Advertisement

Databases: A Weapon from the Arsenal of Bioinformatics for Plant Abiotic Stress Research

  • Anamika
  • Sahil Mehta
  • Baljinder Singh
  • Anupam Patra
  • Md. Aminul Islam
Chapter

Abstract

Plants are an essential part of every food chain on earth. In addition, the humans utterly rely on plants for their every single need including food, shelter, oils, drugs, dyes, flavors, perfumes, etc. Due to the perpetually increasing population, the burden is increasing at an alarming rate. The whole scenario is worsened by changing climatic conditions, overexploitation of natural resources, and deforestation. Due to the combination of all these factors, there is a serious level of pressure which enforces stress on the plants. As a result, abiotic stresses including flooding, drought, heat shock, cold stress, etc. majority hampers crop productivity. Similar to the overall productivity of crops in the post-genomic era, the rates for various types of sequencing, MS analysis, and metabolite profiling have also fallen down. As a result, several genes, proteins, and metabolites which play role in stress tolerance have been identified and annotated. This flow of information with respect to abiotic stress research has resulted in a number of databases for different omics approaches including genomics, proteomics, miRNAomics, transcriptomics, metabolomics, etc. These various technologies provide a holistic picture of stress responses and hence provide a way to better strategies for the current situation challenges. In this book chapter, we have highlighted various useful databases available to the crop scientists and breeders.

Keywords

Plants Abiotic stress Productivity Omics Bioinformatics Databases 

References

  1. Abdurakhmonov IY (2016) Genomics era for plants and crop species–advances made and needed tasks ahead. In: Plant genomics. InTech, CroatiaCrossRefGoogle Scholar
  2. Abola EE, Bernstein FC, Koetzle TF (1984) The protein data bank. In: Neutrons in biology. Springer, Boston, MA, pp 441–441CrossRefGoogle Scholar
  3. Afendi FM, Okada T, Yamazaki M, Hirai-Morita A, Nakamura Y, Nakamura K et al (2011) KNApSAcK family databases: integrated metabolite–plant species databases for multifaceted plant research. Plant Cell Physiol 53:e1PubMedCrossRefPubMedCentralGoogle Scholar
  4. Agrawal GK, Pedreschi R, Barkla BJ, Bindschedler LV, Cramer R, Sarkar A et al (2012) Translational plant proteomics: a perspective. J Proteome 75:4588–4601CrossRefGoogle Scholar
  5. Akiyama K, Chikayama E, Yuasa H, Shimada Y, Tohge T, Shinozaki K et al (2008) PRIMe: a web site that assembles tools for metabolomics and transcriptomics. In Silico Biol 8:339–345PubMedPubMedCentralGoogle Scholar
  6. Alaux M, Rogers J, Letellier T, Flores R, Alfama F, Pommier C et al (2018) Linking the international wheat genome sequencing consortium bread wheat reference genome sequence to wheat genetic and phenomic data. Genome Biol 19:111PubMedPubMedCentralCrossRefGoogle Scholar
  7. Alter S, Bader KC, Spannagl M, Wang Y, Bauer E, Schön C-C et al (2015) Drought DB: an expert-curated compilation of plant drought stress genes and their homologs in nine species. Database 2015:bav046.  https://doi.org/10.1093/database/bav1046CrossRefPubMedPubMedCentralGoogle Scholar
  8. Altpeter F, Springer NM, Bartley LE, Blechl AE, Brutnell TP, Citovsky V et al (2016) Advancing crop transformation in the era of genome editing. Plant Cell 28:1510–1520PubMedPubMedCentralGoogle Scholar
  9. Amâncio S, Gerós H, Dietz K-J, Blumwald E (2017) The use of systems biology for enhancing crop abiotic stress tolerance. Front Plant SciGoogle Scholar
  10. Arivaradarajan P, Misra G (2019) Omics approaches, technologies and applications: integrative approaches for understanding OMICS data. Springer, SingaporeGoogle Scholar
  11. Atkins P, Bowler I (2016) Food in society: economy, culture, geography. Routledge, LondonGoogle Scholar
  12. Atkinson NJ, Urwin PE (2012) The interaction of plant biotic and abiotic stresses: from genes to the field. J Exp Bot 63:3523–3543PubMedCrossRefPubMedCentralGoogle Scholar
  13. Bagati S, Mahajan R, Nazir M, Dar AA, Zargar SM (2018) “Omics”: a gateway towards abiotic stress tolerance. In: Abiotic stress-mediated sensing and signaling in plants: an omics perspective. Springer, Singapore, pp 1–45Google Scholar
  14. Barlett PF (2016) Agricultural decision making: anthropological contributions to rural development. Academic Press, Cambridge, MAGoogle Scholar
  15. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C et al (2006) NCBI GEO: mining tens of millions of expression profiles—database and tools update. Nucleic Acids Res 35:D760–D765PubMedPubMedCentralCrossRefGoogle Scholar
  16. Bauer E, Schmutzer T, Barilar I, Mascher M, Gundlach H, Martis MM et al (2017) Towards a whole-genome sequence for rye (Secale cereale L.). Plant J 89:853–869PubMedCrossRefPubMedCentralGoogle Scholar
  17. Baxevanis AD, Bateman A (2015) The importance of biological databases in biological discovery. Curr Protoc Bioinformatics 50:1.1.1–1.1.8CrossRefGoogle Scholar
  18. Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377PubMedPubMedCentralCrossRefGoogle Scholar
  19. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2004) GenBank: update. Nucleic Acids Res 32:D23–D26PubMedPubMedCentralCrossRefGoogle Scholar
  20. Bilofsky HS, Burks C, Fickett JW, Goad WB, Lewitter FI, Rindone WP et al (1986) The GenBank genetic sequence databank. Nucleic Acids Res 14:1–4PubMedPubMedCentralCrossRefGoogle Scholar
  21. Bolser D, Staines DM, Pritchard E, Kersey P (2016) Ensembl plants: integrating tools for visualizing, mining, and analyzing plant genomics data. In: Plant bioinformatics. Springer, New York, NY, pp 115–140CrossRefGoogle Scholar
  22. Bonnet E, He Y, Billiau K, Van de Peer Y (2010) TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics 26:1566–1568PubMedCrossRefPubMedCentralGoogle Scholar
