نقش بیوانفورماتیک در به‌نژادی گیاهان برای تنش‌های غیرزیستی

نوع مقاله : مروری

نویسندگان

1 محقق پسادکتری، گروه تولید و ژنتیک گیاهی دانشکده کشاورزی دانشگاه ارومیه، ارومیه، ایران.

2 دانشجوی دکتری ژنتیک و به‌نژادی گیاهی، گروه ژنتیک و به‌نژادی گیاهی، پردیس کشاورزی، دانشگاه تربیت مدرس، تهران، ایران.

3 استاد، گروه تولید و ژنتیک گیاهی دانشکده کشاورزی دانشگاه ارومیه، ارومیه، ایران.

4 استاد، گروه ژنتیک و به‌نژادی گیاهی، پردیس کشاورزی، دانشگاه تربیت مدرس، تهران، ایران.

5 دانشیار، گروه تولید و ژنتیک گیاهی دانشکده کشاورزی دانشگاه ارومیه، ارومیه، ایران.

6 دانش آموخته کارشناسی ارشد، انستیتو علوم اعصاب تولوز، فرانسه.

7 دانشیار، گروه مهندسی تولید و ژنتیک گیاهی دانشکده کشاورزی دانشگاه مراغه، مراغه، ایران.

8 استاد، گروه بیوتکنولوژی کشاورزی، پردیس کشاورزی، دانشگاه تربیت مدرس. تهران، ایران.

چکیده

مقدمه: تنش‌های غیرزیستی به‌عنوان عوامل اصلی محدودکننده بهره‌وری در کشاورزی شناخته می‌شوند. در عصر حاضر، با توجه به تغییرات مداوم اقلیمی، درک جنبه‌های مولکولی مرتبط با پاسخ گیاهان به این تنش‌ها از اهمیت بالایی برخوردار است. ظهور فناور­های اُمیکس، راهبردهای کلیدی را برای ارتقای تحقیقات مؤثر در این حوزه ارائه می‌دهد و تحقیقات را از مدل‌های مرجع به سمت گونه‌ها و ژنوتیپ‌های متنوع مقاوم و حساس گسترش می‌دهد. با استفاده از رویکردهای چند سطحی یکپارچه، که شامل بررسی‌های ژنومیکس، ترنسکریپتومیکس، پروتئومیکس و متابولومیکس می‌شوند، می‌توان به درک بهتری از فرآیندهای مولکولی مرتبط با پاسخ به تنش‌های غیرزیستی دست یافت. در این راستا، بیوانفورماتیک به‌عنوان ابزاری اساسی برای تولید، استخراج و یکپارچه‌سازی داده‌ها عمل کرده و برای استخراج اطلاعات ارزشمند و انجام تحلیل‌های مقایسه‌ای ضروری است.
مواد و روش‌ها: مقاله حاضر به‌عنوان یک مقاله مروری، با استفاده از روش تحلیل محتوا تهیه شده است. این مطالعه بر اساس جستجوی سیستماتیک در پایگاه‌های داده معتبر علمی شامل PubMed، Web of Science، Google Scholar و Scopus انجام گرفته است.
یافته‌ها: در این مطالعه به بررسی نقش فناوری‌های اُمیکس و بیوانفورماتیک در بهبود تحمل گیاهان نسبت به تنش‌های غیرزیستی پرداخته شد. در ابتدا، فناوری‌های اصلی تولید داده‌های مولکولی عظیم و منابع عمومی بیوانفورماتیک مرور شده است. سپس، پایگاه‌های داده بیوانفورماتیکی مرتبط با تنش‌های غیرزیستی مورد بررسی قرار گرفته­اند. همچنین، یافته‌های مطالعات بیوانفورماتیکی که به شناسایی ژن‌های کلیدی و مسیرهای متابولیکی مرتبط با تحمل به تنش‌های غیرزیستی پرداخته‌اند، به دقت تحلیل شده­اند.
نتیجه‌گیری: منابع بیوانفورماتیکی به محققان این امکان را می‌دهند که به اطلاعات ژنومی، ترنسکریپتومی و پروتئومیکی دسترسی پیدا کنند و یافته‌های بیوانفورماتیکی را با داده‌های تجربی ترکیب نمایند. این فرآیندها زمینه را برای مدل‌سازی دقیق‌تر فرآیندهای دخیل فراهم می‌سازند و نتایج مطالعات بیوانفورماتیکی می‌توانند به شناسایی ژن‌ها و مسیرهای متابولیکی مؤثر در تحمل به تنش‌های غیرزیستی منجر شوند. در نهایت، این رویکردهای یکپارچه می‌توانند به توسعه استراتژی‌های به‌نژادی هدفمند برای ایجاد گیاهان مقاوم به تنش‌های غیرزیستی کمک کنند و بدین ترتیب بهره‌وری کشاورزی را افزایش دهند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

The Role of Bioinformatics in Plant Breeding for Abiotic Stresses

نویسندگان [English]

  • Maryam Kholghi 1
  • Parviz Radmanesh 2
  • Reza Darvishzadeh 3
  • Ghasem Karizmadeh 4
  • Hadi Alipour 5
  • Somaieh Soufimaleky 6
  • Hamid Hatami Maleki 7
  • Danial Kahrizi 8
1 Post-Doctoral Researcher, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.
2 PhD student of Genetics and Plant Breeding, Department of Plant Genetics and Breeding, College of Agriculture, Tarbiat Modares University, Tehran, Iran.
3 Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.
4 Professor, Department of Plant Genetics and Breeding, College of Agriculture, Tarbiat Modares University, Tehran, Iran.
5 Associate professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.
6 MSc Graduate, Institut des Sciences du Cerveau de Toulouse, France.
7 Associate professor, Department of Plant Production and Genetics, Faculty of Agriculture, University of Maragheh, Maragheh, Iran.
8 Professor, Department of Agricultural Biotechnology, College of Agriculture, Tarbiat Modarres University, Tehran, Iran.
چکیده [English]

Introduction: Abiotic stresses are recognized as the primary limiting factors for productivity in agriculture. In the current era of continuous climate changes, understanding the molecular aspects involved in abiotic stress response in plants is a priority. The emergence of -omics approaches provides key strategies to promote effective research in the field, facilitating investigations from reference models to an increasing number of species, and tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics, and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular mechanisms involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining, and integration and is necessary for extracting valuable information and for comparative efforts.
Materials and methods: This article is a review that utilizes content analysis methodology. The research systematically searched reputable scientific databases such as PubMed, Web of Science, Google Scholar, and Scopus.
Results: The focus on the role of omics technologies and bioinformatics in enhancing plant tolerance to abiotic stresses. Initially, it provides an overview of the main technologies for generating large-scale molecular data and public bioinformatics resources. Subsequently, bioinformatics databases related to abiotic stresses were explored. Additionally, it provides a detailed analysis of findings from bioinformatics studies that have identified key genes and metabolic pathways linked to abiotic stress tolerance.
Conclusion: Bioinformatics tools provide researchers with access to genomic, transcriptomic, and proteomic data, allowing them to combine bioinformatics findings with experimental data. These processes facilitate more accurate modeling of the involved mechanisms, and the results of bioinformatics studies can lead to the identification of genes and metabolic pathways that are effective in conferring tolerance to abiotic stresses. Ultimately, these integrated approaches support the development of targeted breeding strategies to produce stress-resistant plants, thereby improving agricultural productivity.

