Abstract
In recent years, omics technologies have generated a vast amount of biological data, whose interpretation and management needs a sophisticated computational analysis. Bioinformatics has come to rescue, which is an interdisciplinary branch of science aimed at interpreting biological data using information technology and computer science. Integration of bioinformatics with plant science research has generated many applications like single-gene analysis, biochemical pathways, molecular techniques, sequence similarity, modelling of protein, crop improvement, crop breeding, improved nutritional quality, development of drought-resistant varieties and plant biotic/abiotic stress management. Since the data is huge, plant biologists and researchers are facing problems in the interpretation of data as they are unable to exploit available bioinformatics tools, plant-based databases and their applications. Bioinformatics has played an essential role in plant stress management with regard to the understanding of various stress signalling pathways, crosstalk between different pathways and mechanisms. It has many practical applications in current plant stress management such as understanding changes in the metabolomics and proteomics during stress conditions which ultimately helps in designing the best stress management approaches and databases. Keeping that in observation, the present chapter describes some of the key concepts and databases used in bioinformatics, with an emphasis on those relevant to plant stress management. This chapter will also cover some of the latest technologies and bioinformatics applications in today’s plant stress management strategies. Finally, we explore a few emerging research topics in this cutting-edge field of research.
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References
Adams MD, Soares MB, Kerlavage AR, Fields C, Venter JC (1993) Rapid cDNA sequencing (expressed sequence tags) from a directionally cloned human infant brain cDNA library. Nat Genet 4:373–380
Addo-Quaye C, Miller W, Axtell MJ (2008) CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics 25(1):130–131
Agarwal P, Parida SK, Mahto A et al (2014) Expanding frontiers in plant transcriptomics in aid of functional genomics and molecular breeding. Biotechnol J 9:1480–1492
Akiyama K, Chikayama E, Yuasa H, Shimada Y, Tohge T, Shinozaki K, Hirai MY, Sakurai T, Kikuchi J, Saito K (2008) PRIMe: a web site that assembles tools for metabolomics and transcriptomics. Silicon Biol 8(3):339–345
Alter S, Bader KC, Spannagl M, Wang Y, Bauer E, Schön CC, Mayer KFX (2015) DroughtDB: an expert-curated compilation of plant drought stress genes and their homologs in nine species. J Biol Database Curation 2015:bav046
Ambika S, Susan Mary Varghese SM, Shameer K, Udayakumar M, Sowdhamini R (2008) STIF: Hidden Markov Model-based search algorithm for the recognition of binding sites of Stress-upregulated Transcription Factors and genes in Arabidopsis thaliana. Bioinformation 2(10):431–437
Anonymous (2005) Pseudomonas versus Arabidopsis: models for genomic research into plant disease resistance. www.actionbioscience.org
Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408(6814):796
Attwood TK, Beck ME, Bleasby AJ, Parry-Smith DJ (1994) PRINTS–a database of protein motif fingerprints. Nucleic Acids Res 22(17):3590–3596
Badjakov I, Kondakova V, Atanassov A (2012) In: Benkeblia N (ed) Current view on fruit quality in relation to human health in sustainable agriculture and new biotechnologies. CRC Press, Boca Raton, pp 303–319
Baerenfaller K, Grossmann J, Grobei MA et al (2008) Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320:938–941
