The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry
Open accessAbstract
Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of proteomics studies. Here, we introduce an experimental workflow that uses this technique to construct chromatogram libraries that capture fragment ion chromatographic peak shape and retention time for every detectable peptide in a proteomics experiment. These coordinates calibrate protein databases or spectrum libraries to a specific mass spectrometer and chromatography setup, facilitating DIA-only pipelines and the reuse of global resource libraries. We also present EncyclopeDIA, a software tool for generating and searching chromatogram libraries, and demonstrate the performance of our workflow by quantifying proteins in human and yeast cells. We find that by exploiting calibrated retention time and fragmentation specificity in chromatogram libraries, EncyclopeDIA can detect 20–25% more peptides from DIA experiments than with data dependent acquisition-based spectrum libraries alone.
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
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
Density Estimation for Statistics and Data Analysis
Silverman, B. W. (2018) · Routledge eBooks · doi:10.1201/9781315140919 · Cited by 15,796
Density Estimation for Statistics and Data Analysis
(1986) · Springer eBooks · doi:10.1007/978-1-4899-3324-9 · Cited by 8,525
Keller, A., Nesvizhskii, A. I., Kolker, E., & Aebersold, R. (2002) · Analytical Chemistry · doi:10.1021/ac025747h · Cited by 4,830
Skyline: an open source document editor for creating and analyzing targeted proteomics experiments
MacLean, B., Tomazela, D. M., Shulman, N., Chambers, M., Finney, G. L., Frewen, B., Kern, R., Tabb, D. L., Liebler, D. C., & MacCoss, M. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq054 · Cited by 4,697
Self-citationGlobal analysis of protein expression in yeast
Ghaemmaghami, S., Huh, W.-K., Bower, K., Howson, R. W., Belle, A., Dephoure, N., O'Shea, E. K., & Weissman, J. S. (2003) · Nature · doi:10.1038/nature02046 · Cited by 3,750
Gillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877
Large-scale gene function analysis with the PANTHER classification system
Mi, H., Muruganujan, A., Casagrande, J. T., & Thomas, P. D. (2013) · Nature Protocols · doi:10.1038/nprot.2013.092 · Cited by 2,466
G 1 Events and Regulation of Cell Proliferation
Pardee, A. B. (1989) · Science · doi:10.1126/science.2683075 · Cited by 2,182
Cited by
The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight
Garrett-Bakelman, F. E., Darshi, M., Green, S. J., Gur, R. C., Lin, L., Macias, B. R., McKenna, M. J., Meydan, C., Mishra, T., Nasrini, J., et al. (2019) · Science · doi:10.1126/science.aau8650 · Cited by 1,128
Galectin-3 Coordinates a Cellular System for Lysosomal Repair and Removal
Jia, J., Claude-Taupin, A., Gu, Y., Choi, S. W., Peters, R., Bissa, B., Mudd, M. H., Allers, L., Pallikkuth, S., Lidke, K. A., et al. (2020) · Developmental Cell · doi:10.1016/j.devcel.2019.10.025 · Cited by 434
Frankenfield, A. M., Ni, J., Ahmed, M., & Hao, L. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00145 · Cited by 321
A Review on Quantitative Multiplexed Proteomics
Pappireddi, N., Martin, L., & Wühr, M. (2019) · ChemBioChem · doi:10.1002/cbic.201800650 · Cited by 299
Upadhya, R., Madhu, L. N., Attaluri, S., Gitaí, D. L. G., Pinson, M. R., Kodali, M., Shetty, G., Zanirati, G., Kumar, S., Shuai, B., et al. (2020) · Journal of Extracellular Vesicles · doi:10.1080/20013078.2020.1809064 · Cited by 180
Metadata
- Authors
- Searle, B. C., Pino, L. K., Egertson, J. D., Ting, Y. S., Lawrence, R. T., MacLean, B. X., Villén, J., & MacCoss, M. J.
- Venue
- Nature Communications
- Published
- 2018-12-03
- Volume
- 9
- Issue
- 1
- Pages
- 5128-5128
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2903074009
- Cited by
- 522
- References
- 46
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- gold
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: Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. https://app.cochat.ai/papers/10.1038/s41467-018-07454-w
- The paper
- Searle, B. C., Pino, L. K., Egertson, J. D., Ting, Y. S., Lawrence, R. T., MacLean, B. X., Villén, J., & MacCoss, M. J. (2018). Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nature Communications. https://doi.org/10.1038/s41467-018-07454-w
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.