کلیدواژه‌ها [English]

  • Genomics
  • Transcriptomics
  • Proteomics
  • Metabolomics
  • Database
  • Stress
Alter, S., Bader, K. C., Spannagl, M., Wang, Y., Bauer, E., Schön, C. C., & Mayer, K. F. 2015. DroughtDB: an expert-curated compilation of plant drought stress genes and their homologs in nine species. Database, 15, bav046. https://doi.org/10.1093/database/bav046
Altman, R. B. 2004. Building successful biological databases. Briefings in Bioinformatics, 5(1), 4-5. https://doi.org/10.1093/bib/5.1.4
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. 1990. Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410. https://doi.org/10.1016/S0022-2836(05)80360-2
Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W., & Lipman, D. J. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research, 25 (17), 3389-3402. https://doi.org/10.1093/nar/25.17.3389
Ambrosone, A., Batelli, G., Bostan, H., D’Agostino, N., Chiusano, M.L., Perrotta, G., Leone, A., Grillo, S., & Costa, A. 2017. Distinct gene networks drive di_erential response to abrupt or gradual water deficit in potato. Gene, 597, 30–39. https://doi.org/10.1016/j.gene.2016.10.024
Amid, C., Birney, E., Bower, L., Cerdeño-Tárraga, A., Cheng, Y., Cleland, I., Faruque, N., Gibson, R., Goodgame, N., Hunter, C., & Jang, M. 2011. Major submissions tool developments at the European nucleotide archive. Nucleic Acids Researc, 40, D43-D47. http://dx.doi.org/10.1093/nar/gkr946
Anjoy, P., Kumar, K., Chandra, G., & Gaikwad, K. 2024. Genomics Data Analysis for Crop Improvement, Springer Protocols Handbooks. P: 1-374. https://link.springer.com/book/9789819969128
Arab, M., Kazemi-Tabar, S.K., Hashemi-Petroudi, & S.H.R. 1400. Bioinformatics analysis of CBL gene family members in Sesamum indicum under drought stress. Crop Biotechnology, 11(36), 17-31 (In Persian). https://doi.org/10.30473/cb.2022.62549.1867
Arabidopsis Genome Initiative. 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature, 408(6814), 796–815. https://doi.org/10.1038/35048692
Aranzana, M.J., Decroocq, V., Dirlewanger, E., Eduardo, I., Gao, Z.S., Gasic, K., Iezzoni, A., Jung, S., Peace, C., Prieto, H., Tao, R., Verde, I.,  Abbott A.G., & Arús P. 2019. Prunus genetics and applications after de novo genome sequencing: Achievements and prospects. Horticultural Research, 6, 58. https://doi.org/10.1038/s41438-019-0140-8
Arita, M., Karsch-Mizrachi, I., Cochrane, G. 2021. The international nucleotide sequence database collaboration. Nucleic Acids Res., 49(D1): D121-D124. doi: 10.1093/nar/gkaa967.
Aslam, B., Basit, M., Nisar, M.A., Khurshid, M., & Rasool, M.H. 2017. Proteomics: Technologies and Their Applications. Journal of Chromatographic Science, 55, 182-196. https://doi.org/10.1093/chromsci/bmw167
Attwood, T.K., Gisel, A., Eriksson, N.E., & Bongcam-Rudloff, E. 2011. Concepts, Historical Milestones and the Central Place of Bioinformatics in Modern Biology: a European Perspective. Bioinformatics-Trends and Methodologies, 1. DOI: 10.5772/23535
Balaji, J., Crouch, J.H., Petite, P.V., & Hoisington, D.A. 2006. A database of annotated tentative orthologs from crop abiotic stress transcripts. Bioinformation, 1, 225-227. http://intranet.icrisat.org/gt1/tog/homepage
Barker, W.C., Garavelli J.S., Haft, D.H., Hunt, L.T., Marzec, C.R., Orcutt, B.C., Srinivasarao, G.Y., Yeh, L.S., Ledley, R.S., Mewes, H.W., & Pfeiffer, F. 1998. The PIR-international protein sequence database. Nucleic Acids Research, 26: 27-32. doi.org/10.1093/nar/26.1.27
Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J, Ostell, J., Rapp, B.A., & Wheeler, DL. 2000. GenBank. Nucleic Acids Research, 28(1), 15-18. doi.org/10.1093/nar/28.1.15
Berardini, T.Z., Reiser, L., Li, D., Mezheritsky, Y., Muller, R., Strait, E., & Huala, E. 2015. The Arabidopsis information resource: making and mining the “gold standard” annotated reference plant genome. Genesis, 53(8), 474-485. https://doi.org/10.1002/dvg.22877
Berman, H.M., Battistuz, T., Bhat, T.N., Bluhm, W.F., Bourne, P.E., Burkhardt, K., Feng, Z., Gilliland, G.L., Iype, L., Jain, S., & Fagan, P. 2002. The protein data bank. Acta Crystallogr D Biol Crystallogr, 58(1), 899-907. doi: 10.1107/s0907444902003451
Berman, HM., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N, & Bourne PE. 2000. The Protein Data Bank. Nucleic Acids Research, 28(1): 235-42. doi.org/10.1093/nar/28.1.235
Blaszczyk, M., Jamroz, M., Kmiecik, S.,  & Kolinski, A. 2013. CABS-fold: server for the de novo and consensus-based prediction of protein structure. Nucleic Acids Research, 41(1), 406-411. doi.org/10.1093/nar/gkt462
Bludau, I., & Aebersold, R. 2020. Proteomic and interactomic insights into the molecular basis of cell functional diversity. Nature Reviews Molecular Cell Biology, 21,  327–340. https://doi.org/10.1038/s41580-020-0231-2
Bolser, D., Staines D.M., Pritchard, E., & Kersey, P. 2016. Ensembl Plants: Integrating Tools for Visualizing, Mining, and Analyzing Plant Genomics Data. Methods Mol Biol., 2016;1374:115-40. doi: 10.1007/978-1-4939-3167-5_6.
Boutet E, Lieberherr D, Tognolli M, Schneider M, Bansal P, Bridge AJ, Poux S, Bougueleret L, Xenarios I. 2016. UniProtKB/Swiss-Prot, the Manually Annotated Section of the UniProt KnowledgeBase: How to Use the Entry View. Methods Mol Biol., 1374, 23-54. doi: 10.1007/978-1-4939-3167-5_2.
Caligari PDS, Brown J (2017) Plant Breeding, Practice. In: Thomas B, Murray BG, Murphy DJ (eds) Encyclopedia of Applied Plant Sciences, 2nd edn. Academic Press, London. https:// doi. org/ 10. 1016/ B978-0- 12- 394807- 6. 00195-7