  23. Bonthala V, Gajula M (2016) PvTFDB: a Phaseolus vulgaris transcription factors database for expediting functional genomics in legumes. Database 2016:baw114PubMedPubMedCentralCrossRefGoogle Scholar
  24. Borkotoky S, Saravanan V, Jaiswal A, Das B, Selvaraj S, Murali A et al (2013) The Arabidopsis stress responsive gene database. Int J Plant Genom 2013:949564.  https://doi.org/10.1155/2013/949564CrossRefGoogle Scholar
  25. Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A (2007) Uniprotkb/swiss-prot. In: Plant bioinformatics. Springer, New York, NY, pp 89–112CrossRefGoogle Scholar
  26. Bowne J, Bacic A, Tester M, Roessner U (2018) Abiotic stress and metabolomics. Annual Plant Rev 43:61–85CrossRefGoogle Scholar
  27. Brown JW, Echeverria M, Qu L-H, Lowe TM, Bachellerie J-P, Hüttenhofer A et al (2003) Plant snoRNA database. Nucleic Acids Res 31:432–435PubMedPubMedCentralCrossRefGoogle Scholar
  28. Brown JW, Shaw PJ, Shaw P, Marshall DF (2005) Arabidopsis nucleolar protein database (AtNoPDB). Nucleic Acids Res 33:D633–D636PubMedCrossRefPubMedCentralGoogle Scholar
  29. Brun M, Blanc P, Otto H (2016) Global perspective of natural resources. Ciheam. Zero waste in the mediterranean, Natural Resources, Food and Knowledge, Presses de SciencesPo, pp 1–48Google Scholar
  30. Burks C (2018) The flow of nucleotide sequence data into data banks: role and impact of large-scale sequencing projects. In: Computers and DNA. Routledge, London, pp 35–45CrossRefGoogle Scholar
  31. Camon E, Barrell D, Lee V, Dimmer E, Apweiler R (2003) The gene ontology annotation (GOA) database-an integrated resource of GO annotations to the UniProt knowledgebase. In Silico Biol 4:5–6PubMedPubMedCentralGoogle Scholar
  32. Chawla K, Barah P, Kuiper M, Bones AM (2011) Systems biology: a promising tool to study abiotic stress responses. In: Tuteja N (ed) Omics and plant abiotic stress tolerance, pp 163–172CrossRefGoogle Scholar
  33. Chen D, Yuan C, Zhang J, Zhang Z, Bai L, Meng Y et al (2011) PlantNATsDB: a comprehensive database of plant natural antisense transcripts. Nucleic Acids Res 40:D1187–D1193PubMedPubMedCentralCrossRefGoogle Scholar
  34. Chen J, Hu Q, Zhang Y, Lu C, Kuang H (2013) P-MITE: a database for plant miniature inverted-repeat transposable elements. Nucleic Acids Res 42:D1176–D1181PubMedPubMedCentralCrossRefGoogle Scholar
  35. Chen C, Huang H, Wu CH (2017) Protein bioinformatics databases and resources. In: Protein bioinformatics. Springer, New York, NY, pp 3–39CrossRefGoogle Scholar
  36. Chen F, Dong W, Zhang J, Guo X, Chen J, Wang Z et al (2018) The sequenced angiosperm genomes and genome databases. Front Plant Sci 9:418PubMedPubMedCentralCrossRefGoogle Scholar
  37. Chérel I, Gaillard I (2019) The complex fine-tuning of k+ fluxes in plants in relation to osmotic and ionic abiotic stresses. Int J Mol Sci 20:715PubMedCentralCrossRefGoogle Scholar
  38. Chien C-H, Chow C-N, Wu N-Y, Chiang-Hsieh Y-F, Hou P-F, Chang W-C (2015) EXPath: a database of comparative expression analysis inferring metabolic pathways for plants. BMC Genomics 16:S6PubMedPubMedCentralCrossRefGoogle Scholar
  39. Choudhury FK, Rivero RM, Blumwald E, Mittler R (2017) Reactive oxygen species, abiotic stress and stress combination. Plant J 90:856–867PubMedCrossRefPubMedCentralGoogle Scholar
  40. Conijn JG, Bindraban PS, Schröder JJ, Jongschaap REE (2018) Can our global food system meet food demand within planetary boundaries? Agric Ecosyst Environ 251:244–256CrossRefGoogle Scholar
  41. Consortium U (2014) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212CrossRefGoogle Scholar
  42. Cooper L, Meier A, Laporte M-A, Elser JL, Mungall C, Sinn BT et al (2017) The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics. Nucleic Acids Res 46:D1168–D1180PubMedCentralCrossRefGoogle Scholar
  43. Cramer GR, Urano K, Delrot S, Pezzotti M, Shinozaki K (2011) Effects of abiotic stress on plants: a systems biology perspective. BMC Plant Biol 11:163PubMedPubMedCentralCrossRefGoogle Scholar
  44. Cseke LJ, Kirakosyan A, Kaufman PB, Warber S, Duke JA, Brielmann HL (2016) Natural products from plants. CRC Press, Boca Raton, FLCrossRefGoogle Scholar
  45. Das NN (2019) Relevance of poly-omics in system biology studies of industrial crops. OMICS-Based Approaches in Plant Biotechnology 167:167Google Scholar
  46. Davuluri RV, Sun H, Palaniswamy SK, Matthews N, Molina C, Kurtz M et al (2003) AGRIS: Arabidopsis gene regulatory information server, an information resource of Arabidopsis cis-regulatory elements and transcription factors. BMC Bioinformat 4:25CrossRefGoogle Scholar
  47. Debnath M, Pandey M, Bisen P (2011) An omics approach to understand the plant abiotic stress. OMICS 15:739–762PubMedCrossRefPubMedCentralGoogle Scholar
  48. Degtyarenko K, De Matos P, Ennis M, Hastings J, Zbinden M, McNaught A et al (2007) ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 36:D344–D350PubMedPubMedCentralCrossRefGoogle Scholar
  49. Dereeper A, Bocs S, Rouard M, Guignon V, Ravel S, Tranchant-Dubreuil C et al (2014) The coffee genome hub: a resource for coffee genomes. Nucleic Acids Res 43:D1028–D1035PubMedPubMedCentralCrossRefGoogle Scholar
  50. Di Silvestre D, Bergamaschi A, Bellini E, Mauri P (2018) Large scale proteomic data and network-based systems biology approaches to explore the plant world. Proteome 6:27CrossRefGoogle Scholar
  51. Doolittle RF (2018) What we have learned and will learn from sequence databases. In: Computers and DNA. Routledge, London, pp 21–31CrossRefGoogle Scholar
  52. dos Reis SP, Marques DN, Barros NLF, Costa CNM, de Souza CRB (2018) Genetically engineered food crops to abiotic stress tolerance. In: Genetically engineered foods. Elsevier, Amsterdam, pp 247–279CrossRefGoogle Scholar
  53. Dunn WB, Ellis DI (2005) Metabolomics: current analytical platforms and methodologies. TrAC Trend Analyt Chem 24:285–294CrossRefGoogle Scholar
  54. El-Metwally S, Ouda OM, Helmy M (2014) First-and next-generations sequencing methods. Springer, New York, NYCrossRefGoogle Scholar
