Brian Searle
Affiliations: | 2014-2018 | Genome Sciences | University of Washington, Seattle, Seattle, WA |
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Sign in to add trainee| Teeradon Phlairaharn | grad student | 2024- | The Ohio State University College of Medicine |
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Publications
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| Meyer AC, McIlvin MR, Lopez P, et al. (2025) Proteomic profiling of zinc homeostasis mechanisms in Pseudomonas aeruginosa through data-dependent and data-independent acquisition mass spectrometry. Biorxiv : the Preprint Server For Biology |
| Rajczewski AT, Blakeley-Ruiz JA, Meyer A, et al. (2024) Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome. Biorxiv : the Preprint Server For Biology |
| Ghosh G, Shannon AE, Searle BC. (2024) Data acquisition approaches for single cell proteomics. Proteomics. e2400022 |
| Searle B, Shannon A, Teodorescu R, et al. (2024) Rapid assay development for low input targeted proteomics using a versatile linear ion trap. Research Square |
| Shannon AE, Teodorescu RN, Soon N, et al. (2024) A workflow for targeted proteomics assay development using a versatile linear ion trap. Biorxiv : the Preprint Server For Biology |
| Heil LR, Damoc E, Arrey TN, et al. (2023) Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition. Journal of Proteome Research |
| Kirkpatrick J, Stemmer PM, Searle BC, et al. (2023) 2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Proteomics Study. Journal of Biomolecular Techniques : Jbt. 34 |
| Allen C, Meinl R, Paez JS, et al. (2023) nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows. Journal of Proteome Research |
| Wilburn DB, Shannon AE, Spicer V, et al. (2023) Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications. Biorxiv : the Preprint Server For Biology |
| Heil LR, Damoc E, Arrey TN, et al. (2023) Evaluating the performance of the Astral mass analyzer for quantitative proteomics using data independent acquisition. Biorxiv : the Preprint Server For Biology |