Çelik, H., Aravena, A., & Turgut Kara, N. 2023. Bioinformatics and gene expression analysis of the legume F box/WD40 proteins in NaCl and high temperature stress. Genetic Resources and Crop Evolution, 70(8), 2637–2655. https://doi.org/10.1007/s10722-023-01592-x
Chang, Y., Liu, H., Liu, M., Liao, X., Sahu, S.K., Fu, Y., Song, B., Cheng, S., Kariba, R., & Muthemba, S. 2018. The draft genomes of five agriculturally important African orphan crops. GigaScience, 8, Article 10.1093/gigascience/giy152. https://doi.org/10.1093/gigascience/giy152
Chaturvedi, P., Pierides, I., Zhang, S., Schwarzerova, J., Ghatak, A., & Weckwerth, W. 2024. Multiomics for Crop Improvement. In: Frontier Technologies for Crop Improvement Sustainability Sciences in Asia and Africa. Springer Nature Singapore, pp. 107-141. https://link.springer.com/chapter/10.1007/978-981-99-4673-0_6
Chen, C., Huang, H., & Wu, C.H. 2017. Protein bioinformatics databases and resources. In: Protein bioinformatics. Humana Press, New York, NY. pp: 3-39. https://doi.org/10.1007/978-1-4939-6783-4_1
Chen, J., Anderson, J.B., DeWeese-Scott, C., Fedorova, N.D., Geer, L.Y., He, S., Hurwitz, D.I., Jackson, J.D., Jacobs, A.R., Lanczycki, C.J., & Liebert, C.A. 2003. MMDB: Entrez’s 3D-structure database. Nucleic Acids Research, 31(1) 474-477. doi.org/10.1093/nar/30.1.249
Choudhury, F.K., Rivero, R.M., Eduardo, B., & Mittler, R. 2017. Reactive oxygen species, abiotic stress and stress combination. The Plant Journal, 90(5), 856-867. doi: 10.1111/tpj.13299.
Claros, M. G., Bautista, R., Guerrero-Fernández, D., Benzerki, H., Seoane, P., & Fernández-Pozo, N. 2012. Why assembling plant genome sequences is so challenging. Biology (Basel), 1(2), 439–459. https://doi.org/10.3390/biology1020439
D’Alessandro, A., Taamalli, M., Gevi, F., Timperio, A.M., Zolla, L., Ghnaya, T., 2013. Cadmium stress responses in Brassica juncea: Hints from proteomics and metabolomics. Journal of Proteome Research, 12, 4979-4997. https://doi.org/10.1021/pr400793e
Delanne, J., Nambot, S., Chassagne, A., Putois, O., Pelissier, A., Peyron, C., Gautier, E., Thevenon, J., Cretin, E., Bruel, A.L. & Goussot, V. 2019. Secondary findings from whole-exome/genome sequencing evaluating stakeholder perspectives. A review of the literature. European Journal of Medical Genetics, 62(6), p.103529. https://doi.org/10.1016/j.ejmg.2018.08.010
Domon, B., & Aebersold, R. 2010. Options and considerations when selecting a quantitative proteomics strategy. Nature Biotechnology, 28, 710–721. https://doi.org/10.1038/nbt.1661
Dong, Q., Wallrad, L., Almutairi, B.O., & Kudla, J. 2022. Ca2+ signaling in plant responses to abiotic stresses. Journal of Integrative Plant Biology, 64, 287–300. https://doi.org/10.1111/jipb.13228
Edgar, R.C. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research, 32, 1792-1797. doi.org/10.1093/nar/gkh340
Ercolano, M.R., Sacco, A., Ferriello, F., D’Alessandro, R., Tononi, P., Traini, A., Barone, A., Zago, E., Chiusano, M.L., & Buson, G., et al. 2014. Patchwork sequencing of tomato San Marzano and Vesuviano varieties highlights genome-wide variations. BMC Genomics, 15, Article 138. https://doi.org/10.1186/1471-2164-15-138
Fazan, L., Song, Y. G., & Kozlowski, G. 2020. The woody planet: from past triumph to manmade decline. Plants (Basel), 9(11), 1593. https://doi.org/10.3390/plants9111593
Friesner, R.A., Banks, J.L., Murphy, R.B., Halgren, T.A, Klicic, J.J, Mainz, D.T., Repasky, M.P., Knoll, E.H., Shelley, M., Perry, J.K., & Shaw, D.E. 2004. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47, 1739-1749. https://doi.org/10.1021/jm0306430
Goh, H.H., 2018. Integrative Multi-Omics Through Bioinformatics. Advances in Experimental Medicine and Biology, 1102, 69-80. https://doi.org/10.1007/978-3-319-98758-3_5
Gomez-Casati, D. F., Busi, M. V., Barchiesi, J., Peralta, D. A., Hedin, N., & Bhadauria, V. 2018. Applications of bioinformatics to plant biotechnology. Current Issues in Molecular Biology, 27, 89–104. https://doi.org/10.21775/cimb.027.089
Guex, N., & Peitsch, M.C. 1997. SWISS-MODEL and the Swiss-Pdb viewer: an environment for comparative protein modeling. Electrophoresis, 18, 2714-2723. doi: 10.1002/elps.1150181505
Guo, F., Islam, M.A., Lv, C., Jin, X., Sun, L., Zhao, K.,  & Sun, D. 2023. Insights into the bioinformatics and transcriptional analysis of the Elongator complexes (ELPs) Gene Family of wheat: TaELPs contribute to wheat abiotic stress tolerance and leaf senescence. Plants, 12(4), 952. doi: 10.3390/plants12040952
Guo, M., Liu, X., Wang, J., Li, L., Jiang, Y., Yu, X.,  & Meng, T. 2020. In-depth investigation on abiotic stress-responsive differentially expressed genes in Arabidopsis roots through GEO database. Journal of Plant Interactions, 15(1), 294-302. https://doi.org/10.1080/17429145.2020.1812742
Guo, P., Zhang, H., Wang, Y., & Zhang, Z. 2009. Differentially expressed genes between drought-tolerant and drought-sensitive barley genotypes in response to drought stress during the reproductive stage. Journal of Experimental Botany, 60, 3531–3544. https://doi.org/10.1093/jxb/erp194
Haq, S. A. U., Bashir, T., Roberts, T. H., &   Husaini, A. M. 2024. Ameliorating the effects of multiple stresses on agronomic traits in crops: Modern biotechnological and omics approaches. Molecular Biology Reports, 51, 41. https://doi.org/10.1007/s11033-023-09042-8