  55. Fahimirad S, Ghorbanpour M (2019) Omics approaches in developing abiotic stress tolerance in rice (Oryza sativa L.). In: Advances in rice research for abiotic stress tolerance. Elsevier, Amsterdam, pp 767–779CrossRefGoogle Scholar
  56. Fan K, Zhang Q, Liu M, Ma L, Shi Y, Ruan J (2019) Metabolomic and transcriptional analyses reveal the mechanism of C, N allocation from source leaf to flower in tea plant (Camellia sinensis. L). J Plant Physiol 232:200–208PubMedCrossRefPubMedCentralGoogle Scholar
  57. Fiehn O (2002) Metabolomics—the link between genotypes and phenotypes. In: Functional genomics. Springer, New York, NY, pp 155–171CrossRefGoogle Scholar
  58. Fredslund J (2008) DATFAP: a database of primers and homology alignments for transcription factors from 13 plant species. BMC Genomics 9:140PubMedPubMedCentralCrossRefGoogle Scholar
  59. Furbank RT, Tester M (2011) Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644PubMedCrossRefPubMedCentralGoogle Scholar
  60. Gao J, Agrawal GK, Thelen JJ, Xu D (2008) P3DB: a plant protein phosphorylation database. Nucleic Acids Res 37:D960–D962PubMedPubMedCentralCrossRefGoogle Scholar
  61. Garnatje T, Canela MÁ, Garcia S, Hidalgo O, Pellicer J, Sánchez-Jiménez I et al (2011) GSAD: a genome size in the Asteraceae database. Cytometry A 79:401–404PubMedCrossRefPubMedCentralGoogle Scholar
  62. Gendler K, Paulsen T, Napoli C (2007) ChromDB: the chromatin database. Nucleic Acids Res 36:D298–D302PubMedPubMedCentralCrossRefGoogle Scholar
  63. Ghosh A, Mehta A (2017) Concept, development, and application of computational methods for the analysis and integration of omics data. In: Plant bioinformatics. Springer, New York, NY, pp 241–266CrossRefGoogle Scholar
  64. Ghosh D, Xu J (2014) Abiotic stress responses in plant roots: a proteomics perspective. Front Plant Sci 5:6PubMedPubMedCentralCrossRefGoogle Scholar
  65. Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J et al (2011) Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res 40:D1178–D1186PubMedPubMedCentralCrossRefGoogle Scholar
  66. Grafton RQ, Daugbjerg C, Qureshi ME (2015) Towards food security by 2050. Food Security 7:179–183CrossRefGoogle Scholar
  67. Grover A, Pareek A, Singla SL, Minhas D, Katiyar S, Ghawana S et al (1998) Engineering crops for tolerance against abiotic stresses through gene manipulation. Curr Sci 75:689–696Google Scholar
  68. Gupta B, Sengupta A, Saha J, Gupta K (2013) Plant abiotic stress: ‘Omics’ approach. J Plant Biochem Physiol 1:1–3Google Scholar
  69. Gurjar AKS, Panwar AS, Gupta R, Mantri SS (2016) PmiRExAt: plant miRNA expression atlas database and web applications. Database 2016:baw060.  https://doi.org/10.1093/database/baw1060CrossRefPubMedPubMedCentralGoogle Scholar
  70. Hamilton JP, Robin Buell C (2012) Advances in plant genome sequencing. Plant J 70:177–190PubMedCrossRefPubMedCentralGoogle Scholar
  71. Heazlewood JL, Durek P, Hummel J, Selbig J, Weckwerth W, Walther D et al (2007) PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor. Nucleic Acids Res 36:D1015–D1021PubMedPubMedCentralCrossRefGoogle Scholar
  72. Helmy M, Tomita M, Ishihama Y (2011) OryzaPG-DB: rice proteome database based on shotgun proteogenomics. BMC Plant Biol 11:63PubMedPubMedCentralCrossRefGoogle Scholar
  73. Helmy M, Crits-Christoph A, Bader GD (2016) Ten simple rules for developing public biological databases. PLoS Comput Biol 12(11):e1005128PubMedPubMedCentralCrossRefGoogle Scholar
  74. Heyman HM, Dubery IA (2016) The potential of mass spectrometry imaging in plant metabolomics: a review. Phytochem Rev 15:297–316CrossRefGoogle Scholar
  75. Hivrale V, Zheng Y, Puli COR, Jagadeeswaran G, Gowdu K, Kakani VG et al (2016) Characterization of drought-and heat-responsive microRNAs in switchgrass. Plant Sci 242:214–223PubMedCrossRefGoogle Scholar
  76. Hong J, Yang L, Zhang D, Shi J (2016) Plant metabolomics: an indispensable system biology tool for plant science. Int J Mol Sci 17:767PubMedCentralCrossRefGoogle Scholar
  77. Hooper CM, Castleden IR, Tanz SK, Aryamanesh N, Millar AH (2016) SUBA4: the interactive data analysis Centre for Arabidopsis subcellular protein locations. Nucleic Acids Res 45:D1064–D1074PubMedPubMedCentralCrossRefGoogle Scholar
  78. Hossain MA, Li Z-G, Hoque TS, Burritt DJ, Fujita M, Munné-Bosch S (2018) Heat or cold priming-induced cross-tolerance to abiotic stresses in plants: key regulators and possible mechanisms. Protoplasma 255:399–412PubMedCrossRefPubMedCentralGoogle Scholar
  79. Hu J, Rampitsch C, Bykova NV (2015) Advances in plant proteomics toward improvement of crop productivity and stress resistancex. Front Plant Sci 6:209PubMedPubMedCentralGoogle Scholar
  80. Hu H, Scheben A, Edwards D (2018) Advances in integrating genomics and bioinformatics in the plant breeding pipeline. Agriculture 8:75CrossRefGoogle Scholar
  81. Huala E, Dickerman AW, Garcia-Hernandez M, Weems D, Reiser L, LaFond F et al (2001) The Arabidopsis information resource (TAIR): a comprehensive database and web-based information retrieval, analysis, and visualization system for a model plant. Nucleic Acids Res 29:102–105PubMedPubMedCentralCrossRefGoogle Scholar
  82. Iida K, Seki M, Sakurai T, Satou M, Akiyama K, Toyoda T et al (2005) RARTF: database and tools for complete sets of Arabidopsis transcription factors. DNA Res 12:247–256PubMedCrossRefPubMedCentralGoogle Scholar
  83. International Arabidopsis Informatics Consortium, Doherty C, Friesner J, Gregory B, Loraine A, Megraw M et al (2019) Arabidopsis bioinformatics resources: the current state, challenges, and priorities for the future. Plant Direct 3:e00109CrossRefGoogle Scholar
  84. Jain M, Olsen HE, Paten B, Akeson M (2016) The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol 17:239PubMedPubMedCentralCrossRefGoogle Scholar
  85. Jin J, Zhang H, Kong L, Gao G, Luo J (2013) PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res 42:D1182–D1187PubMedPubMedCentralCrossRefGoogle Scholar