Bairoch A, Apweiler R (2000) The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res 28(1):45–48
Bais P, Moon SM, He K, Leitao R, Dreher K, Walk T et al (2015) PlantMetabolomics.org: a web portal for plant metabolomics experiments. Plant Physiol 152(4):1807–1816
Basse M-J, Betzi S, Morelli X, Roche P (2016) 2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein–protein interactions. Database 2016:baw007
Bino RJ, Hall RD, Fiehn O, Kopka J, Saito K, Draper J, Nikolau BJ, Mendes P, Roessner-Tunali U, Beale MH et al (2004) Potential of metabolomics as a functional genomics tool. Trends Plant Sci 9:418–425
Bolser D, Staines DM, Pritchard E, Kersey P (2016) Ensembl plants: integrating tools for visualizing, mining, and analyzing plant genomics data. In: Edwards D (ed) Plant bioinformatics, Methods in molecular biology, vol 1374. Humana Press, New York, NY
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 26:1566–1568
Borkotoky S, Saravanan V, Jaiswal A, et al. (2013) The Arabidopsis stress responsive gene database. Int J Plant Genom 2013:949564
Broughton WJ, Hernández G, Blair M et al (2003) Beans (Phaseolus spp.)—model food legumes. Plant Soil 252:55–128
Brown JW, Shaw PJ, Shaw P, Marshall DF (2005) Arabidopsis nucleolar protein database (AtNoPDB). Nucleic Acids Res 33:D633–D636
Bülow L, Schindler M, Choi C, Hehl R (2004) PathoPlant: a database on plant-pathogen interactions. In Silico Biol 4(4):529–536
Castellana NE, Payne SH, Shen Z et al (2008) Discovery and revision of Arabidopsis genes by proteogenomics. Proc Natl Acad Sci U S A 105:21034–21038
Castillo-Peinado LS, de Castro ML (2016) Present and foreseeable future of metabolomics in forensic analysis. Anal Chim Acta 925:1–5
Chawla K, Barah P, Kuiper M, Bones AM (2011) Systems biology: a promising tool to study abiotic stress responses. Omics Plant Abiotic Stress Tolerance 10:163–172
Chen X, Qi X, Duan LX (2015) Overview. In: Plant metabolomics. Springer, Netherlands, p 1–24
Coman D, Gruissem W, Hennig L (2013) Transcript profiling in Arabidopsis with genome tiling microarrays. In: Tiling arrays: methods and protocols. Humana Press, Totowa, pp 35–49
Cook D, Fowler S, Fiehn O, Thomashow MF (2004) A prominent role for the CBF cold response pathway in configuring the low-temperature metabolome of Arabidopsis. Proc Natl Acad Sci U S A 101:15243–15248
Cooke IR, Jones D, Bowen JK et al (2014) Proteogenomic analysis of the Venturia pirina (Pear Scab Fungus) secretome reveals potential effectors. J Proteome Res 13:3635–3644
Cornell M, Paton NW, Hedeler C, Kirby P, Delneri D, Hayes A, Oliver SG (2003) GIMS: an integrated data storage and analysis environment for genomic and functional data. Yeast 20(15):1291–1306
Cushman JC, Bohnert HJ (2000) Genomic approaches to plant stress tolerance. Curr Opin Plant Biol 3:117–124
Dash S, Van Hemert J, Hong L, Wise RP, Dickerson JA (2012) PLEXdb: gene expression resources for plants and plant pathogens. Nucleic Acids Res 40:D1194–D1201
Dash S, Campbell JD, Cannon EK, Cleary AM, Huang W, Kalberer SR, Karingula V, Rice AG, Singh J, Umale PE, Weeks NT, Wilkey AP, Farmer AD, Cannon SB (2016) Legume information system (LegumeInfo.org): a key component of a set of federated data resources for the legume family. Nucleic Acids Res 44:D1181–D1188
De Cremer K, Mathys J, Vos C et al (2013) RNAseq-based transcriptome analysis of Lactuca sativa infected by the fungal necrotroph Botrytis cinerea. Plant Cell Environ 36:1992–2007
Delaunois B, Jeandet P, Clément C, Baillieul F, Dorey S, Cordelier S (2014) Uncovering plant–pathogen crosstalk through apoplastic proteomic studies. Front Plant Sci 5:249