He, L., Xu, X.Q., Wang, Y., Chen, W.K., Sun, R.Z., Cheng, G., Liu, B., Chen, W., Duan, C.Q., & Wang, J., et al. 2020. Modulation of volatile compound metabolome and transcriptome in grape berries exposed to sunlight under dry-hot climate. BMC Plant Biology, 20, Article 59. https://doi.org/10.1186/s12870-020-2268-y
Hess, B., Kutzner, C, Van Der Spoel, D., & Lindahl, E. 2008. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of Chemical Theory and Computation, 4, 435-447. doi: 10.1021/ct700301q
Idrisi-Merian, K.h., Samizadeh Lahijani, H., Hosni, S.H, & Farrokhi, N. 2017. In silico prediction of cold responsive genes in canola by comparative genomics using Arabidopsis thaliana. Crop Biotechnology, 8(24), 29-46 (In Persian). https://doi.org/10.30473/cb.2019.40313.1735
İncili, Ç.Y., Arslan, B., Çelik, E.N.Y., Ulu, F., Horu, E., Baloglu, M.C., Çağlıyan, E., Burcu, G., Bayarslan, A.U., & Altunoglu, Y.C. 2023. Comparative bioinformatics analysis and abiotic stress responses of expansin proteins in Cucurbitaceae members: watermelon and melon. Protoplasma, 260, 509-527. https://doi.org/10.1007/s00709-022-01793-8 
Iovieno, P., Punzo, P., Guida, G., Mistretta, C., Van Oosten, M.J., Nurcato, R., Bostan, H., Colantuono, C., Costa, A., & Bagnaresi, P., et al. 2016. Transcriptomic changes drive physiological responses to progressive drought stress and rehydration in tomato. Frontiers in Plant Science, 7, 371. https://doi.org/10.3389/fpls.2016.00371
Jayaram, B., Dhingra, P., Mishra, A., Kaushik, R., Mukherjee, G., Singh, A., & Shekhar, S. 2014. BhageerathH: a homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins. BMC Bioinformatics, 15, 1-12. doi: 10.1186/1471-2105-15-S16-S7
Jayaram, B., Singh, T., Mukherjee, G., Mathur, A., Shekhar, S.,  & Shekhar, V. 2012. Sanjeevini: a freely accessible web-server for target directed lead molecule discovery. BMC Bioinformatics, 13, 1-13. doi: 10.1186/1471-2105-13-S17-S7
Jia, X., Sun, C., Zuo, Y., Li, G., Li, G., Ren, L., & Chen, G. 2016. Integrating transcriptomics and metabolomics to characterise the response of Astragalus membranaceus Bge. var. mongolicus (Bge.) to progressive drought stress. BMC Genomics, 17, Article 188. https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-2554-0
Jiang, W.J., Wang, M.T., Du, Z.Y., Li, J.H, Shi, Y., Wang, X., Wu, L.Y., Chen, J., Zhong, M., Yong, J., Hu, B.H., & Huang, J. 2023. Bioinformatic and functional analysis of OsDHN2 under cadmium stress. Functional and Integrative Genomics, 23(2), 1-11. https://doi.org/10.1007/s10142-023-01101-4
Jorge, T.F., Rodrigues, J.A., Caldana, C., Schmidt, R., van Dongen, J.T., Thomas-Oates, J., & Antonio, C. 2016. Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress. Mass Spectrometry Reviews, 35, 620-649. https://doi.org/10.1002/mas.21449
Joshi, S., Kaur, K., Khare, T., Srivastava, A. K., Suprasanna, P., & Kumar, V. 2021. Genome-wide identification, characterization and transcriptional profiling of NHX-type (Na+/H+) antiporters under salinity stress in soybean. 3 Biotech, 11, 1–17. https://doi.org/10.1007/s13205-020-02555-0
Källberg, M., Margaryan, G., Wang, S., Ma, J.,  Xu, J. 2014. RaptorX server: a resource for template-based protein structure modeling. Methods Mol Biol., 2014;1137:17-27. doi: 10.1007/978-1-4939-0366-5_2. PMID: 24573471.
Kamali, S., & Singh, A. 2023. Genomic and transcriptomic approaches to developing abiotic stress-resilient crops. Agronomy, 13, 2903. http://dx.doi.org/10.3390/agronomy13122903
Kaminuma, E., Kosuge, T., Kodama, Y., Aono, H., Mashima, J., Gojobori, T., Sugawara, H., Ogasawara, O., Takagi, T., Okubo, K., & Nakamura, Y. 2010. DDBJ progress report. Nucleic Acids Res., 39 (Database issue): D22-7. doi: 10.1093/nar/gkq1041.
Kanehisa, M., & Goto, S. 2000. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 28(1):27-30. doi: 10.1093/nar/28.1.27.
Karami-Lake, B., Sohani, M.M.,  & Abedi, A. 2019. Bioinformatical study of Calcium/cation (CaCA) antiporters gene family in maize (Zea mays L.). Crop Biotechnology, 9(29), 21-37 (In Persian). doi: 10.3389/fpls.2016.01775. eCollection 2016
Katoh, K., Kuma, K.I., Toh, H.,  Miyata, T. 2005. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Research, 33, 511-518. doi: 10.1093/nar/gki198
Keller, M., Consortium, S., & Simm, S. 2018. The coupling of transcriptome and proteome adaptation during development and heat stress response of tomato pollen. BMC Genomics, 19,  447. https://doi.org/10.1186/s12864-018-4824-5
Kim, D.E., Chivian, D., & Baker, D. 2004. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Research, 32, W526-W531. doi.org/10.1093/nar/gkh468
Kyriakidou, M., Tai, H. H., Anglin, N. L., Ellis, D., & Strömvik, M. V. 2018. Current strategies of polyploid plant genome sequence assembly. Frontiers in Plant Science, 9, 1660. https://doi.org/10.3389/fpls.2018.01660
Kumar, A., Pathak, R.K., Gupta, S.M., Gaur, V.S., & Pandey, D. 2015. Systems biology for smart crops and agricultural innovation: Filling the gaps between genotype and phenotype for complex traits linked with robust agricultural productivity and sustainability. OMICS: A Journal of Integrative Biology, 19, 581–601. https://doi.org/10.1089/omi.2015.0106
Kumar, S.A., Kumari, P.H., Sundararajan, V.S., Suravajhala, P., Kanagasabai, R., & Kishor, P.K. 2014. PSPDB: plant stress protein database. Plant Molecular Biology Reporter, 32(4), 940–942. doi: 10.1007/s11105-014-0698-0
