2022 Publications

Adams, S., & Carré, I. A. (2021). Chromatin Immunoprecipitation Protocol for Circadian Clock Proteins. In D. Staiger, S. Davis, & A. M. Davis (Eds.), Plant Circadian Networks (Vol. 2398, pp. 135–150). Springer US. https://doi.org/10.1007/978-1-0716-1912-4_12
Alawadi, A. A., Benedito, V. A., Skinner, R. C., Warren, D. C., Showman, C., & Tou, J. C. (2022). RNA-sequencing revealed apple pomace ameliorates expression of genes in the hypothalamus associated with neurodegeneration in female rats fed a Western diet during adolescence to adulthood. Nutritional Neuroscience, 1–13. https://doi.org/10.1080/1028415X.2022.2050008
Amos, B., Aurrecoechea, C., Barba, M., Barreto, A., Basenko, E. Y., Bażant, W., Belnap, R., Blevins, A. S., Böhme, U., Brestelli, J., Brunk, B. P., Caddick, M., Callan, D., Campbell, L., Christensen, M. B., Christophides, G. K., Crouch, K., Davis, K., DeBarry, J., … Zheng, J. (2022). VEuPathDB: The eukaryotic pathogen, vector and host bioinformatics resource center. Nucleic Acids Research, 50(D1), D898–D911. https://doi.org/10.1093/nar/gkab929
Amundson, K. K., Borton, M. A., Daly, R. A., Hoyt, D. W., Wong, A., Eder, E., Moore, J., Wunch, K., Wrighton, K. C., & Wilkins, M. J. (2022). Microbial colonization and persistence in deep fractured shales is guided by metabolic exchanges and viral predation. Microbiome, 10(1), 5. https://doi.org/10.1186/s40168-021-01194-8
Andrade-Martínez, J. S., Camelo Valera, L. C., Chica Cárdenas, L. A., Forero-Junco, L., López-Leal, G., Moreno-Gallego, J. L., Rangel-Pineros, G., & Reyes, A. (2022). Computational Tools for the Analysis of Uncultivated Phage Genomes. Microbiology and Molecular Biology Reviews, 86(2), e00004-21. https://doi.org/10.1128/mmbr.00004-21
Arras, P., Frank, P., Haim, P., Knollmüller, J., Leike, R., Reinecke, M., & Enßlin, T. (2022). Variable structures in M87* from space, time and frequency resolved interferometry. Nature Astronomy, 6(2), 259–269. https://doi.org/10.1038/s41550-021-01548-0
Beck, M. A., Liu, C.-Y., Bidinosti, C. P., Henry, C. J., Godee, C. M., & Ajmani, M. (2021). An extensive lab-and field-image dataset of crops and weeds for computer vision tasks in agriculture.
Bendaoud, F., Kim, G., Larose, H., Westwood, J. H., Zermane, N., & Haak, D. C. (2022). Genotyping‐by‐sequencing analysis of Orobanche crenata populations in Algeria reveals genetic differentiation. Ecology and Evolution, 12(3). https://doi.org/10.1002/ece3.8750
Boeckman, J., Korn, A., Yao, G., Ravindran, A., Gonzalez, C., & Gill, J. (2022). Sheep in wolves’ clothing: Temperate T7-like bacteriophages and the origins of the Autographiviridae. Virology, 568, 86–100. https://doi.org/10.1016/j.virol.2022.01.013
Buchholz, H. H., Bolaños, L. M., Bell, A. G., Michelsen, M. L., Allen, M. J., & Temperton, B. (2022). A Novel and Ubiquitous Marine Methylophage Provides Insights into Viral-Host Coevolution and Possible Host-Range Expansion in Streamlined Marine Heterotrophic Bacteria. Applied and Environmental Microbiology, 88(7), e00255-22. https://doi.org/10.1128/aem.00255-22