  86. Johnson C, Bowman L, Adai AT, Vance V, Sundaresan V (2006) CSRDB: a small RNA integrated database and browser resource for cereals. Nucleic Acids Res 35:D829–D833PubMedPubMedCentralCrossRefGoogle Scholar
  87. Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17:451–459PubMedPubMedCentralCrossRefGoogle Scholar
  88. Jorge TF, Rodrigues JA, Caldana C, Schmidt R, van Dongen JT, Thomas-Oates J et al (2016) Mass spectrometry-based plant metabolomics: metabolite responses to abiotic stress. Mass Spectrom Rev 35:620–649PubMedCrossRefPubMedCentralGoogle Scholar
  89. Joshi HJ, Hirsch-Hoffmann M, Baerenfaller K, Gruissem W, Baginsky S, Schmidt R et al (2011) MASCP gator: an aggregation portal for the visualization of Arabidopsis proteomics data. Plant Physiol 155:259–270PubMedCrossRefPubMedCentralGoogle Scholar
  90. Jung S, Jesudurai C, Staton M, Du Z, Ficklin S, Cho I et al (2004) GDR (genome database for Rosaceae): integrated web resources for Rosaceae genomics and genetics research. BMC Bioinformat 5:130CrossRefGoogle Scholar
  91. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M (2013) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42:D199–D205PubMedPubMedCentralCrossRefGoogle Scholar
  92. Kera K, Fine DD, Wherritt DJ, Nagashima Y, Shimada N, Ara T et al (2018) Pathway-specific metabolome analysis with 18O2-labeled Medicago truncatula via a mass spectrometry-based approach. Metabolomics 14:71PubMedPubMedCentralCrossRefGoogle Scholar
  93. Kersey PJ (2019) Plant genome sequences: past, present, future. Curr Opin Plant Biol 48:1–8PubMedCrossRefPubMedCentralGoogle Scholar
  94. Kim E, Hwang S, Lee I (2016) SoyNet: a database of co-functional networks for soybean Glycine max. Nucleic Acids Res 45:D1082–D1089PubMedPubMedCentralCrossRefGoogle Scholar
  95. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S et al (2018) PubChem 2019 update: improved access to chemical data. Nucleic Acids Res 47:D1102–D1109PubMedCentralCrossRefGoogle Scholar
  96. Kodama Y, Mashima J, Kosuge T, Kaminuma E, Ogasawara O, Okubo K et al (2017) DNA data bank of Japan: 30th anniversary. Nucleic Acids Res 46:D30–D35PubMedCentralCrossRefGoogle Scholar
  97. Kosová K, Vítámvás P, Prášil IT, Renaut J (2011) Plant proteome changes under abiotic stress—contribution of proteomics studies to understanding plant stress response. J Proteome 74:1301–1322CrossRefGoogle Scholar
  98. Kosová K, Vítámvás P, Urban MO, Prášil IT, Renaut J (2018) Plant abiotic stress proteomics: the major factors determining alterations in cellular proteome. Front Plant Sci 9:122PubMedPubMedCentralCrossRefGoogle Scholar
  99. Kreszies T, Shellakkutti N, Osthoff A, Yu P, Baldauf JA, Zeisler-Diehl VV et al (2019) Osmotic stress enhances suberization of apoplastic barriers in barley seminal roots: analysis of chemical, transcriptomic and physiological responses. New Phytol 221:180–194PubMedCrossRefPubMedCentralGoogle Scholar
  100. Kudo T, Akiyama K, Kojima M, Makita N, Sakurai T, Sakakibara H (2013) UniVIO: a multiple omics database with hormonome and transcriptome data from rice. Plant Cell Physiol 54:e9–e9PubMedPubMedCentralCrossRefGoogle Scholar
  101. Kudo T, Terashima S, Takaki Y, Tomita K, Saito M, Kanno M et al (2017) PlantExpress: a database integrating OryzaExpress and ArthaExpress for single-species and cross-species gene expression network analyses with microarray-based transcriptome data. Plant Cell Physiol 58:e1PubMedCrossRefPubMedCentralGoogle Scholar
  102. Kudoh H (2016) Molecular phenology in plants: in natura systems biology for the comprehensive understanding of seasonal responses under natural environments. New Phytol 210:399–412PubMedCrossRefPubMedCentralGoogle Scholar
  103. Kumar S, Shanker A (2018) Bioinformatics resources for the stress biology of plants. In: Biotic and abiotic stress tolerance in plants. Springer, Singapore, pp 367–386CrossRefGoogle Scholar
  104. Kumar SA, Kumari PH, Sundararajan VS, Suravajhala P, Kanagasabai R, Kishor PK (2014) PSPDB: plant stress protein database. Plant Mol Biol Report 32:940–942CrossRefGoogle Scholar
  105. Kumar J, Pratap A, Kumar S (2015) Phenomics in crop plants: trends, options and limitations. Springer, IndiaGoogle Scholar
  106. Künne C, Lange M, Funke T, Miehe H, Thiel T, Grosse I et al (2005) CR-EST: a resource for crop ESTs. Nucleic Acids Res 33:D619–D621PubMedCrossRefPubMedCentralGoogle Scholar
  107. Kushwaha UKS, Deo I, Jaiswal JP, Prasad B (2017) Role of bioinformatics in crop improvement. Global J Sci Front Res D 17:1–13Google Scholar
  108. Lai K, Lorenc MT, Edwards D (2012) Genomic databases for crop improvement. Agronomy 2:62–73CrossRefGoogle Scholar
  109. Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R et al (2011) The arabidopsis information resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res 40:D1202–D1210PubMedPubMedCentralCrossRefGoogle Scholar
  110. Lavarenne J, Guyomarc’h S, Sallaud C, Gantet P, Lucas M (2018) The spring of systems biology-driven breeding. Trends Plant Sci 23:706–720PubMedCrossRefPubMedCentralGoogle Scholar
  111. Lawrence CJ, Dong Q, Polacco ML, Seigfried TE, Brendel V (2004) MaizeGDB, the community database for maize genetics and genomics. Nucleic Acids Res 32:D393–D397PubMedPubMedCentralCrossRefGoogle Scholar
  112. Lee T-H, Tang H, Wang X, Paterson AH (2012) PGDD: a database of gene and genome duplication in plants. Nucleic Acids Res 41:D1152–D1158PubMedPubMedCentralCrossRefGoogle Scholar
  113. Lee T, Yang S, Kim E, Ko Y, Hwang S, Shin J et al (2014) AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species. Nucleic Acids Res 43:D996–D1002PubMedPubMedCentralCrossRefGoogle Scholar
  114. Lee T, Oh T, Yang S, Shin J, Hwang S, Kim CY et al (2015) RiceNet v2: an improved network prioritization server for rice genes. Nucleic Acids Res 43:W122–W127PubMedPubMedCentralCrossRefGoogle Scholar
