The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries
Open accessAbstract
Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.
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
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Eng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484
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-citationA cross-platform toolkit for mass spectrometry and proteomics
Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L., Neumann, S., Gatto, L., Fischer, B., Pratt, B., Egertson, J., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2377 · Cited by 4,093
Self-citationGillet, 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
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2007) · Nature Methods · doi:10.1038/nmeth1113 · Cited by 2,424
Self-citationMass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334
Peterson, A. C., Russell, J. D., Bailey, D. J., Westphall, M. S., & Coon, J. J. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o112.020131 · Cited by 1,288
Bruderer, R., Bernhardt, O. M., Gandhi, T., Miladinović, S. M., Cheng, L.-Y., Messner, S., Ehrenberger, T., Zanotelli, V., Butscheid, Y., Escher, C., et al. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.044305 · Cited by 1,238
Cited by
Frankenfield, A. M., Ni, J., Ahmed, M., & Hao, L. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00145 · Cited by 321
Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform
Yu, F., Teo, G. C., Kong, A. T., Fröhlich, K., Li, G. X., Demichev, V., & Nesvizhskii, A. I. (2023) · Nature Communications · doi:10.1038/s41467-023-39869-5 · Cited by 292
Proteotoxic stress response drives T cell exhaustion and immune evasion
Wang, Y., Ma, A., Song, N.-J., Shannon, A. E., Amankwah, Y. S., Chen, X., Wu, W., Wang, Z., Saadey, A. A., Yousif, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09539-1 · Cited by 68
Self-citationThe regulatory landscape of the yeast phosphoproteome
Leutert, M., Barente, A. S., Fukuda, N. K., Rodriguez-Mias, R. A., & Villén, J. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01115-3 · Cited by 52
Skowronek, P., Wallmann, G., Wahle, M., Willems, S., & Mann, M. (2025) · Nature Protocols · doi:10.1038/s41596-024-01104-w · Cited by 43
Metadata
- Authors
- Pino, L. K., Just, S. C., MacCoss, M. J., & Searle, B. C.
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 19
- Issue
- 7
- Pages
- 1088-1103
- ISSN
- 1535-9476 (ISSN-L)
- OpenAlex
- W3016694036
- Cited by
- 284
- References
- 60
- FWCI
- 11.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries. https://app.cochat.ai/papers/10.1074/mcp.p119.001913
- The paper
- Pino, L. K., Just, S. C., MacCoss, M. J., & Searle, B. C. (2020). Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries. Molecular & Cellular Proteomics. https://doi.org/10.1074/mcp.p119.001913
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.