Busta, L., Dweikat, I., Sato, S. J., Qu, H., Xue, Y., Zhou, B., Gan, L., Yu, B., Clemente, T. E., Cahoon, E. B., & Zhang, C. (2022). Chemical and genetic variation in feral Cannabis sativa populations across the Nebraska climate gradient. Phytochemistry, 200, 113206. https://doi.org/10.1016/j.phytochem.2022.113206
Chakrabarti, M., Nagabhyru, P., Schardl, C. L., & Dinkins, R. D. (2022). Differential gene expression in tall fescue tissues in response to water deficit. The Plant Genome, 15(2). https://doi.org/10.1002/tpg2.20199
Chávez Montes, R. A., Haber, A., Pardo, J., Powell, R. F., Divisetty, U. K., Silva, A. T., Hernández-Hernández, T., Silveira, V., Tang, H., Lyons, E., Herrera Estrella, L. R., VanBuren, R., & Oliver, M. J. (2022). A comparative genomics examination of desiccation tolerance and sensitivity in two sister grass species. Proceedings of the National Academy of Sciences, 119(5), e2118886119. https://doi.org/10.1073/pnas.2118886119
Chiozzi, G., De Marchi, G., Fasola, M., Ibrahim, K. M., Bardelli, G., Hagos, F., Rocca, F., & Masseti, M. (2022). Insular gazelles of the circum-Arabian seas: Origin, distribution, dwarfism and taxonomy. Mammalian Biology, 102(1), 1–20. https://doi.org/10.1007/s42991-021-00186-3
Dykes, J., Kappenman, K., & Nissen, E. D. (2022). Using DNA Barcoding Methods to Identify Wild Huckleberry, Vaccinium membranaceum , as a Classroom Project. The American Biology Teacher, 84(1), 40–44. https://doi.org/10.1525/abt.2022.84.1.40
Ehsani, M. R., Zarei, A., Gupta, H. V., Barnard, K., Lyons, E., & Behrangi, A. (2022). NowCasting-Nets: Representation Learning to Mitigate Latency Gap of Satellite Precipitation Products Using Convolutional and Recurrent Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–21. https://doi.org/10.1109/TGRS.2022.3158888
Fernandez-Pozo, N., Haas, F. B., Gould, S. B., & Rensing, S. A. (2022). An overview of bioinformatics, genomics and transcriptomics resources for bryophytes. Journal of Experimental Botany, erac052. https://doi.org/10.1093/jxb/erac052
Fitzgerald, C., Vens, C. S., Miller, N., Barker, R., Westphall, M., Lombardino, J., Miao, J., Swanson, S. J., & Gilroy, S. (2022). Using the Automated Botanical Contact Device (ABCD) to Deliver Reproducible, Intermittent Touch Stimulation to Plants. In E. B. Blancaflor (Ed.), Plant Gravitropism (Vol. 2368, pp. 81–94). Springer US. https://doi.org/10.1007/978-1-0716-1677-2_6
Gage, J. L., Mali, S., McLoughlin, F., Khaipho-Burch, M., Monier, B., Bailey-Serres, J., Vierstra, R. D., & Buckler, E. S. (2022). Variation in upstream open reading frames contributes to allelic diversity in maize protein abundance. Proceedings of the National Academy of Sciences, 119(14), e2112516119. https://doi.org/10.1073/pnas.2112516119
Gupta, S., & Rather, S. A. (2022). Innovations in Crop Production: An Amalgamation of Abiotic Stress Physiology and Technology. Plant Abiotic Stress Physiology: Volume 1: Responses and Adaptations, 1.