  115. Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tárraga A, Cheng Y et al (2010) The European nucleotide archive. Nucleic Acids Res 39:D28–D31PubMedPubMedCentralCrossRefGoogle Scholar
  116. Leisner CP, Yendrek CR, Ainsworth EA (2017) Physiological and transcriptomic responses in the seed coat of field-grown soybean (Glycine max L. Merr.) to abiotic stress. BMC Plant Biol 17:242.  https://doi.org/10.1186/s12870-12017-11188-yCrossRefPubMedPubMedCentralGoogle Scholar
  117. Letunic I, Bork P (2017) 20 years of the SMART protein domain annotation resource. Nucleic Acids Res 46:D493–D496PubMedCentralCrossRefGoogle Scholar
  118. Li J, Dai X, Liu T, Zhao PX (2011) LegumeIP: an integrative database for comparative genomics and transcriptomics of model legumes. Nucleic Acids Res 40:D1221–D1229PubMedPubMedCentralCrossRefGoogle Scholar
  119. Li M, Xia L, Zhang Y, Niu G, Li M, Wang P et al (2018) Plant editosome database: a curated database of RNA editosome in plants. Nucleic Acids Res 47:D170–D174PubMedCentralCrossRefGoogle Scholar
  120. Liu Y, Tian T, Zhang K, You Q, Yan H, Zhao N et al (2017) PCSD: a plant chromatin state database. Nucleic Acids Res 46:1157–D1167CrossRefGoogle Scholar
  121. Lo CG, Hernández I, Ceci L, Pesole G, Picardi E (2019) RNA editing in plants: a comprehensive survey of bioinformatics tools and databases. Plant Physiol Biochem 137:53–61CrossRefGoogle Scholar
  122. Luan H, Shen H, Pan Y, Guo B, Lv C, Xu R (2018) Elucidating the hypoxic stress response in barley (Hordeum vulgare L.) during waterlogging: a proteomics approach. Sci Rep 8:9655PubMedPubMedCentralCrossRefGoogle Scholar
  123. Magaña Ugarte R, Escudero A, Gavilán RG (2019) Metabolic and physiological responses of Mediterranean high-mountain and alpine plants to combined abiotic stresses. Physiol PlantGoogle Scholar
  124. Magaña UR, Escudero A, Gavilán RG (2019) Metabolic and physiological responses of mediterranean high-mountain and alpine plants to combined abiotic stresses. Physiol Plant 165:403–412Google Scholar
  125. Makita Y, Shimada S, Kawashima M, Kondou-Kuriyama T, Toyoda T, Matsui M (2014) MOROKOSHI: transcriptome database in Sorghum bicolor. Plant Cell Physiol 56:e6PubMedPubMedCentralCrossRefGoogle Scholar
  126. Matsuda F, Hirai MY, Sasaki E, Akiyama K, Yonekura-Sakakibara K, Provart NJ et al (2010) AtMetExpress development: a phytochemical atlas of Arabidopsis development. Plant Physiol 152:566–578PubMedPubMedCentralCrossRefGoogle Scholar
  127. McCombie WR, McPherson JD, Mardis ER (2018) Next-generation sequencing technologies. Cold Spring Harb Perspect MedGoogle Scholar
  128. McGlew K, Shaw V, Zhang M, Kim RJ, Yang W, Shorrosh B et al (2015) An annotated database of Arabidopsis mutants of acyl lipid metabolism. Plant Cell Rep 34:519–532PubMedCrossRefPubMedCentralGoogle Scholar
  129. Members BIG Data Center (2019) Database resources of the BIG data Center in 2019. Nucleic Acids Res 47:D8CrossRefGoogle Scholar
  130. Miettinen K, Inigo S, Kreft L, Pollier J, De Bo C, Botzki A et al (2017) The TriForC database: a comprehensive up-to-date resource of plant triterpene biosynthesis. Nucleic Acids Res 46:D586–D594PubMedCentralCrossRefGoogle Scholar
  131. Mihara M, Itoh T, Izawa T (2009) SALAD database: a motif-based database of protein annotations for plant comparative genomics. Nucleic Acids Res 38:D835–D842PubMedPubMedCentralCrossRefGoogle Scholar
  132. Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA (2019) High-throughput phenotyping for crop improvement in the genomics era. Plant SciGoogle Scholar
  133. Mishra NS, Tripathi A, Goswami K, Shukla RN, Vasudevan M, Goswami H (2018) ARMOUR–A Rice miRNA: mRNA interaction resource. Front Plant Sci 9:602.  https://doi.org/10.3389/fpls.2018.00602CrossRefGoogle Scholar
  134. Mittler R (2006) Abiotic stress, the field environment and stress combination. Trends Plant Sci 11:15–19PubMedPubMedCentralCrossRefGoogle Scholar
  135. Mo Q, Shen R, Guo C, Vannucci M, Chan KS, Hilsenbeck SG (2017) A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19:71–86PubMedCentralCrossRefGoogle Scholar
  136. Moreno-Risueno MA, Busch W, Benfey PN (2010) Omics meet networks—using systems approaches to infer regulatory networks in plants. Curr Opin Plant Biol 13:126–131PubMedCrossRefPubMedCentralGoogle Scholar
  137. Mosa KA, Ismail A, Helmy M (2017) Omics and system biology approaches in plant stress research. In: Plant stress tolerance. Springer, New York, NY, pp 21–34CrossRefGoogle Scholar
  138. Mueller LA, Zhang P, Rhee SY (2003) AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol 132:453–460PubMedPubMedCentralCrossRefGoogle Scholar
  139. Mueller LA, Solow TH, Taylor N, Skwarecki B, Buels R, Binns J et al (2005) The SOL genomics network. A comparative resource for Solanaceae biology and beyond. Plant Physiol 138:1310–1317PubMedPubMedCentralCrossRefGoogle Scholar
  140. Muthuramalingam P, Jeyasri R, Kalaiyarasi D, Pandian S, Krishnan SR, Satish L et al (2019) Emerging advances in computational omics tools for systems analysis of gramineae family grass species and their abiotic stress responsive functions. OMICS-Based Approach Plant Biotechnol 185:185Google Scholar
  141. Mutwil M, Klie S, Tohge T, Giorgi FM, Wilkins O, Campbell MM et al (2011) PlaNet: combined sequence and expression comparisons across plant networks derived from seven species. Plant Cell 23:895–910PubMedPubMedCentralCrossRefGoogle Scholar
  142. Naithani S, Preece J, D'Eustachio P, Gupta P, Amarasinghe V, Dharmawardhana PD et al (2016) Plant Reactome: a resource for plant pathways and comparative analysis. Nucleic Acids Res 45:D1029–D1039PubMedPubMedCentralCrossRefGoogle Scholar
  143. Newton A, Lyon G, Marshall B (2002) DRASTIC: a database resource for analysis of signal transduction in cells. BSPP Newslett 42:36–37Google Scholar
  144. Obayashi T, Kinoshita K, Nakai K, Shibaoka M, Hayashi S, Saeki M et al (2006) ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis. Nucleic Acids Res 35:D863–D869PubMedPubMedCentralCrossRefGoogle Scholar