Delmotte N, Knief C, Chaffron S et al (2009) Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc Natl Acad Sci U S A 106:16428–16433
Dong Q, Schlueter SD, Brendel V (2004) PlantGDB, plant genome database and analysis tools. Nucleic Acids Res 32:D354–D359
Dubois A, Carrere S, Raymond O, Pouvreau B, Cottret L, Roccia A, Onesto JP, Sakr S, Atanassova R, Baudino S, Foucher F, Le Bris M, Gouzy J, Bendahmane M (2012) Transcriptome database resource and gene expression atlas for the rose. BMC Genomics 13:638
Duque AS, de Almeida AM, da Silva AB, da Silva JM, et al (2013) Abiotic stress—plant responses and applications in agriculture. InTech, Chapter 3, p 40–101
Durek P, Schmidt R, Heazlewood JL, Jones A, Maclean D, Nagel A, Kersten B, Schulze WX (2010) PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res 38:D828–D834
Eldem V, Okay S, Ünver T (2013) Plant microRNAs: new players in functional genomics. Turk J Agric For 37:1–21
El-Metwally S, Ouda OM, Helmy M (2014a) Next generation sequencing technologies and challenges in sequence assembly, 1st edn. Springer, ISBN: 978-1-4939-0714-4
El-Metwally S, Ouda OM, Helmy M (2014b) First- and next-generations sequencing methods. Next gener seq technol Challenges seq assem. Springer, New York, pp 29–36
El-Metwally S, Ouda OM, Helmy M (2014c) New horizons in next-generation sequencing. Next gener seq technol Challenges seq assem. Springer, New York, pp 51–59
Ernst M, Silva DB, Silva RR, Vêncio RZ, Lopes NP (2014) Mass spectrometry in plant metabolomics strategies: from analytical platforms to data acquisition and processing. Nat Prod Rep 31(6):784–806
Esposito A, Colantuono C, Ruggieri V, Chiusano ML (2016) Bioinformatics for agriculture in the next-generation sequencing era. Chem Biol Technol Agric 3(1):9
Fernie AR, Schauer N (2009) Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet 25(1):39–48
Feuillet C, Leach JE, Rogers J, Schnable PS, Eversole K (2011) Crop genome sequencing: lessons and rationales. Trends Plant Sci 16(2):77–88
Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, Heger A, Hetherington K, Holm L, Mistry J, Sonnhammer ELL, Tate J, Punta M (2013) Pfam: the protein families database. Nucleic Acids Res 42(D1):D222–D230
Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S et al (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26:407–415
Futamura N, Totoki Y, Toyoda A, Igasaki T, Nanjo T, Seki M et al (2008) Characterization of expressed sequence tags from a full-length enriched cDNA library of Cryptomeria japonica male strobili. BMC Genomics 9:383
Gao J, Agrawal GK, Thelen JJ, Xu D (2009) Helmy Nucleic Acids Res 37:D960–D962
Ghosh D, Xu J (2014) Abiotic stress responses in plant roots: a proteomics perspective. Front Plant Sci 5:6
Gomez-Casati DF, Zanor MI, Busi MV (2013) Metabolomics in plants and humans: applications in the prevention and diagnosis of diseases. Biomed Res Int 2013:1–11
Govind G, Harshavardhan VT, Patricia JK et al (2009) Identification and functional validation of a unique set of drought induced genes preferentially expressed in response to gradual water stress in peanut. Mol Gen Genomics 281:607
Govindaraj M, Vetriventhan M, Srinivasan M (2015) Importance of genetic diversity assessment in crop plants and its recent advances: an overview of its analytical perspectives. Genet Res Int 2015:431487
Griffiths-Jones S, Saini HK, vanDongen S, Enright AJ (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36:D154–D158
Gupta P, Naithani S, Tello-Ruiz MK, Chougule K, D’Eustachio P, Fabregat A et al (2016) Gramene database: navigating plant comparative genomics resources. Curr Plant Biol 8:10–15