Kwon, S.-W., Kim, M., Kim, H., & Lee, J. 2016. Shotgun quantitative proteomic analysis of proteins responding to drought stress in Brassica rapa L. (Inbred Line “Chiifu”). International Journal of Genomics, 2016, 9. https://doi.org/10.1155/2016/4235808
Lambert, C., Leonard, N., De Bolle, X.,  & Depiereux, E. 2002. ESyPred3D: prediction of proteins 3D structures. Bioinformatics, 18, 1250-1256. doi: 10.1093/bioinformatics/18.9.1250
Le, D.T., Kim, H., Nguyen, K.T., et al. 2012. Differential gene expression in soybean leaf tissues at late developmental stages under drought stress revealed by genome-wide transcriptome analysis. PLoS One, 7, 49522. https://doi.org/10.1371/journal.pone.0049522
Levitt, J. 1980. Responses of Plants to Environmental Stress. In Water, Radiation, Salt and other Stress. New York: Academic Press, 1, 345-447. https://doi.org/10.1126/science.177.4051.786.a
Li, H., Zhao, Q., Sun, X., Jia, H.,  & Ran, K. 2017. Bioinformatic identification and expression analysis of the Malus domestica DREB2 transcription factors in different tissues and abiotic stress. Journal of Plant Biochemistry and Biotechnology, 26, 436-443. https://doi.org/10.1007/s13562-017-0405-y
Lindemose, S., O’Shea, C., Jensen, M. K., & Skriver, K. 2013. Structure, function and networks of transcription factors involved in abiotic stress responses. International Journal of Molecular Sciences, 14, 5842–5878. https://doi.org/10.3390/ijms14035842
Liu, M., Yu, H., Zhao, G., Huang, Q., Lu, Y.,  Ouyang, B. 2017. Profiling of drought-responsive microRNA and mRNA in tomato using high-throughput sequencing. BMC Genomics, 18, 481. https://doi.org/10.1186/s12864-017-3869-1
Liu, Y., Zhou, J., & White, K.P. 2014. RNA-seq differential expression studies: More sequence or more replication? Bioinformatics, 30(3), 301–304. https://doi.org/10.1093/bioinformatics/btt688
Lopez de Maturana, E., Alonso, L., Alarcon, P., Martin-Antoniano, I.A., Pineda, S., Piorno, L., Calle, M.L., & Malats, N. 2019. Challenges in the Integration of Omics and Non-Omics Data. Genes, 10, 238. https://doi.org/10.3390/genes10030238
Luscombe, N.M., Greenbaum, D., & Gerstein, M. 2001. What is bioinformatics? An introduction and overview. Yearbook of Medical Informatics, 10(01), 83-100. http://dx.doi.org/10.1055/s-0038-1638103
Meena, K.K., Sorty, A.M., Bitla, U.M., Choudhary, K., Gupta, P., Pareek, A., …& Minhas, P.S. 2017. Abiotic Stress Responses and Microbe-Mediated Mitigation in Plants: The Omics Strategies. Frontiers in Plant Science, 8, 172. https://doi.org/10.3389/fpls.2017.0017
Michaletti, A., Naghavi, M.R., Toorchi, M., Zolla, L., & Rinalducci, S. 2018. Metabolomics and proteomics reveal drought-stress responses of leaf tissues from spring-wheat. Scientific Reports, 8, 5710. https://www.nature.com/articles/s41598-018-24012-y
Moraes Filho, R.M., Menezes, A.F., & Martins, L.S. 2017. In silico modeling and characterization of phytoparasitic nematodes translationally-controlled tumor proteins. Genetics and Molecular Research, 16(3), 1-11. doi: 10.4238/gmr16039800.
Morgenstern, B. 1999. DIALIGN 2: improvement of the segment-to-segment approach to multiple sequence alignment. Bioinformatics (Oxford, England), 15(3), 211-218. doi: 10.1093/bioinformatics/15.3.211
Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S., & Olson, A.J. 2009. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30, 2785-2791. doi: 10.1002/jcc.21256.
Mount, D.M. 2004. Bioinformatics: sequence and genome analysis (2endEd.). Cold Spring Harbor, NY : Cold Spring Harbor Laboratory Press, 652p. http://www.bioinformaticsonline.org/
Mousavi, S.A., Pouya, F.M., Ghaffari, M.R., Mirzaei, M., Ghaffari, A., Alikhani, M., Ghareyazie, M., Komatsu, S., Haynes, P.A., & Salekdeh, G.H. 2016. PlantPReS: a database for plant proteome response to stress. Journal of Proteome Research, 143, 69-72. doi: 10.1016/j.jprot.2016.03.009
Muscolo, A., Junker, A., Klukas, C., Weigelt-Fischer, K., Riewe, D., & Altmann, T. 2015. Phenotypic and metabolic responses to drought and salinity of four contrasting lentil accessions. Journal of Experimental Botany, 66, 5467-5480. https://doi.org/10.1093/jxb/erv208
Mykles, D.L., Burnett, K.G., Durica, D.S., Joyce, B.L., McCarthy, F.M., Schmidt, C.J., & Stillman, J.H. 2016. Resources and recommendations for using transcriptomics to address grand challenges in comparative biology. Integrative and Comparative Biology, 56, 1183–1191. https://doi.org/10.1093/icb/icw083
Naika, M., Shameer, K., Mathew, O.K., Gowda, R., & Sowdhamini, R. 2013. STIFDB2: an updated version of plant stress-responsive transcription factor database with additional stress signals, stress-responsive transcription factor binding sites and stress-responsive genes in Arabidopsis and rice. Plant and Cell Physiology, 54(2), 1-15. doi: 10.1093/pcp/pcs185
Nakabayashi, R., & Saito, K. 2015. Integrated metabolomics for abiotic stress responses in plants. Current Opinion in Plant Biology, 24, 10–16. https://doi.org/10.1016/j.pbi.2015.01.003
Naseri, R., Cheghamirza, K., Mohammadi, R., Zarei, L. & Beheshti Aleagha, A. 2023. Localization of QTLs controlling flagleaf and peduncle related traits in durum wheat. Cereal
Biotechnology and Biochemistry, 2 (1), 42-63. (In Persian) doi:10.22126/cbb.2023.8638.1030
Nawaz, M., Iqbal, N., Idrees, S., & Ullah, I. 2014. DREB1A from Oryza sativa var. IR6: homology modelling and molecular docking. Turkish Journal of Botany, 38, 1095-1102. doi: 10.3906/bot-1403-45
