The DOI matches its Crossref record; 284 of the 284 references it cites were found in a registry or an index.
Metabolomics for Plant Improvement: Status and Prospects
Open accessAbstract
Post-genomics era has witnessed the development of cutting-edge technologies that have offered cost-efficient and high-throughput ways for molecular characterization of the function of a cell or organism. Large-scale metabolite profiling assays have allowed researchers to access the global data sets of metabolites and the corresponding metabolic pathways in an unprecedented way. Recent efforts in metabolomics have been directed to improve the quality along with a major focus on yield related traits. Importantly, an integration of metabolomics with other approaches such as quantitative genetics, transcriptomics and genetic modification has established its immense relevance to plant improvement. An effective combination of these modern approaches guides researchers to pinpoint the functional gene(s) and the characterization of massive metabolites, in order to prioritize the candidate genes for downstream analyses and ultimately, offering trait specific markers to improve commercially important traits. This in turn will improve the ability of a plant breeder by allowing him to make more informed decisions. Given this, the present review captures the significant leads gained in the past decade in the field of plant metabolomics accompanied by a brief discussion on the current contribution and the future scope of metabolomics to accelerate plant improvement.
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
MYB transcription factors in Arabidopsis
Dubos, C., Stracke, R., Grotewold, E., Weisshaar, B., Martin, C., & Lepiniec, L. (2010) · Trends in Plant Science · doi:10.1016/j.tplants.2010.06.005 · Cited by 4,865
Understanding plant responses to drought—from genes to the whole plant.
Chaves, M. M., Maroco, J. P., & Pereira, J. S. (2003) · Functional Plant Biology · doi:10.1071/fp02076 · Cited by 3,845
MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis
Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., & Arita, M. (2015) · Nature Methods · doi:10.1038/nmeth.3393 · Cited by 3,397
Bird, A. (2007) · Nature · doi:10.1038/nature05913 · Cited by 3,011
Engineering the Provitamin A (β-Carotene) Biosynthetic Pathway into (Carotenoid-Free) Rice Endosperm
Ye, X., Al-Babili, S., Klöti, A., Zhang, J., Lucca, P., Beyer, P., & Potrykus, I. (2000) · Science · doi:10.1126/science.287.5451.303 · Cited by 2,271
Cytokinin Oxidase Regulates Rice Grain Production
Ashikari, M., Sakakibara, H., Lin, S., Yamamoto, T., Takashi, T., Nishimura, A., Angeles, E. R., Qian, Q., Kitano, H., & Matsuoka, M. (2005) · Science · doi:10.1126/science.1113373 · Cited by 1,905
Legumes: Importance and Constraints to Greater Use
Graham, P. H., & Vance, C. P. (2003) · Plant Physiology · doi:10.1104/pp.017004 · Cited by 1,801
Interactome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771
Apple phytochemicals and their health benefits
Boyer, J., & Liu, R. H. (2004) · Nutrition Journal · doi:10.1186/1475-2891-3-5 · Cited by 1,583
Improving the nutritional value of Golden Rice through increased pro-vitamin A content
Paine, J. A., Shipton, C. A., Chaggar, S., Howells, R. M., Kennedy, M. J., Vernon, G., Wright, S. Y., Hinchliffe, E., Adams, J. L., Silverstone, A. L., et al. (2005) · Nature Biotechnology · doi:10.1038/nbt1082 · Cited by 1,457
Cited by
Hu, L., Liu, J., Zhang, W., Wang, T., Zhang, N., Lee, Y. H., & Lu, H. (2020) · Mass Spectrometry Reviews · doi:10.1002/mas.21611 · Cited by 81
Review: Metabolomics as a Prediction Tool for Plants Performance under Environmental Stress
Villate, A., San Nicolas, M., Gallastegi, M., Aulas, P.-A., Olivares, M., Usobiaga, A., Etxebarria, N., & Aizpurua-Olaizola, O. (2021) · Plant Science · doi:10.1016/j.plantsci.2020.110789 · Cited by 72
Metadata
- Authors
- Kumar, R., Bohra, A., Pandey, A. K., Pandey, M. K., & Kumar, A.
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2017-08-07
- Volume
- 8
- Pages
- 1302-1302
- ISSN
- 1664-462X (ISSN-L)
- OpenAlex
- W2744254595
- Cited by
- 328
- References
- 284
- FWCI
- 13.91
- 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: Metabolomics for Plant Improvement: Status and Prospects. https://app.cochat.ai/papers/10.3389/fpls.2017.01302
- The paper
- Kumar, R., Bohra, A., Pandey, A. K., Pandey, M. K., & Kumar, A. (2017). Metabolomics for Plant Improvement: Status and Prospects. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2017.01302
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.