Gurjar AKS, Singh Panwar A, Gupta R, Mantri SS (2016) PmiRExAt: plant miRNA expression atlas database and web applications. Database 2016:baw060
Hammami R, Ben Hamida J, Vergoten G, Fliss I (2009) PhytAMP: a database dedicated to antimicrobial plant peptides. Nucleic Acids Res 37:D963–D968
Harbers M, Carninci P (2005) Tag-based approaches for transcriptome research and genome annotation. Nat Methods 2:495–502
Heazlewood JL, Durek P, Hummel J, Selbig J, Weckwerth W, Walther D, Schulze WX (2008) PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor. Nucleic Acids Res 36:D1015–D1021
Helmy M, Tomita M, Ishihama Y (2011) OryzaPG-DB: rice proteome database based on shotgun proteogenomics. BMC Plant Biol 11:63
Helmy M, Sugiyama N, Tomita M, Ishihama Y (2012a) Mass spectrum sequential subtraction speeds up searching large peptide MS/MS spectra datasets against large nucleotide databases for proteogenomics. Cell Mech 17:633–644
Helmy M, Sugiyama N, Tomita M, Ishihama Y (2012b) The rice proteogenomics database oryza PG-DB: development, expansion, and new features. Front Plant Sci 3:65
Hopff D, Wienkoop S, Lüthje S (2013) The plasma membrane proteome of maize roots grown under low and high iron conditions. J Proteome 91:605–618
Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L (2009) InterPro: the integrative protein signature database. Nucleic Acids Res 37:D211–D215
Hulo N (2006) The PROSITE database. Nucleic Acids Res 34(90001):D227–D230
International Rice Genome Sequencing Project (2005) The map-based sequence of the rice genome. Nature 436:793–800
Iijima Y, Nakamura Y, Ogata Y, Tanaka K’i, Sakurai N, Suda K, Suzuki T, Suzuki H, Okazaki K, Kitayama M, Kanaya S, Aoki K, Shibata D (2008) Metabolite annotations based on the integration of mass spectral information. Plant J 54(5):949–962
Jagadeeswaran G, Zheng Y, Sumathipala N, Jiang H, Arrese EL, Soulages JL et al (2010) Deep sequencing of small RNA libraries reveals dynamic regulation of conserved and novel microRNAs and microRNA-stars during silkworm development. BMC Genomics 11:52
Jayashree B, Crouch JH, Prasad PVNS, Hoising-ton D (2006) A database of annotated tentative orthologs from crop abiotic stress transcripts. Bioinformation 1:225–227
Jeong DH, Green PJ (2013) The role of rice microRNAs in abiotic stress responses. J Plant Biol 56:187–197
Jones-Rhoades MW, Bartel DP (2004) Computational identification of plant MicroRNAs and their targets, including a stress-induced miRNA. Mol Cell 14(6):787–799
Jogaiah S, Govind SR, Tran L-SP (2013) Systems biology-based approaches toward understanding drought tolerance in food crops. Crit Rev Biotechnol 33:23–39
Jorrín-Novo JV, Maldonado AM, Echevarría-Zomeño S, Valledor L, Castillejo MA, Curto M, Valero J, Sghaier B, Donoso G, Redondo I (2009) Plant proteomics update (2007–2008): second-generation proteomic techniques, an appropriate experimental design, and data analysis to fulfill MIAPE standards, increase plant proteome coverage and expand biological knowledge. J Proteome 72(3):285–314
Joshi R, Karan R, Singla-Pareek SL, Pareek A (2012) Microarray technology. In: Gupta AK, Pareek A, Gupta SM (eds) Biotechnology in medicine and agriculture: principles and practices. IK International Publishing House Pvt. Ltd., New Delhi, pp 273–296
Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30
Kang S, Ayers JE, Dewolf ED, Geiser DM, Kuldau G, Moorman GW, Mullins E, Uddin W, Correll JC, Deckert G, Lee YH, Lee YW, Martin FN, Subbarao K (2002) The internet-based fungal pathogen database: a proposed model. Phytopathology 92(3):232–236