Harrison, K., Levy, J. G., & Tamborindeguy, C. (2022). Effects of ‘Candidatus Liberibacter solanacearum’ haplotypes A and B on tomato gene expression and geotropism. BMC Plant Biology, 22(1), 156. https://doi.org/10.1186/s12870-022-03505-z
Heilman, K. A., Dietze, M. C., Arizpe, A. A., Aragon, J., Gray, A., Shaw, J. D., Finley, A. O., Klesse, S., DeRose, R. J., & Evans, M. E. K. (2022). Ecological forecasting of tree growth: Regional fusion of tree‐ring and forest inventory data to quantify drivers and characterize uncertainty. Global Change Biology, gcb.16038. https://doi.org/10.1111/gcb.16038
Jhu, M.-Y., Farhi, M., Wang, L., Philbrook, R. N., Belcher, M. S., Nakayama, H., Zumstein, K. S., Rowland, S. D., Ron, M., Shih, P. M., & Sinha, N. R. (2022). Heinz-resistant tomato cultivars exhibit a lignin-based resistance to field dodder ( Cuscuta campestris ) parasitism. Plant Physiology, kiac024. https://doi.org/10.1093/plphys/kiac024
Joaquim, P. I. L., Molinari, M. D. C., Marin, S. R. R., Barbosa, D. A., Viana, A. J. C., Rech, E. L., Henning, F. A., Nepomuceno, A. L., & Mertz-Henning, L. M. (2022). Nitrogen compounds transporters: Candidates to increase the protein content in soybean seeds. Journal of Plant Interactions, 17(1), 309–318. https://doi.org/10.1080/17429145.2022.2039791
Martínez-Alvarez, L., Ramond, J.-B., Vikram, S., León-Sobrino, C., Maggs-Kölling, G., & Cowan, D. (2022). With a pinch of salt: Metagenomic insights into Namib Desert salt pan microbial mats and halites reveal functionally adapted and competitive communities [Preprint]. Microbiology. https://doi.org/10.1101/2022.02.18.481119
Martinson, J. W., Bencic, D. C., Toth, G. P., Kostich, M. S., Flick, R. W., See, M. J., Lattier, D., Biales, A. D., & Huang, W. (2022). De Novo Assembly of the Nearly Complete Fathead Minnow Reference Genome Reveals a Repetitive but Compact Genome. Environmental Toxicology and Chemistry, 41(2), 448–461. https://doi.org/10.1002/etc.5266
Morisse, M., Wells, D. M., Millet, E. J., Lillemo, M., Fahrner, S., Cellini, F., Lootens, P., Muller, O., Herrera, J. M., Bentley, A. R., & Janni, M. (2022). A European perspective on opportunities and demands for field-based crop phenotyping. Field Crops Research, 276, 108371. https://doi.org/10.1016/j.fcr.2021.108371
Nguyen, D. T., Hayes, J. E., Atieno, J., Li, Y., Baumann, U., Pattison, A., Bramley, H., Hobson, K., Roorkiwal, M., Varshney, R. K., Colmer, T. D., & Sutton, T. (2022). The genetics of vigour-related traits in chickpea (Cicer arietinum L.): Insights from genomic data. Theoretical and Applied Genetics, 135(1), 107–124. https://doi.org/10.1007/s00122-021-03954-4
Nguyen, N. T., Khan, M. A., Castro–Guerrero, N. A., Chia, J.-C., Vatamaniuk, O. K., Mari, S., Jurisson, S. S., & Mendoza-Cozatl, D. G. (2022). Iron Availability within the Leaf Vasculature Determines the Magnitude of Iron Deficiency Responses in Source and Sink Tissues in Arabidopsis. Plant and Cell Physiology, 63(6), 829–841. https://doi.org/10.1093/pcp/pcac046