  145. Oldeman LR, Hakkeling R, Sombroek WG (2017) World map of the status of human-induced soil degradation: an explanatory note. International Soil Reference and Information CentreGoogle Scholar
  146. Orchard S, Kerrien S, Abbani S, Aranda B, Bhate J, Bidwell S et al (2012) Protein interaction data curation: the international molecular exchange (IMEx) consortium. Nat Methods 9:345–350PubMedPubMedCentralCrossRefGoogle Scholar
  147. Pandey P, Irulappan V, Bagavathiannan MV, Senthil-Kumar M (2017) Impact of combined abiotic and biotic stresses on plant growth and avenues for crop improvement by exploiting physio-morphological traits. Front Plant Sci 8:537PubMedPubMedCentralGoogle Scholar
  148. Parida AK, Panda A, Rangani J (2018) Metabolomics-guided elucidation of abiotic stress tolerance mechanisms in plants. In: Ahmad P, Ahanger MA, Singh VP, Tripathi DK, Alam P, Alyemeni MN (eds) Plant metabolites and regulation under environmental stress. Elsevier, Amsterdam, pp 89–131CrossRefGoogle Scholar
  149. Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87:1123–1124CrossRefGoogle Scholar
  150. Picardi E, Regina TMR, Brennicke A, Quagliariello C (2006) REDIdb: the RNA editing database. Nucleic Acids Res 35:D173–D177PubMedPubMedCentralCrossRefGoogle Scholar
  151. Pilcher JM (2017) Food in world history. Routledge, LondonCrossRefGoogle Scholar
  152. Popescu GV, Noutsos C, Popescu SC (2016) Big data in plant science: resources and data mining tools for plant genomics and proteomics. In: Data mining techniques for the life sciences. Springer, New York, NY, pp 533–547CrossRefGoogle Scholar
  153. Prabha R, Ghosh I, Singh DP (2011) Plant stress gene database: a collection of plant genes responding to stress condition. ARPN J Sci Technol 1:28–31Google Scholar
  154. Proost S, Van Bel M, Sterck L, Billiau K, Van Parys T, Van de Peer Y et al (2009) PLAZA: a comparative genomics resource to study gene and genome evolution in plants. Plant Cell 21:3718–3731PubMedPubMedCentralCrossRefGoogle Scholar
  155. Pruitt KD, Tatusova T, Maglott DR (2005) NCBI reference sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 33:D501–D504PubMedCrossRefPubMedCentralGoogle Scholar
  156. Ramegowda V, Senthil-Kumar M (2015) The interactive effects of simultaneous biotic and abiotic stresses on plants: mechanistic understanding from drought and pathogen combination. J Plant Physiol 176:47–54PubMedCrossRefPubMedCentralGoogle Scholar
  157. Rao VS, Das SK, Rao VJ, Srinubabu G (2008) Recent developments in life sciences research: role of bioinformatics. Afr J Biotechnol 7:495–503Google Scholar
  158. Raubenheimer D, Simpson SJ, Mayntz D (2009) Nutrition, ecology and nutritional ecology: toward an integrated framework. Funct Ecol 23:4–16CrossRefGoogle Scholar
  159. Reuter JA, Spacek DV, Snyder MP (2015) High-throughput sequencing technologies. Mol Cell 58:586–597PubMedPubMedCentralCrossRefGoogle Scholar
  160. Riaño-Pachón DM, Ruzicic S, Dreyer I, Mueller-Roeber B (2007) PlnTFDB: an integrative plant transcription factor database. BMC Bioinformat 8:42CrossRefGoogle Scholar
  161. Romeuf I, Tessier D, Dardevet M, Branlard G, Charmet G, Ravel C (2010) wDBTF: an integrated database resource for studying wheat transcription factor families. BMC Genomics 11:185PubMedPubMedCentralCrossRefGoogle Scholar
  162. Ruiz M, Rouard M, Raboin LM, Lartaud M, Lagoda P, Courtois B (2004) TropGENE-DB, a multi-tropical crop information system. Nucleic Acids Res 32:D364–D367PubMedPubMedCentralCrossRefGoogle Scholar
  163. Saeed M (2018) Abiotic stress tolerance in Rice (Oryza sativa L.): a genomics perspective of salinity tolerance. In: Rice crop-current developments. IntechOpen, CroatiaGoogle Scholar
  164. Sakurai T, Satou M, Akiyama K, Iida K, Seki M, Kuromori T et al (2005) RARGE: a large-scale database of RIKEN Arabidopsis resources ranging from transcriptome to phenome. Nucleic Acids Res 33:D647–D650PubMedCrossRefPubMedCentralGoogle Scholar
  165. Sakurai N, Ara T, Ogata Y, Sano R, Ohno T, Sugiyama K et al (2010) KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data. Nucleic Acids Res 39:D677–D684PubMedPubMedCentralCrossRefGoogle Scholar
  166. Salgotra R, Gupta B, Stewart C (2014) From genomics to functional markers in the era of next-generation sequencing. Biotechnol Lett 36:417–426PubMedCrossRefPubMedCentralGoogle Scholar
  167. Sandelin A, Alkema W, Engström P, Wasserman WW, Lenhard B (2004) JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res 32:D91–D94PubMedPubMedCentralCrossRefGoogle Scholar
  168. Sato Y, Antonio BA, Namiki N, Takehisa H, Minami H, Kamatsuki K et al (2010) RiceXPro: a platform for monitoring gene expression in japonica rice grown under natural field conditions. Nucleic Acids Res 39:D1141–D1148PubMedPubMedCentralCrossRefGoogle Scholar
  169. Sato Y, Namiki N, Takehisa H, Kamatsuki K, Minami H, Ikawa H et al (2012) RiceFREND: a platform for retrieving coexpressed gene networks in rice. Nucleic Acids Res 41:D1214–D1221PubMedPubMedCentralCrossRefGoogle Scholar
  170. Schaeffer ML, Harper LC, Gardiner JM, Andorf CM, Campbell DA, Cannon EK, Sen TZ, Lawrence CJ (2011) MaizeGDB: curation and outreach go hand-in-hand. Database, 2011Google Scholar
  171. Scheben A, Batley J, Edwards D (2018) Revolution in genotyping platforms for crop improvement. Plant Genet Molecul Biol:37–52Google Scholar
  172. Schilling CH, Edwards JS, Palsson BO (1999) Toward metabolic phenomics: analysis of genomic data using flux balances. Biotechnol Prog 15:288–295PubMedCrossRefPubMedCentralGoogle Scholar
  173. Schmitt T, Ogris C, Sonnhammer EL (2013) FunCoup 3.0: database of genome-wide functional coupling networks. Nucleic Acids Res 42:D380–D388PubMedPubMedCentralCrossRefGoogle Scholar
  174. Seren Ü, Grimm D, Fitz J, Weigel D, Nordborg M, Borgwardt K et al (2016) AraPheno: a public database for Arabidopsis thaliana phenotypes. Nucleic Acids Res:D1054–D1059Google Scholar