Karp PD, Billington R, Caspi R, Fulcher CA, et al. (2017) The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform. https://doi.org/10.1093/bib/bbx085
Kawahara Y, de la Bastide M, Hamilton JP, Kanamori H, McCombie et al (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6:4
Kawahara Y, Oono Y, Wakimoto H, Ogata J (2016) TENOR: database for comprehensive mRNA-Seq experiments in rice. Plant Cell Physiol 57(1):e7
Khraiwesh B, Zhu JK, Zhu J (2012) Role of miRNAs and siRNAs in biotic and abiotic stress responses of plants. Biochim Biophys Acta 1819:137–148
Kim HS, Mittenthal JE, Caetano-Anolles G (2006) MANET:tracing evolution of protein architecture in metabolic networks. BMC Bioinforma 7:351
King ZA, Lu JS, Dräger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, Lewis NE (2016) BiGG Models: a platform for integrating, standardizing, and sharing genome-scale models. Nucleic Acids Res 44(D1):D515–D522
Kinjo AR, Bekker GJ, Suzuki H, Tsuchiya Y, Kawabata T, Ikegawa Y, Nakamura H (2017) Protein Data Bank Japan (PDBj): updated user interfaces, resource description framework, analysis tools for large structures. Nucleic Acids Res 45:D282–D288
Kissoudis C, van de Wiel C, Visser RGF, van der Linden G (2014) Enhancing crop resilience to combined abiotic and biotic stress through the dissection of physiological and molecular crosstalk. Front Plant Sci 5:e207
Koltai H, Volpin H (2003) Agricultural genomics: an approach to plant protection. Eur J Plant Pathol 109:101–108
Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel B, Bergmuller E, Dormann P, Weckwerth W, Gibon Y, Stitt M, Willmitzer L, Fernie AR, Steinhauser D (2005) GMD@CSB.DB: the Golm metabolome database. Bioinformatics 21(8):1635–1638
Krüger J, Rehmsmeier M (2006) RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res 34:W451–W454
Kudo T, Terashima S, Takaki Y, Tomita K 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(1):e1
Lee TH, Kim YK, Pham THM, Song SI et al (2009) Correlating gene expression from transcriptome profiling, and its application to the analysis of coexpressed genes in rice. Plant Physiol 151(1):16–33
Li JR, Liu CC, Sun CH, Chen YT (2018) Plant stress RNA-seq nexus: a stress-specific transcriptome database in plant cells. BMC Genomics 19:966
Liu B, Zhang N, Zhao S et al (2015) Proteomic changes during tuber dormancy release process revealed by iTRAQ quantitative proteomics in potato. Plant Physiol Biochem 86:181–190
Makita Y, Shimada S, Kawashima M, Kondou-Kuriyama T, Toyoda T, Matsui M (2015) MOROKOSHI: transcriptome database in Sorghum bicolor. Plant Cell Physiol 56:1
Maruyama K, Takeda M, Kidokoro S, Yamada K, Sakuma Y, Urano K, Fujita M, Yoshiwara K, Matsukura S, Morishita Y, Sasaki R (2009) Metabolic pathways involved in cold acclimation identified by integrated analysis of metabolites and transcripts regulated by DREB1A and DREB2A. Plant Physiol 150(4):1972–1980
Masoudi-Nejad A, Goto S, Jauregui R et al (2007) EGENES: transcriptome-based plant database of genes with metabolic pathway information and expressed sequence tag indices in KEGG. Plant Physiol 144(2):857–866
Matsuda F, Yonekura-Sakakibara K, Niida R, Kuromori T, Shinozaki K, Saito K (2009) MS/MS spectral tag-based annotation of non-targeted profile of plant secondary metabolites. Plant J 57(3):555–577
Matsui A, Ishida J, Morosawa T, Mochizuki Y, Kaminuma E, Endo TA, Okamoto M, Nambara E, Nakajima M, Kawashima M, Satou M (2008) Arabidopsis transcriptome analysis under drought, cold, high-salinity and ABA treatment conditions using a tiling array. Plant Cell Physiol 49(8):1135–1149