Needleman, S.B., & Wunsch, C.D. (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48:443–453Nielsen, M., Lundegaard, C., Lund, O., & Petersen, T.N. 2010. CPHmodels-3.0-remote homology modeling using structure-guided sequence profiles. Nucleic Acids Research, 38, W576-W581. doi: 10.1093/nar/gkq535.
Notredame, C., Higgins, D.G., & Heringa, J. 2000. T-coffee: a novel method for fast and accurate multiple sequence alignment. Journal of Molecular Biology, 302, 205-217. doi: 10.1006/jmbi.2000.4042.
Ohyanagi, H., Takano, T., Terashima, S., Kobayashi, M., Kanno, M., Morimoto, K., et al. 2015. Plant Omics Data Center: an integrated web repository for interspecies gene expression networks with NLP-based curation. Plant Cell Physiology, 56(1), e9. https://doi.org/10.1093/pcp/pcu188
Ong, Q., Nguyen, P., Thao, N. P., & Le, L. 2016. Bioinformatics approach in plant genomic research. Current Genomics, 17(4), 368–378. (In Persian) https://doi.org/10.2174/1389202917666160331202956
Panahi-Fakour, Y., Shabar, Z., Pourabed, A., Qane Golmohammadi, F., & Razavi, S.M. 2014. In silico characterization and expression analysis of SnRK2 family in barley. Crop Biotechnology, 4(12), 25-38 (In Persian) https://dor.isc.ac/dor/20.1001.1.22520783.1394.5.12.3.6
Pandey, M.K., Bentley, A., Desmae, H., Roorkiwal, M., & Varshney, R.K. 2024. Frontier Technologies for Crop Improvement. Springer, p: 276. https://doi.org/10.1007/978-981-99-4673-0_1
Parida, A.K., Panda, A., & Rangani, J. 2018. Metabolomics-guided elucidation of abiotic stress tolerance mechanisms in plants. In: Plant Metabolites and Regulation under Environmental Stress. Academic Press, San Diego, CA, pp: 89-131. https://doi.org/10.3390/metabo11070445
Pasandideh-Arjamand, M., Samizadeh Lahiji, H., Bigloui, M.H., & Mohsenzadeh Golfzani, M. 2023. In silico identification of drought responsive miRNAs target genes in Canola (Brassica napus). Plant Research Journal (Iranian Biology Journal) (Scientific), 36(2), 110-126 (In Persian). https://dor.isc.ac/dor/20.1001.1.23832592.1402.36.2.1.4
Peace, C.P., Bianco, L., Troggio, M., van de Weg, E., Howard, N.P., Cornille, A., Durel, C.-E., Myles, S., Migicovsky, Z., Schaffer, R.J., et al. 2019. Apple whole genome sequences: Recent advances and new prospects. Horticulture Research, 6, Article 59. https://doi.org/10.1038/s41438-019-0141-7
Pearlman, D.A., Case, D.A., Caldwell, J.W., Ross, W.S., Cheatham, T.E., DeBolt, S., Ferguson, D., Seibel, G., & Kollman, P. 1995. AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Computer Physics Communications, 91, 1-41. https://doi.org/10.1016/0010-4655(95)00041-D
pearson, W.R. 1990. Rapid and sensitive sequence comparison with FASTP and FASTA. Methods in Enzymology, 183, 63-98. doi: 10.1016/0076-6879(90)83007-v
Pearson, W.R., & Lipman, D.J. 1988. Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences of the United States of America, 85(8), 2444-2448. doi: 10.1073/pnas.85.8.2444.
Piasecka, A., Kachlicki, P., & Stobiecki, M. 2019. Analytical methods for detection of plant metabolomes changes in response to biotic and abiotic stresses. International Journal of Molecular Sciences, 20, 379. https://doi.org/10.3390/ijms20020379
Platten JD, Cobb JN, Zantua RE (2019) Criteria for evaluating molecular markers: Comprehensive quality metrics to improve marker-assisted selection. PLoS One 14(1):e0210529. https:// doi. org/ 10. 1371/ journ al. pone. 02105 29Prabha, R., Ghosh, I., & Singh, D.P. 2011. Plant stress gene database: a collection of plant genes responding to stress condition. ARPN Journal of Science and Technology, 1, 28–31
Prieto, G., Aloria, K., Osinalde, N., Fullaondo, A., Arizmendi, J.M., & Matthiesen, R. 2012. PAnalyzer: A software tool for protein inference in shotgun proteomics. BMC Bioinformatics, 13, Article 288. https://doi.org/10.1186/1471-2105-13-288
Priya, P., & Jain, M. 2013. RiceSRTFDB: A database of rice transcription factors containing comprehensive expression, cis-regulatory element and mutant information to facilitate gene function analysis. Database, bat027. doi: 10.1093/database/bat027. Print 2013.
Purty, R.S., Sachar, M., & Chatterjee, S. 2017. Structural and expression analysis of salinity stress responsive phosphoserine phosphatase from Brassica juncea (L.). Journal of Proteomics  Bioinformatics, 10, 119-127. doi: 10.4172/jpb.1000432
Rabilloud, T. 2014. How to use 2D gel electrophoresis in plant proteomics. Methods in Molecular Biology, 1072, 43–50. https://doi.org/10.1007/978-1-62703-631-3_4
Rabilloud, T., & Lelong, C. 2011. Two-dimensional gel electrophoresis in proteomics: A tutorial. Journal of Proteomics, 74, 1829–1841. https://doi.org/10.1016/j.jprot.2011.05.040
Redman, J.C., Haas, B.J., Tanimoto, G., & Town, C.D. 2004. Development and evaluation of an Arabidopsis whole genome Affymetrix probe array. Plant Journal, 38, 545561. https://doi.org/10.1111/j.1365-313X.2004.02061.x
Rice, P., Longden, I., & Bleasby, A. 2000. EMBOSS: the European molecular biology open software suite. Trends in Genetics, 16: 276-277. doi: 10.1016/s0168-9525(00)02024-2.
Rodziewicz, P., Swarcewicz, B., Chmielewska, K., Wojakowska, A., & Stobiecki, M. 2014. Influence of abiotic stresses on plant proteome and metabolome changes. Acta Physiologiae Plantarum, 36, 1–9. https://doi.org/10.1007/s11738-013-1402-y
Saed-Moucheshi, A., & Safari, H. 2022. Superoxide Dismutase Enzyme Expression in Root and Shoot of Triticale Seedlings under Drought Stress Conditions. Cereal Biotechnology and Biochemistry, 1(4), 481-495. (In Persian) doi: 10.22126/cbb.2023.8680.1033
