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ARS Home » Pacific West Area » Pullman, Washington » WHGQ » People & Locations » Xianran Li
Xianran Li
Wheat Health, Genetics, and Quality Research
Research Biologist

Phone: (509) 335-3620
Fax:
Room 281

WASHINGTON STATE UNIV
281 Clark Hall
PULLMAN, WA 99164

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Projects
Wheat and Barley Adaptation to a Changing Climate - Discovery of Genetic and Physiological Processes for Improved Crop Productivity and Quality
In-House Appropriated (D)
  Accession Number: 445231
Identifying the Underlying Biochemical Mechanisms that Lead to Low Falling Number in Wheat
Non-Assistance Cooperative Agreement (S)
  Accession Number: 440218

Publications (Clicking on the reprint icon Reprint Icon will take you to the publication reprint.)
Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes Reprint Icon - (Peer Reviewed Journal)
Zhang, B., Huang, H., Tibbs, L.E., Zhang, Z., Sanguinet, K., Vanous, A.E., Garland Campbell, K.A., Yu, J., Li, X. 2023. Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes. Molecular Plant. 16(6):975-978. https://doi.org/10.1016/j.molp.2023.05.005.
An Independent Validation Reveals the Potential to Predict Hagberg-Perten Falling Number Using Spectrometers Reprint Icon - (Peer Reviewed Journal)
Chen, C., Hu, Y., Li, X., Cannon, A., Morris, C., Delwiche, S.R., Steber, C.M., Zhang, Z. 2023. An Independent Validation Reveals the Potential to Predict Hagberg-Perten Falling Number Using Spectrometers. The Plant Phenome Journal. 2023:6(1). https://doi.org/10.1002/ppj2.20070.
Generation of sesame mutant population by mutagenesis and identification of high oleate mutants by GC analysis Reprint Icon - (Peer Reviewed Journal)
Wang, M.L., Tonnis, B.D., Li, X., Morris, J.B. 2023. Generation of sesame mutant population by mutagenesis and identification of high oleate mutants by GC analysis. Plants. 12(6). https://doi.org/10.3390/plants12061294.
Machine learning for predicting phenotype from genotype and environment Reprint Icon - (Peer Reviewed Journal)
Guo, T., Li, X. 2023. Machine learning for predicting phenotype from genotype and environment. Current Opinion in Biotechnology. 79. Article 102853. https://doi.org/10.1016/j.copbio.2022.102853.
Genomic prediction of tocochromanols in exotic-derived maize Reprint Icon - (Peer Reviewed Journal)
Tibbs-Cortes, L.E., Guo, T., Li, X., Tanaka, R., Vanous, A.E., Peters, D.W., Gardner, C.A., Magallanes-Lundback, M., Deason, N.T., DellaPenna, D., Gore, M.A., Yu, J. 2022. Genomic prediction of tocochromanols in exotic-derived maize. The Plant Genome. Article e20286. https://doi.org/10.1002/tpg2.20286.
Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain Reprint Icon - (Peer Reviewed Journal)
Tanaka, R., Wu, D., Li, X., Tibbs-Cortes, L.E., Wood, J., Magallanes-Lundback, M., Bornowski, N., Hamilton, J.P., Vaillancourt, B., Li, X., Deason, N.T., Schoenbaum, G.R., Buell, C.R., DellaPenna, D., Yu, J., Gore, M.A. 2022. Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain. The Plant Genome. Article e20276. https://doi.org/10.1002/tpg2.20276.
Evaluation of variability in seed coat color, weight, oil content, and fatty acid composition within the entire USDA-cultivated peanut germplasm collection Reprint Icon - (Peer Reviewed Journal)
Wang, M.L., Tonnis, B.D., Chen, C., Li, X., Pinnow, D.L., Tallury, S.P., Stigura, N.E., Pederson, G.A., Harrison, M.L. 2022. Evaluation of variability in seed coat color, weight, oil content, and fatty acid composition within the entire USDA-cultivated peanut germplasm collection. Crop Science. 62:2332-2346. https://doi.org/10.1002/csc2.20830.
Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain Reprint Icon - (Peer Reviewed Journal)
Wu, D., Li, X., Tanaka, R., Wood, J., Tibbs-Cortes, L., Magallanes-Lundback, M., Bornowski, N., Hamilton, J., Vaillancourt, B., Diepenbrock, C., Li, X., Deason, N., Schoenbaum, G., Yu, J., Buell, R., Dellapenna, D., Gore, M. 2022. Combining GWAS and TWAS to identify candidate causal genes for tocochromanol levels in maize grain. Genetics. 221(4). Article iyac091. https://doi.org/10.1093/genetics/iyac091.
Unraveling the sorghum domestication Reprint Icon - (Literature Review)
Li, X., Yu, J. 2022. Unraveling the sorghum domestication. Molecular Plant. 15:1-2. https://doi.org/10.1016/j.molp.2022.03.006.
Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range Reprint Icon - (Peer Reviewed Journal)
Mu, Q., Guo, T., Li, X., Yu, J. 2022. Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range. New Phytologist. 233(4):1768-1779. https://doi.org/10.1111/nph.17904.
Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions Reprint Icon - (Peer Reviewed Journal)
Li, X., Guo, T., Bai, G., Zhang, Z., See, D.R., Marshall, J., Garland Campbell, K.A., Yu, J. 2022. Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions. Molecular Plant. 15(2):203-206. https://doi.org/10.1016/j.molp.2022.01.001.
High-efficiency plastome base editing in rice with TAL cytosine deaminase Reprint Icon - (Peer Reviewed Journal)
Li, R., Char, S., Liu, B., Liu, H., Li, X., Yang, B. 2021. High-efficiency plastome base editing in rice with TAL cytosine deaminase. Molecular Plant. 14(9):1412-1414. https://doi.org/10.1016/j.molp.2021.07.007.
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