Matvienko M, Kozik A, Froenicke L, Lavelle D, Martineau B, Perroud B et al (2013) Consequences of normalizing transcriptomic and genomic libraries of plant genomes using a duplex-specific nuclease and tetramethylammonium chloride. PLoS One 8(2):e55913
McCarthy FM, Wang N, Bryce Magee G, Nanduri B, Lawrence ML, Camon EB, Barrell DG, Hill DP, Dolan ME, Paul Williams W, Luthe DS, Bridges SM, Burgess SC (2006) AgBase: a functional genomics resource for agriculture. BMC Genomics 7(1):229
Michelmore RW (2003) The impact zone: genomics and breeding for durable disease resistance. Curr Opin Plant Biol 6:397–404
Mine A, Sato M, Tsuda K (2014) Toward a systems understanding of plant–microbe interactions. Front Plant Sci 5:423
Mir S, Alhroub Y, Anyango S, Armstrong DR, Berrisford JM, Clark AR et al (2018) PDBe: towards reusable data delivery infrastructure at protein data bank in Europe. Nucleic Acids Res 46:D486–D492
Moco S, Bino RJ, Vorst O, Verhoeven HA, de Groot J, van Beek TA, Vervoort J, de Vos CHR (2006) A liquid chromatography-mass spectrometry-based metabolome database for tomato. Plant Physiol 141(4):1205–1218
Mousavi SA, Pouya FM, Ghaffari MR, Mirzaei M, Ghaffari A, Alikhani M, Ghareyazie M, Komatsu S, Haynes PA, Salekdeh GH (2016) PlantPReS: a database for plant proteome response to stress. J Proteome 143:69–72
Moxon S, Schwach F, Dalmay T, Maclean D, Studholme DJ, Moulton V (2008) A toolkit for analysing large-scale plant small RNA datasets. Bioinformatics 24:2252–2253
Murzin AG, Brenner SE, Hubbard T, Chothia C (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247:536–540
Naika M, Shameer K, Mathew OK, 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 Cell Physiol 54:1–15
Nakagami H, Sugiyama N, Ishihama Y, Shirasu K (2012) Shotguns in the front line: phosphoproteomics in plants. Plant Cell Physiol 53:118–124
Nakaya A, Ichihara H, Asamizu E, Shirasawa S, Nakamura Y, Tabata S, Hirakawa H (2017) Plant genomics databases. Methods in molecular biology, vol 1533. Humana Press, New York, pp 45–77
Narsai R, Ivanova A, Ng S, Whelan J (2010) Defining reference genes in Oryza sativa using organ, development, biotic and abiotic transcriptome datasets. BMC Plant Biol 10:56
Newton AC, Lyon GD, Marshall B (2002) DRASTIC: a database resource for analysis of signal transduction in cells. BSPP Newsl 42:36–37
Numnark S, Mhuantong W, Ingsriswang S, Wichadakul D (2012) C-mii: a tool for plant miRNA and target identification. BMC Genomics 13:S16
Osuna-Cruz CM, Paytuvi-Gallart A, Di Donato A, Sundesha V, Andolfo G, Aiese Cigliano R, Sanseverino W, Ercolano MR (2018) PRGdb 3.0: a comprehensive platform for prediction and analysis of plant disease resistance genes. Nucleic Acids Res 46:D1197–D1201
Pearson W (2004) Finding protein and nucleotide similarities with FASTA. Curr Protoc Bioinforma Chapter 3:Unit3.9
Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C (2008) WikiPathways: pathway editing for the people. PLoS Biol 6(7):e184
Prabha R, Ghosh I, Singh DP (2011) Plant stress gene database: a collection of plant genes responding to stress condition. ARPN J Sci Tech 1(1):28–31
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. J Database Curation Bat027:1–7
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 176:47–54
Remita AM, Lord E, Agharbaoui Z, Leclercq M et al (2016) WMP: a novel comprehensive wheat miRNA database, including related bioinformatics software. Curr Plant Biol 7(8):31–33
Rhee SY, Beavis W, Berardini TZ, Chen G, Dixon D, Doyle A et al (2003) The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community. Nucleic Acids Res 31(1):224
Rocha I, Förster J, Nielsen J (2008) Design and application of genome-scale reconstructed metabolic models. Microbi Gene Essentiality: Protoc Bioinforma 416:409–431
