepNovo: De Novo Peptide Sequencing via Probabilistic Network Modeling
Abstract
We present a novel scoring method for de novo interpretation of peptides from tandem mass spectrometry data. Our scoring method uses a probabilistic network whose structure reflects the chemical and physical rules that govern the peptide fragmentation. We use a likelihood ratio hypothesis test to determine whether the peaks observed in the mass spectrum are more likely to have been produced under our fragmentation model than under a model that treats peaks as random events. We tested our de novo algorithm PepNovo on ion trap data and achieved results that are superior to popular de novo peptide sequencing algorithms. PepNovo can be accessed via the URL http://www-cse.ucsd.edu/groups/bioinformatics/software.html.
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.
Integrity signals
Registry
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.
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
Large-scale analysis of the yeast proteome by multidimensional protein identification technology
Washburn, M. P., Wolters, D., & Yates, J. R. (2001) · Nature Biotechnology · doi:10.1038/85686 · Cited by 4,752
Bayesian Networks and Decision Graphs
Jensen, F. V. (2001) · Springer eBooks · doi:10.1007/978-1-4757-3502-4 · Cited by 1,734
Error-Tolerant Identification of Peptides in Sequence Databases by Peptide Sequence Tags
Mann, M., & Wilm, M. (1994) · Analytical Chemistry · doi:10.1021/ac00096a002 · Cited by 1,468
Mobile and localized protons: a framework for understanding peptide dissociation
Wysocki, V. H., Tsaprailis, G., Smith, L. L., & Breci, L. A. (2000) · Journal of Mass Spectrometry · doi:10.1002/1096-9888(200012)35:12<1399::aid-jms86>3.0.co;2-r · Cited by 997
Proteomics: the first decade and beyond
Patterson, S. D., & Aebersold, R. H. (2003) · Nature Genetics · doi:10.1038/ng1106 · Cited by 745
De Novo Peptide Sequencing via Tandem Mass Spectrometry
Dančík, V., Addona, T. A., Clauser, K. R., Vath, J. E., & Pevzner, P. A. (1999) · Journal of Computational Biology · doi:10.1089/106652799318300 · Cited by 595
Self-citationPathBLAST: a tool for alignment of protein interaction networks
Kelley, B. P., Yuan, B., Lewitter, F., Sharan, R., Stockwell, B. R., & Ideker, T. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh411 · Cited by 420
Sequence database searches viade novo peptide sequencing by tandem mass spectrometry
Taylor, J. A., & Johnson, R. S. (1997) · Rapid Communications in Mass Spectrometry · doi:10.1002/(sici)1097-0231(19970615)11:9<1067::aid-rcm953>3.0.co;2-l · Cited by 414
Intensity-based protein identification by machine learning from a library of tandem mass spectra
Elias, J. E., Gibbons, F. D., King, O. D., Roth, F. P., & Gygi, S. P. (2004) · Nature Biotechnology · doi:10.1038/nbt930 · Cited by 304
Experimental Protein Mixture for Validating Tandem Mass Spectral Analysis
Keller, A., Purvine, S., Nesvizhskii, A. I., Stolyar, S., Goodlett, D. R., & Kolker, E. (2002) · OMICS: A Journal of Integrative Biology · doi:10.1089/153623102760092805 · Cited by 261
Cited by
Protein Analysis by Shotgun/Bottom-up Proteomics
Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C., & Yates, J. R. (2013) · Chemical Reviews · doi:10.1021/cr3003533 · Cited by 1,491
InsPecT: Identification of Posttranslationally Modified Peptides from Tandem Mass Spectra
Tanner, S., Shu, H., Frank, A., Wang, L.-C., Zandi, E., Mumby, M., Pevzner, P. A., & Bafna, V. (2005) · Analytical Chemistry · doi:10.1021/ac050102d · Cited by 567
Self-citationProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity
Xu, T., Park, S. K., Venable, J. D., Wohlschlegel, J. A., Diedrich, J. K., Cociorva, D., Lu, B., Liao, L., Hewel, J., Han, X., et al. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.07.001 · Cited by 511
Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
Chen, C., Hou, J., Tanner, J. J., & Cheng, J. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082873 · Cited by 280
JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy
Wang, X., Li, Y., Wu, Z., Wang, H., Tan, H., & Peng, J. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.039586 · Cited by 194
Metadata
- Authors
- Frank, A., & Pevzner, P.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2005-01-13
- Volume
- 77
- Issue
- 4
- Pages
- 964-973
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2038615001
- Cited by
- 688
- References
- 16
- FWCI
- 18.51
- 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: PepNovo: De Novo Peptide Sequencing via Probabilistic Network Modeling. https://app.cochat.ai/papers/10.1021/ac048788h
- The paper
- Frank, A., & Pevzner, P. (2005). PepNovo: De Novo Peptide Sequencing via Probabilistic Network Modeling. Analytical Chemistry. https://doi.org/10.1021/ac048788h
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.