  175. Shameer K, Ambika S, Varghese SM, Karaba N, Udayakumar M, Sowdhamini R (2009) STIFDB—Arabidopsis stress responsive transcription factor dataBase. Int J Plant Genomics 2009:583429PubMedPubMedCentralCrossRefGoogle Scholar
  176. Shang Y, Huang S (2019) Multi-omics data-driven investigations of metabolic diversity of plant triterpenoids. Plant J 97:101–111PubMedCrossRefPubMedCentralGoogle Scholar
  177. Sharma N, Mittal D, Mishra NS (2017) Micro-regulators of hormones and stress. Mechanism of plant hormone signaling under. Stress 2:319–351Google Scholar
  178. Shen L, Gong J, Caldo RA, Nettleton D, Cook D, Wise RP et al (2005) BarleyBase—an expression profiling database for plant genomics. Nucleic Acids Res 33:D614–D618PubMedCrossRefPubMedCentralGoogle Scholar
  179. Shen W, Li H, Teng R, Wang Y, Wang W, Zhuang J (2018) Genomic and transcriptomic analyses of HD-zip family transcription factors and their responses to abiotic stress in tea plant (Camellia sinensis). Genomics.  https://doi.org/10.1016/j.ygeno.2018.07.009
  180. Singh A, Sharma AK, Singh NK, Sharma TR (2017) PpTFDB: a pigeonpea transcription factor database for exploring functional genomics in legumes. PLoS One 12:e0179736PubMedPubMedCentralCrossRefGoogle Scholar
  181. Singh B, Mishra S, Bohra A, Joshi R, Siddique KH (2018a) Crop phenomics for abiotic stress tolerance in crop plants. In: Biochemical, physiological and molecular avenues for combating abiotic stress tolerance in plants. Elsevier, Amsterdam, pp 277–296CrossRefGoogle Scholar
  182. Singh RK, Lee J-K, Selvaraj C, Singh R, Li J, Kim S-Y et al (2018b) Protein engineering approaches in the post-genomic era. Curr Protein Pept Sci 19:5–15PubMedCrossRefPubMedCentralGoogle Scholar
  183. Smalter HA, Shan Y, Lushington G, Visvanathan M (2013) An overview of computational life science databases & exchange formats of relevance to chemical biology research. Combinat Chem High Throughp Screen 16:189–198CrossRefGoogle Scholar
  184. Spannagl M, Nussbaumer T, Bader KC, Martis MM, Seidel M, Kugler KG et al (2015) PGSB PlantsDB: updates to the database framework for comparative plant genome research. Nucleic Acids Res 44:D1141–D1147PubMedPubMedCentralCrossRefGoogle Scholar
  185. Speed D, Balding DJ (2015) Relatedness in the post-genomic era: is it still useful? Nat Rev Genet 16:33–44PubMedCrossRefPubMedCentralGoogle Scholar
  186. Stein LD (2003) Integrating biological databases. Nat Rev Genet 4:337PubMedCrossRefPubMedCentralGoogle Scholar
  187. Stone SL (2019) Role of the ubiquitin proteasome system in plant response to abiotic stress. In: International review of cell and molecular biology. Elsevier, Amsterdam, pp 65–110Google Scholar
  188. Sun Q, Zybailov B, Majeran W, Friso G, Olinares PDB, van Wijk KJ (2008) PPDB, the plant proteomics database at Cornell. Nucleic Acids Res 37:D969–D974PubMedPubMedCentralCrossRefGoogle Scholar
  189. Szcześniak MW, Deorowicz S, Gapski J, Kaczyński Ł, Makałowska I (2011) miRNEST database: an integrative approach in microRNA search and annotation. Nucleic Acids Res 40:D198–D204PubMedPubMedCentralCrossRefGoogle Scholar
  190. Szklarczyk D, Jensen LJ (2015) Protein-protein interaction databases. In: Protein-protein interactions. Springer, New York, NY, pp 39–56Google Scholar
  191. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J et al (2014) STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D447–D452PubMedPubMedCentralCrossRefGoogle Scholar
  192. Tardieu F, Tuberosa R (2010) Dissection and modelling of abiotic stress tolerance in plants. Curr Opin Plant Biol 13:206–212CrossRefGoogle Scholar
  193. Tateno Y, Gojobori T (1997) DNA data Bank of Japan in the age of information biology. Nucleic Acids Res 25:14–17PubMedPubMedCentralCrossRefGoogle Scholar
  194. Tello-Ruiz MK, Naithani S, Stein JC, Gupta P, Campbell M, Olson A et al (2017) Gramene 2018: unifying comparative genomics and pathway resources for plant research. Nucleic Acids Res 46:D1181–D1189PubMedCentralCrossRefGoogle Scholar
  195. Tsesmetzis N, Couchman M, Higgins J, Smith A, Doonan JH, Seifert GJ et al (2008) Arabidopsis reactome: a foundation knowledgebase for plant systems biology. Plant Cell 20:1426–1436PubMedPubMedCentralCrossRefGoogle Scholar
  196. Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11:2301PubMedCrossRefPubMedCentralGoogle Scholar
  197. Udayakumar M, Chandar DP, Arun N, Mathangi J, Hemavathi K, Seenivasagam R (2012) PMDB: plant Metabolome database—a metabolomic approach. Med Chem Res 21:47–52CrossRefGoogle Scholar
  198. Upadhyay J, Joshi R, Singh B, Bohra A, Vijayan R, Bhatt M et al (2017) Application of bioinformatics in understanding of plant stress tolerance. In: Plant bioinformatics. Springer, Cham, pp 347–374CrossRefGoogle Scholar
  199. Urano K, Kurihara Y, Seki M, Shinozaki K (2010) ‘Omics’ analyses of regulatory networks in plant abiotic stress responses. Curr Opin Plant Biol 13:132–138PubMedPubMedCentralCrossRefGoogle Scholar
  200. Valentine AJ, Benedito VA, Kang Y (2011) Legume nitrogen fixation and soil abiotic stress: from physiology to genomics and beyond. Ann Plant Rev 43:207–248Google Scholar
  201. Van Wyk B-E, Wink M (2017) Medicinal plants of the world. CABI, WallingfordCrossRefGoogle Scholar
  202. Vassilev D, Nenov A, Atanassov A, Dimov G, Getov L (2006) Application of bioinformatics in fruit plant breeding. J Fruit Ornament Plant Res 14:145Google Scholar
  203. Vaughan MM, Block A, Christensen SA, Allen LH, Schmelz EA (2018) The effects of climate change associated abiotic stresses on maize phytochemical defenses. Phytochem Rev 17:37–49CrossRefGoogle Scholar
  204. Vít P, Krak K, Trávníček P, Douda J, Lomonosova MN, Mandák B (2016) Genome size stability across eurasian chenopodium species (Amaranthaceae). Bot J Linn Soc 182:637–649CrossRefGoogle Scholar
  205. Vranová E, Hirsch-Hoffmann M, Gruissem W (2011) AtIPD: a curated database of Arabidopsis isoprenoid pathway models and genes for isoprenoid network analysis. Plant Physiol 156:1655–1660PubMedPubMedCentralCrossRefGoogle Scholar