Saed-Moucheshi, A., & Safari, H. 2022. Investigation of regulatory elements related to superoxide dismutase enzyme genes in wheat. Cereal Biotechnology and Biochemistry, 2(1), 64-73. (In Persian) doi: 10.22126/cbb.2023.8692.1034
Saed-Moucheshi, A., Sohrabi, F., Fasihfar, E., Baniasadi, F., Riasat, M., & Mozafari, A. A. 2021. Superoxide dismutase (SOD) as a selection criterion for triticale grain yield under drought stress: a comprehensive study on genomics and expression profiling, bioinformatics, heritability, and phenotypic variability. BMC Plant Biol., 21, 148. https://doi.org/10.1186/s12870-021-02919-5
Sahoo, J. P., Sahoo, J., Behera, L., Sharma, S. S., Praveena, J., Nayak, S. K., & Samal, K. C. 2020. Omics studies and systems biology perspective towards abiotic stress response in plants. American Journal of Plant Sciences, 11, 2172–2194. https://doi.org/10.4236/ajps.2020.1112152
Schatz, M. C., Witkowski, J., & McCombie, W. R. 2012. Current challenges in de novo plant genome sequencing and assembly. Genome Biology, 13(4), 243. https://doi.org/10.1186/gb-2012-13-4-243
Schena, M., Shalon, D., Davis, R.W., & Brown, P.O. 1995. Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray. Science, 270, 467–470. https://doi.org/10.1126/science.270.5235
Schwede, T., Kopp, J., Guex, N., & Peitsch, M.C. 2003. SWISS-MODEL: An Automated Protein Homology-Modeling Server. Nucleic Acids Research, 31, 3381–3385. doi: 10.1093/nar/gkg520.
Senjari, S., Shirzadian Khorramabad, R., Shabar, Z., & Shahbazi, M. 2017. Phylogenetic Analysis of NAC Gene Family in Grain Sorghum and Investigation of the Expression Pattern of the Involved Members in Response to Drought Stress. Crop Biotechnology, 7(21), 1-15 (In Persian). https://dor.isc.ac/dor/20.1001.1.22520783.1397.8.21.1.3
Sham, A., & Aly, M.A. 2012. Bioinformatics based comparative analysis of Omega-3 fatty acids in desert plants and their role in stress resistance and tolerance. International Journal of Plant Sciences, 2, 80-89. doi: 10.5923/j.plant.20120203.06
Shameer, K., Ambika, S., Varghese, S.M., Karaba, N., Udayakumar, M., & Sowdhamini, R. 2009. STIFDB: Arabidopsis Stress Responsive Transcription Factor Database. International Journal of Plant Genomics, 2009(1), 583429. doi: 10.1155/2009/583429.
Sharma, V., Munjal, A., & Shanker, A. 2016. A Textbook of Bioinformatics (2nd ed.). Rastogi Publications: Meerut.
Shen, Y., Maupetit, J., Derreumaux, P., & Tufféry, P. 2014. Improved PEP-FOLD Approach for Peptide and Miniprotein Structure Prediction. Journal of Chemical Theory and Computation, 10, 4745-4758. doi: 10.1021/ct500592m
Silva-Sanchez, C., Li, H., & Chen, S. 2015. Recent Advances and Challenges in Plant Phospho-Proteomics. Proteomics, 15, 1127–1141. https://doi.org/10.1002/pmic.201400410
Singh, B., Mehta, S., Tiwari, M., & Bhatia, S. 2018. Legume Breeding for Fungal Resistance: A Lesson to Learn in Molecular Approaches for Plant Improvement. Kalpaz Publication, New Delhi. http://dx.doi.org/10.1007/s10681-011-0367-4
Smita, S., Lenka, S.K., Katiyar, A., Jaiswal, P., Preece, J., & Bansal, K.C. 2011. QlicRice: A Web Interface for Abiotic Stress Responsive QTL and Loci Interaction Channels in Rice. Database, 2011: bar037. doi: 10.1093/database/bar037.
Smith, K. 2013. A Brief History of NCBI’s Formation and Growth. The NCBI Handbook, 4p.
Smith, T.F. 1990. The History of the Genetic Sequence Databases. Genomics, 6(4), 701-707.
Smith, T.F., & Waterman, M.S. 1981. Identification of Common Molecular Subsequences. Journal of Molecular Biology, 147, 195-197. https://doi.org/10.1016/0022-2836(81)90087-5
Sohrabi, F., & Saed-Moucheshi, A. 2023. A review on biological roles of long non-coding RNAs (LncRNAs) in plants: A focus on cereal crops. Cereal Biotechnology and Biochemistry, 2(4),  481-501. (In Persian) doi: 10.22126/cbb.2024.9699.1055
Strasser, B.J. 2008. GenBank—Natural History in the 21st Century? Science, 322(5901), 537-538. DOI: 10.1126/science.1163399
Tan, Y. C., Kumar, A. U., Wong, Y. P., & Ling, A. P. K. 2022. Bioinformatics approaches and applications in plant biotechnology. Journal of Genetic Engineering and Biotechnology, 20(1), 106. https://doi.org/10.1186/s43141-022-00394-5
Tang G, Qin J, Dolnikowski GG, Russell RM, Grusak MA (2009) Golden Rice is an effective source of vitamin A. Am J Clin Nutr 89(6):1776–1783. https:// doi. org/ 10. 3945/ ajcn. 2008. 27119
Tapprich, W.E., Reichart, L., Simon, D.M., Duncan, G., McClung, W., Grandgenett, N., & Pauley, M.A. 2021. An Instructional Definition and Assessment Rubric for Bioinformatics Instruction. Biochemistry and Molecular Biology Education, 49(1), 38-45. doi: 10.1002/bmb.21361
Thompson, J.D., Higgins, D.G., & Gibson, T.J. 1994. CLUSTAL W: Improving the Sensitivity of Progressive Multiple Sequence Alignment Through Sequence Weighting, Position-Specific Gap Penalties and Weight Matrix Choice. Nucleic Acids Research, 22, 4673-4680. doi: 10.1093/nar/22.22.4673.
Tohge, T., & Fernie, A.R. 2015. Metabolomics-Inspired Insight into Developmental, Environmental and Genetic Aspects of Tomato Fruit Chemical Composition and Quality. Plant Cell Physiology, 56, 1681-1696. https://doi.org/10.1093/pcp/pcv093
Tranchida-Lombardo, V., Aiese Cigliano, R., Anzar, I., Landi, S., Palombieri, S., Colantuono, C., Bostan, H., Termolino, P., Aversano, R., Batelli, G., et al. 2018. Whole-Genome Re-Sequencing of Two Italian Tomato Landraces Reveals Sequence Variations in Genes Associated with Stress Tolerance, Fruit Quality and Long Shelf-Life Traits. DNA Research, 25, 149-160. https://doi.org/10.1093/dnares/dsx045