Rose PW, Prlić A, Altunkaya A, Bi C, Bradley AR, Christie CH et al (2017) The RCSB protein data Bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 45:D271–D281
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. Nucl Acids Res 33:D647–D650
Scheer M, Grote A, Chang A, Schomburg I, Munaretto C, Rother M, Söhngen C, Stelzer M, Thiele J, Schomburg D (2011) BRENDA, the enzyme information system in 2011. NucleicAcids Res 39:D670–D676
Schulze S, Henkel SG, Driesch D, Guthke R, Linde J (2015) Computational prediction of molecular pathogen–host interactions based on dual transcriptome data. Front Microbiol 6:65
Shafi A, Dogra V, Gill T, Ahuja PS, Sreenivasulu Y (2014) Simultaneous over-expression of PaSOD and RaAPX in transgenic Arabidopsis thaliana confers cold stress tolerance through increase in vascular lignifications. PLoS One 9:e110302
Shafi A, Gill T, Sreenivasulu Y, Kumar S, Ahuja PS, Singh AK (2015a) Improved callus induction, shoot regeneration, and salt stress tolerance in Arabidopsis overexpressing superoxide dismutase from Potentilla atrosanguinea. Protoplasma 252:41–51
Shafi A, Chauhan R, Gill T, Swarnkar MK, Sreenivasulu Y, Kumar S, Kumar N, Shankar R, Ahuja PS, Singh AK (2015b) Expression of SOD and APX genes positively regulates secondary cell wall biosynthesis and promotes plant growth and yield in Arabidopsis under salt stress. Plant Mol Biol 87:615–631
Shafi A, Pal AK, Sharma V, Kalia S, Kumar S, Ahuja PS, Singh AK (2017) Transgenic potato plants overexpressing SOD and APX exhibit enhanced lignification and starch biosynthesis with improved salt stress tolerance. Plant Mol Biol Rep 35:504–518
Shameer K, Ambika S, Varghese SM, Karaba N, Udayakumar M, Sowdhamini R (2009) STIFDB-Arabidopsis stress responsive transcription factor dataBase. Int J Plant Genomics 2009:583429
Shankar A, Singh A, Kanwar P et al (2013) Gene expression analysis of rice seedling under potassium deprivation reveals major changes in metabolism and signaling components. PLoS One 8:e70321
Singh VK, Singh AK, Chand R, Kushwaha C (2011) Role of bioinformatics in agriculture and sustainable development. Int J Bioinform Res 3(2):221–226
Singh B, Bohra A, Mishra S, Joshi R, Pandey S (2015) Embracing new-generation ‘omics’ tools to improve drought tolerance in cereal and food-legume crops. Biol Plant 59(3):413–428
Smita S, Lenka SK, Katiyar A, Jaiswal P, Preece J, Bansal KC (2011) QlicRice: a web interface for abiotic stress responsive QTL and loci interaction channels in rice. Database 1:1–9
Soanes DM, Skinner W, Keon J, Hargreaves J, Talbot NJ (2002) Genomics of phytopathogenic fungi and the development of bioinformatic resources. Mol Plant-Microbe Interact 15(5):421–427
Soga T, Ueno Y, Naraoka H et al (2002) Simultaneous determination of anionic intermediates for Bacillus subtilis metabolic pathways by capillary electrophoresis electrospray ionization mass spectrometry. Anal Chem 74:2233–2239
Somerville C, Dangl J (2000) Genomics. Plant biology in 2010. Science 290:2077–2078
Spannagl M, Nussbaumer T, Bader KC, Martis MM, Seidel M, Kugler KG, Gundlach H, Mayer KFX (2016) PGSB PlantsDB: updates to the database framework for comparative plant genome research. Nucleic Acids Res 44:D1141–D1147
Sun Q, Zybailov B, Majeran W, Friso G, Olinares PD, van Wijk KJ (2009) PPDB, the Plant Proteomics Database at Cornell. Nucleic Acids Res 37:D969–D974
Suzuki N, Rivero RM, Shulaev V, Blumwald E, Mittler R (2014) Abiotic and biotic stress combinations. New Phytol 203:32–43
Swarbreck D, Wilks C, Lamesch P et al (2008) The Arabidopsis information resource (TAIR): gene structure and function annotation. Nucleic Acids Res 36:D1009–D1014