  206. Wang Y, You FM, Lazo GR, Luo M-C, Thilmony R, Gordon S et al (2012) PIECE: a database for plant gene structure comparison and evolution. Nucleic Acids Res 41:D1159–D1166PubMedPubMedCentralCrossRefGoogle Scholar
  207. Wang P, Su L, Gao H, Jiang X, Wu X, Li Y et al (2018) Genome-wide characterization of bHLH genes in grape and analysis of their potential relevance to abiotic stress tolerance and secondary metabolite biosynthesis. Front Plant Sci 9:64PubMedPubMedCentralCrossRefGoogle Scholar
  208. Wani SH (2018) Biochemical, physiological and molecular avenues for combating abiotic stress in plants. Academic Press, Cambridge, MAGoogle Scholar
  209. Ware D, Jaiswal P, Ni J, Pan X, Chang K, Clark K et al (2002) Gramene: a resource for comparative grass genomics. Nucleic Acids Res 30:103–105PubMedPubMedCentralCrossRefGoogle Scholar
  210. Weckwerth W (2003) Metabolomics in systems biology. Annu Rev Plant Biol 54:669–689PubMedCrossRefPubMedCentralGoogle Scholar
  211. Winter G, Krömer JO (2013) Fluxomics–connecting ‘omics analysis and phenotypes. Environ Microbiol 15:1901–1916PubMedCrossRefPubMedCentralGoogle Scholar
  212. Wise R, Caldo R, Hong L, Wu S, Cannon E, Dickerson J (2006) BarleyBase/PLEXdb: a unifited expression prolfiling database for plants and plant pathogens. Method Molecul Biol 406:347–363Google Scholar
  213. Wu H-J, Ma Y-K, Chen T, Wang M, Wang X-J (2012) PsRobot: a web-based plant small RNA meta-analysis toolbox. Nucleic Acids Res 40:W22–W28PubMedPubMedCentralCrossRefGoogle Scholar
  214. Yi X, Zhang Z, Ling Y, Xu W, Su Z (2014) PNRD: a plant non-coding RNA database. Nucleic Acids Res 43:D982–D989PubMedPubMedCentralCrossRefGoogle Scholar
  215. Yilmaz A, Nishiyama MY, Fuentes BG, Souza GM, Janies D, Gray J et al (2009) GRASSIUS: a platform for comparative regulatory genomics across the grasses. Plant Physiol 149:171–180PubMedPubMedCentralCrossRefGoogle Scholar
  216. Yoshida T, Mogami J, Yamaguchi-Shinozaki K (2015) Omics approaches toward defining the comprehensive abscisic acid signaling network in plants. Plant Cell Physiol 56:1043–1052PubMedCrossRefPubMedCentralGoogle Scholar
  217. Yu J, Jung S, Cheng C-H, Ficklin SP, Lee T, Zheng P et al (2013) CottonGen: a genomics, genetics and breeding database for cotton research. Nucleic Acids Res 42:D1229–D1236PubMedPubMedCentralCrossRefGoogle Scholar
  218. Yuan C, Meng X, Li X, Illing N, Ingle RA, Wang J et al (2016) PceRBase: a database of plant competing endogenous RNA. Nucleic Acids Res 45:D1009–D1014PubMedPubMedCentralCrossRefGoogle Scholar
  219. Yura K, Sulaiman S, Hatta Y, Shionyu M, Go M (2009) RESOPS: a database for analyzing the correspondence of RNA editing sites to protein three-dimensional structures. Plant Cell Physiol 50:1865–1873PubMedPubMedCentralCrossRefGoogle Scholar
  220. Zandalinas SI, Mittler R, Balfagón D, Arbona V, Gómez-Cadenas A (2018) Plant adaptations to the combination of drought and high temperatures. Physiol Plant 162:2–12PubMedCrossRefPubMedCentralGoogle Scholar
  221. Zargar SM, Rai V (2017) Plant molecular breeding: way forward through next-generation sequencing. In: Plant OMICS and crop breeding. Apple Academic Press, Ontario, pp 226–259CrossRefGoogle Scholar
  222. Zhang B (2015) MicroRNA: a new target for improving plant tolerance to abiotic stress. J Exp Bot 66:1749–1761PubMedPubMedCentralCrossRefGoogle Scholar
  223. Zhang Z, Yu J, Li D, Zhang Z, Liu F, Zhou X et al (2009) PMRD: plant microRNA database. Nucleic Acids Res 38:D806–D813PubMedPubMedCentralCrossRefGoogle Scholar
  224. Zhang S, Yue Y, Sheng L, Wu Y, Fan G, Li A et al (2013) PASmiR: a literature-curated database for miRNA molecular regulation in plant response to abiotic stress. BMC Plant Biol 13:33PubMedPubMedCentralCrossRefGoogle Scholar
  225. Zhang L, Li X, Ma B, Gao Q, Du H, Han Y et al (2017a) The tartary buckwheat genome provides insights into rutin biosynthesis and abiotic stress tolerance. Mol Plant 10:1224–1237PubMedPubMedCentralCrossRefGoogle Scholar
  226. Zhang P, Meng X, Chen H, Liu Y, Xue J, Zhou Y et al (2017b) PlantCircNet: a database for plant circRNA–miRNA–mRNA regulatory networks. Database 2017:bax089.  https://doi.org/10.1093/database/bax1089CrossRefPubMedCentralGoogle Scholar
  227. Zhang X, Xu Y, Huang B (2018a) Lipidomic reprogramming associated with drought stress priming-enhanced heat tolerance in tall fescue (Festuca arundinacea). Plant Cell Environ.  https://doi.org/10.1111/pce.13405CrossRefGoogle Scholar
  228. Zhang X, Yao C, Fu S, Xuan H, Wen S, Liu C et al (2018b) Stress2TF: a manually curated database of TF regulation in plant response to stress. Gene 638:36–40PubMedPubMedCentralCrossRefGoogle Scholar
  229. Zheng Y, Wu S, Bai Y, Sun H, Jiao C, Guo S et al (2018) Cucurbit genomics database (CuGenDB): a central portal for comparative and functional genomics of cucurbit crops. Nucleic Acids Res 47:D1128–D1136PubMedCentralCrossRefGoogle Scholar
  230. Zhu J-K (2016) Abiotic stress signaling and responses in plants. Cell 167:313–324PubMedPubMedCentralCrossRefGoogle Scholar
  231. Zielezinski A, Dolata J, Alaba S, Kruszka K, Pacak A, Swida-Barteczka A et al (2015) mirEX 2.0-an integrated environment for expression profiling of plant microRNAs. BMC Plant Biol 15:144PubMedPubMedCentralCrossRefGoogle Scholar
  232. Zou D, Sun S, Li R, Liu J, Zhang J, Zhang Z (2014) MethBank: a database integrating next-generation sequencing single-base-resolution DNA methylation programming data. Nucleic Acids Res 43:D54–D58PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anamika
    • 1
  • Sahil Mehta
    • 1
  • Baljinder Singh
    • 2
  • Anupam Patra
    • 1
  • Md. Aminul Islam
    • 2
  1. 1.International Centre for Genetic Engineering and BiotechnologyNew DelhiIndia
  2. 2.National Institute of Plant Genome ResearchNew DelhiIndia

Personalised recommendations