Palos, K. (2022). Identifying Long Noncoding RNAs and Examining Their Functional Roles in Brassicaceae [PhD Thesis]. In ProQuest Dissertations and Theses. https://ezproxy.library.arizona.edu/login?url=https://www.proquest.com/dissertations-theses/identifying-long-noncoding-rnas-examining-their/docview/2626931596/se-2?accountid=8360
Pucker, B., Irisarri, I., de Vries, J., & Xu, B. (2022). Plant genome sequence assembly in the era of long reads: Progress, challenges and future directions. Quantitative Plant Biology, 3, e5. https://doi.org/10.1017/qpb.2021.18
Rahman, M. A., Tutul, A. A., Abdullah, S. M., & Bayzid, Md. S. (2022). CHAPAO: Likelihood and hierarchical reference-based representation of biomolecular sequences and applications to compressing multiple sequence alignments. PLOS ONE, 17(4), e0265360. https://doi.org/10.1371/journal.pone.0265360
Ratnaparkhe, M. B., Nataraj, V., Shivakumar, M., Chandra, S., Ramesh, S. V., Kumawat, G., Kamble, V., Rajput, L. S., Kumar, S., Rajesh, V., Satpute, G. K., Ramteke, R., Kavishwar, R., Dubey, A., Marmat, N., Shroti, R., Shrivastava, M., Gupta, S., Sharma, M. P., … Nguyen, H. (2022). Genomic Design for Biotic Stresses in Soybean. In C. Kole (Ed.), Genomic Designing for Biotic Stress Resistant Oilseed Crops (pp. 1–54). Springer International Publishing. https://doi.org/10.1007/978-3-030-91035-8_1
Ratnaparkhe, M. B., Satpute, G. K., Kumawat, G., Chandra, S., Kamble, V. G., Kavishwar, R., Singh, V., Singh, J., Singh, A. K., Ramesh, S. V., Kumar, V., Sudhakaran, S., Srivastava, M. K., Shesh, N., Jajoo, A., Gupta, S., Singh, M., Xu, D., Bhattacharya, M., & Nguyen, H. T. (2022). Genomic Designing for Abiotic Stress Tolerant Soybean. In C. Kole (Ed.), Genomic Designing for Abiotic Stress Resistant Oilseed Crops (pp. 1–73). Springer International Publishing. https://doi.org/10.1007/978-3-030-90044-1_1
Rhoades, N. S., Davies, M., Lewis, S. A., Cinco, I. R., Kohama, S. G., Bermudez, L. E., Winthrop, K. L., Fuss, C., Mattison, J. A., Spindel, E. R., & Messaoudi, I. (2022). Functional, transcriptional, and microbial shifts associated with healthy pulmonary aging: Insights from rhesus macaques [Preprint]. Immunology. https://doi.org/10.1101/2022.02.08.479578
Salgado-Salazar, C., Romberg, M. K., Blomquist, C., Nunziata, S., Cai, W., & Rivera, Y. (2022). Lifestyle, mating type and mitochondrial genome features of the plant pathogen Calonectria hawksworthii (Hypocreales, Nectriaceae) as revealed by genome analyses. Canadian Journal of Plant Pathology, 1–14. https://doi.org/10.1080/07060661.2022.2065534
Sams, E. I., Ng, J. K., Tate, V., Claire Hou, Y.-C., Cao, Y., Antonacci-Fulton, L., Belhassan, K., Neidich, J., Mitra, R. D., Cole, F. S., Dickson, P., Milbrandt, J., & Turner, T. N. (2022). From karyotypes to precision genomics in 9p deletion and duplication syndromes. Human Genetics and Genomics Advances, 3(1), 100081. https://doi.org/10.1016/j.xhgg.2021.100081
Satapathy, K., Psaltis, D., Özel, F., Medeiros, L., Dougall, S. T., Chan, C.-K., Wielgus, M., Prather, B. S., Wong, G. N., Gammie, C. F., & others. (2022). The Variability of the Black Hole Image in M87 at the Dynamical Timescale. The Astrophysical Journal, 925(1), 13.
