ReviewFrontiers in plant science2026
Data-independent acquisition-based quantitative proteomics in plants.
Review in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-independent acquisition (DIA)-based proteomics is increasingly becoming a standard procedure for comprehensive protein analysis in plant systems. This high-throughput approach is now extensively employed to characterize protein dynamics across various plant physiological processes including growth, development, and stress responses. Particularly in plant science, this approach facilitates the discovery of novel disease resistance proteins and regulatory pathways involved in plant defense mechanisms. Continuing advancements in DIA proteomics methodologies bolster its sensitivity, reproducibility, and throughput, thus broadening its utility in botanical research. This review offers a comprehensive overview of the myriad applications of DIA proteomics in elucidating biological processes and molecular mechanisms with a primary focus on plants. Ultimately, it underscores the pivotal role of DIA proteomics in advancing our understanding of plant biological systems, while also discussing future directions and challenges in the field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.