Tardieu F, Tuberosa R (2010) Dissection and modelling of abiotic stress tolerance in plants. Curr Opin Plant Biol 13:206–212
Tripathi A, Goswami K, Mishra NS (2015) Role of bioinformatics in establishing microRNAs as modulators of abiotic stress responses: the new revolution. Front Physiol 26:286
Tyers M, Mann M (2003) From genomics to proteomics. Nature 422:193–197
Ueno S, Nakamura Y, Kobayashi M, Terashima S (2018) TodoFirGene: developing transcriptome resources for genetic analysis of Abies sachalinensis. Plant Cell Physiol 59(6):1276–1284
Urano K, Kurihara Y, Seki M, Shinozaki K (2010) ‘Omics’ analyses of regulatory networks in plant abiotic stress responses. Curr Opin Plant Biol 13:132–138
Vandepoele K (2017) A guide to the PLAZA 3.0 plant comparative genomic database. Plant genomics databases. Methods Mol Biol 1533. Humana Press, New York:183–200
Vassilev D, Leunissen JA, Atanassov A, Nenov A, Dimov G (2005) Application of bioinformatics in plant breeding. Wageningen University, Netherland
Vassilev D, Nenov A, Atanassov A, Dimov G, Getov L (2006) Application of bioinformatics infruit plant breeding. J Fruit Ornamental Plant Res 14:145–162
Verma M, Kumar V, Patel RK, Garg R, Jain M (2015) CTDB: an integrated chickpea transcriptome database for functional and applied genomics. PLoS One 10:0136880
Wanchana SS, Thongjuea VJ, Ulat M, Anacleto R, Mauleon M, Conte M, Rouard M, Wang B, Sun YF, Song N, Wei JP, Wang XJ et al (2018) MicroRNAs involving in cold, wounding and salt stresses in Triticumaestivum L., Plant Physiology and Biochemistry In press. Nucleic Acids Res 36:D943–D946
Wang B, Sun YF, Song N, Wei JP, Wang XJ et al (2014) MicroRNAs involving in cold, wounding and salt stresses in Triticum aestivum L. Plant Physiol Biochem 80:90–96
Winnenburg R, Baldwin TK, Urban M, Rawlings C, Köhler J, Hammond-Kosack KE (2006) Nucleic Acids Res 34:D459–D464
Wojciech M, Karlowski, Schoof H, Janakiraman V, Stuempflen V, Mayer KFX (2003) MOsDB: an integrated information resource for rice genomics. Nucleic Acids Res 31(1):190–192
Yan S, Du X, Wu F et al (2014) Proteomics insights into the basis of interspecific facilitation formaize (Zea mays) in faba bean (Vicia faba)/maize intercropping. J Proteome 109:111–124
Yang X, Li L (2011) miRDeep-P: a computational tool for analyzing the microRNA transcriptome in plants. Bioinformatics 27:2614–2615
Yu J, Hu S et al (2002) A draft sequence of rice genome. Science 296:79–92
Yuan JS, Galbraith DW, Dai SY, Griffin P, Stewart CN Jr (2008) Plant systems biology comes of age. Trends Plant Sci 13:165–171
Zhang Z, Yu J, Li D, Zhang Z, Liu F, Zhou X, Wang T, Ling Y, Su Z (2010) PMRD: plant microRNA database. Nucleic Acids Res 38:D806–D813
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 Biol 13:1–8
Zhang M, Lv D, Ge P et al (2014) Phosphoproteome analysis reveals new drought response and defense mechanisms of seedling leaves in bread wheat (Triticum aestivum L.). J Proteome 109:290–308
Zhou X, Wang G, Sutoh K, Zhu JK, Zhang W (2008) Identification of cold-inducible microRNAs in plants by transcriptome analysis. Biochim Biophys Acta 1779:780–788
Zhou L, Liu Y, Liu Z, Kong D, Duan M et al (2010) Genome-wide identification and analysis of drought-responsive microRNAs in Oryza sativa. J Exp Bot 61:4157–4168
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Shafi, A., Zahoor, I. (2019). Bioinformatics and Plant Stress Management. In: Hakeem, K., Shaik, N., Banaganapalli, B., Elango, R. (eds) Essentials of Bioinformatics, Volume III. Springer, Cham. https://doi.org/10.1007/978-3-030-19318-8_3
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