Senn, S., Pangell, K., & Bowerman, A. L. (2022). Metagenomic Insights into the Composition and Function of Microbes Associated with the Rootzone of Datura inoxia. BioTech, 11(1), 1. https://doi.org/10.3390/biotech11010001
Shang, J., & Sun, Y. (2022). CHERRY: A Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model. ArXiv:2201.01018 [Cs, q-Bio]. http://arxiv.org/abs/2201.01018
Song, X., Li, Y., Stirling, E., Zhao, K., Wang, B., Zhu, Y., Luo, Y., Xu, J., & Ma, B. (2022). AsgeneDB: A curated orthology arsenic metabolism gene database and computational tool for metagenome annotation [Preprint]. Preprints. https://doi.org/10.22541/au.164975586.65142559/v1
Spalding, E., Morzinski, K. M., Hinz, P., Males, J., Meyer, M., Quanz, S. P., Leisenring, J., & Power, J. (2022). High-contrast Imaging with Fizeau Interferometry: The Case of Altair*. The Astronomical Journal, 163(2), 62. https://doi.org/10.3847/1538-3881/ac3b5b
Stillson, P. T., Baltrus, D. A., & Ravenscraft, A. (2022). Prevalence of an Insect-Associated Genomic Region in Environmentally Acquired Burkholderiaceae Symbionts. Applied and Environmental Microbiology, 88(9), e02502-21. https://doi.org/10.1128/aem.02502-21
Tran, H., Zhang, J., O’Neill, M. M., Ryken, A., Condon, L. E., & Maxwell, R. M. (2022). A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019. Scientific Data, 9(1), 16. https://doi.org/10.1038/s41597-022-01123-w
Tuggle, C. K., Clarke, J., Dekkers, J. C. M., Ertl, D., Lawrence-Dill, C. J., Lyons, E., Murdoch, B. M., Scott, N. M., & Schnable, P. S. (2022). The Agricultural Genome to Phenome Initiative (AG2PI): Creating a shared vision across crop and livestock research communities. Genome Biology, 23(1), 3, s13059-021-02570–02571. https://doi.org/10.1186/s13059-021-02570-1
Wang, L., Lu, Z., Van Buren, P., & Ware, D. (2022). SciApps: An Automated Platform for Processing and Distribution of Plant Genomics Data. In D. Edwards (Ed.), Plant Bioinformatics (Vol. 2443, pp. 197–209). Springer US. https://doi.org/10.1007/978-1-0716-2067-0_10
Wei, W., Zhao, Q., Wang, Z., Liau, W.-S., Basic, D., Ren, H., Marshall, P. R., Zajaczkowski, E. L., Leighton, L. J., Madugalle, S. U., Musgrove, M., Periyakaruppiah, A., Shi, J., Zhang, J., Mattick, J. S., Mercer, T. R., Spitale, R. C., Li, X., & Bredy, T. W. (2022). ADRAM is an experience-dependent long noncoding RNA that drives fear extinction through a direct interaction with the chaperone protein 14-3-3. Cell Reports, 38(12), 110546. https://doi.org/10.1016/j.celrep.2022.110546
Williams, J. (2022). CyVerse for Reproducible Research: RNA-Seq Analysis. In D. Edwards (Ed.), Plant Bioinformatics (Vol. 2443, pp. 57–79). Springer US. https://doi.org/10.1007/978-1-0716-2067-0_3
Wyman, C. L., Gontijo Silva Maia, L., Yang, L., & Benedito, V. A. (2022). The Model Legume, Medicago truncatula in the Genomic Era: Speeding Up Discoveries in Legume Biology. In S. Sinharoy, Y. Kang, & V. Benedito (Eds.), The Medicago truncatula Genome (pp. 1–9). Springer International Publishing. https://doi.org/10.1007/978-3-030-90757-0_1
Xu, G., Lyu, J., Obata, T., Liu, S., Ge, Y., Schnable, J. C., & Yang, J. (2022). A historically balanced locus under recent directional selection in responding to changed nitrogen conditions during modern maize breeding [Preprint]. Genomics. https://doi.org/10.1101/2022.02.09.479784
Zhang, J., Sun, H., Guo, S., Ren, Y., Li, M., Wang, J., Yu, Y., Zhang, H., Gong, G., He, H., Zhang, C., & Xu, Y. (2022). ClZISO mutation leads to photosensitive flesh in watermelon. Theoretical and Applied Genetics. https://doi.org/10.1007/s00122-022-04054-7
Zhang, X., Zhu, Y., Kremling, K. A. G., Romay, M. C., Bukowski, R., Sun, Q., Gao, S., Buckler, E. S., & Lu, F. (2022). Genome-wide analysis of deletions in maize population reveals abundant genetic diversity and functional impact. Theoretical and Applied Genetics, 135(1), 273–290. https://doi.org/10.1007/s00122-021-03965-1
Zhao, Y., Zhang, S., Lv, Y., Ning, F., Cao, Y., Liao, S., Wang, P., & Huang, S. (2022). Optimizing ear-plant height ratio to improve kernel number and lodging resistance in maize (Zea mays L.). Field Crops Research, 276, 108376. https://doi.org/10.1016/j.fcr.2021.108376