Trott, O., & Olson, A.J. 2010. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. Journal of Computational Chemistry, 31, 455-461. doi: 10.1002/jcc.21334
Wang, J., Hu, T., Wang, W., Hu, H., Wei, Q., Wei, X., & Bao, C. 2019. Bioinformatics Analysis of the Lipoxygenase Gene Family in Radish (Raphanus sativus) and Functional Characterization in Response to Abiotic and Biotic Stresses. International Journal of Molecular Sciences, 20(23), 6095. doi: 10.3390/ijms20236095
Wang, L., Guo, Z., Zhang, Y., Wang, Y., Yang, G., Yang, L., Wang, R., & Xie, Z. 2017. Characterization of LhSorP5CS, a Gene Catalyzing Proline Synthesis in Oriental Hybrid Lily Sorbonne: Molecular Modelling and Expression Analysis. Botanical Studies, 58, 1-8. https://doi.org/10.1186/s40529-017-0163-0
Wang, W.W., Zheng, C., Hao, W.J., Ma, C.L., Ma, J.Q., Ni, D.J., & Chen, L. 2018. Transcriptome and Metabolome Analysis Reveal Candidate Genes and Biochemicals Involved in Tea Geometrid Defense in Camellia sinensis. PLoS ONE, 13, e0201670. https://doi.org/10.1371/journal.pone.0201670
Wang, Y., Hu, Z., Shang, D., Xue, Y., Islam, A.T., & Chen, S. 2020. Effects of Warming and Elevated O3 Concentrations on N2O Emission and Soil Nitrification and Denitrification Rates in a Wheat-Soybean Rotation Cropland. Environmental Pollution, 257, 113556. https://doi.org/10.1016/j.envpol.2019.113556
Wang, Z., Gerstein, M., & Snyder, M. 2009. RNA-Seq: A Revolutionary Tool for Transcriptomics. Nature Reviews Genetics, 10, 57-63. https://doi.org/10.1038/nrg2484
Webb, B., & Sali, A. 2014. Protein Structure Modeling with MODELLER. Protein Structure Prediction 2014: 1-15. doi: 10.1007/978-1-4939-0366-5_1.
Wheeler, D.L., Barrett, T., Benson, D.A., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Edgar, R., Federhen, S., & Feolo, M. 2007. Database Resources of the National Center for Biotechnology Information. Nucleic Acids Research, 36, D13-D21. doi: 10.1093/nar/gkaa892.
Wickett, N.J., Mirarab, S., Nguyen, N., Warnow, T., Carpenter, E., Matasci, N., Ayyampalayam, S., Barker, M.S., Burleigh, J.G., Gitzendanner, M.A., et al. 2014. Phylotranscriptomic Analysis of the Origin and Early Diversification of Land Plants. Proceedings of the National Academy of Sciences USA, 111, E4859–E4868. https://doi.org/10.1073/pnas.1323926111
Wong, D.C.J. 2019. Harnessing Integrated Omics Approaches for Plant Specialized Metabolism Research: New Insights into Shikonin Biosynthesis. Plant Cell Physiology, 60, 4-6. https://doi.org/10.1093/pcp/pcy230
Wu, S., & Zhang, Y. 2007. LOMETS: A Local Meta-Threading-Server for Protein Structure Prediction. Nucleic Acids Research, 35, 3375-3382. doi: 10.1093/nar/gkm251
Xiao, B.Z., Chen, X., Xiang, C.B., Tang, N., Zhang, Q.F., Xiong, L.Z. 2009. Evaluation of seven functionknown candidate genes for their effects on improving drought resistance of transgenic rice under field conditions. Mol Plant. 2(1), 73-83. https://doi.org/10.1093/mp/ssn068
Xiong, L., & Zhu, J.K. 2002. Molecular and Genetic Aspects of Plant Responses to Osmotic Stress. Plant Cell and Environment, 25, 131-139. doi: 10.1046/j.1365-3040.2002.00782.x
Xiong, L., Schumaker, K.S., & Zhu, J.K. 2002. Cell Signaling during Cold, Drought, and Salt Stress. Plant Cell, 14, S165. https://doi.org/10.1105/tpc.000596
Yang, H., Zhang, Q., Zhong, S., Yang, H., Ren, T., Chen, C.,  Luo, P. 2023. Genome-Wide Identification of Superoxide Dismutase and Expression in Response to Fruit Development and Biological Stress in Akebia trifoliata: A Bioinformatics Study. Antioxidants, 12(3), 726. https://doi.org/10.3390/antiox12030726
Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. 2015. The I-TASSER Suite: Protein Structure and Function Prediction. Nature Methods, 12, 7-8. https://doi.org/10.1038/nmeth.3213
Yang, J.M.,  Chen, C.C. 2004. GEMDOCK: A Generic Evolutionary Method for Molecular Docking. Proteins: Structure, Function, and Bioinformatics, 55, 288-304. doi: 10.1002/prot.20035.
Yang, Y., & Guo, Y. 2018. Elucidating the Molecular Mechanisms Mediating Plant Salt-Stress Responses. New Phytologist, 217, 523–539. https://doi.org/10.1111/nph.14920
Zhang, F., Ge, W., Ruan, G., Cai, X., & Guo, T. 2020. Data-Independent Acquisition Mass Spectrometry-Based Proteomics and Software Tools: A Glimpse in 2020. Proteomics, 1900276. doi: 10.1002/pmic.201900276.
Zhang, H., Zhao, Y., & Zhu, J.-K. 2020. Thriving under stress: How plants balance growth and the stress response. Developmental Cell, 55, 529–543. https://doi.org/10.1016/j.devcel.2020.10.012
Zhang, H., Zhu, J., Gong, Z., & Zhu, J.-K. 2022. Abiotic stress responses in plants. Nature Reviews Genetics, 23, 104–119. https://doi.org/10.1038/s41576-021-00413-0
Zhang, S., Yue, Y., Sheng, L., Wu, Y., Fan, G., Li, A., Hu, X., Shang Guan, M., & Wei, C. 2013. PASmiR: A Literature Curated Database for miRNA Molecular Regulation in Plant Response to Abiotic Stress. BMC Plant Biology, 13, 1-8. doi: 10.1186/1471-2229-13-33.
Zhang, X., Ibrahim, Z., Khaskheli, M.B., Raza, H., Zhou, F., & Shamsi, I.H. 2024. Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications. Sustainability, 16(17), 7651. https://doi.org/10.3390/su16177651
Zhu, J.K. 2016. Abiotic Stress Signaling and Responses in Plants. Cell, 167(2), 313-324. doi: 10.1016/j.cell.2016.08.029