Abstract
The continuous development of analytical and experimental technologies as well as instruments resulted in the development of very specialized experimental approaches that can identify, measure and quantify particular types of cellular molecules. These technologies are known as “Omics Technologies”. Most of the omics technologies are high throughput with very fast data generation rates and humongous outputs. Thus, they are highly dependent on bioinformatics and computational tools. These technologies have made noticeable contributions to the current advancements in our understanding of plant biology in general and plant stress tolerance and response in particular. In this chapter, we will introduce the main omics technologies employed in plant biology and the bioinformatics platforms associated with them.
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References
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. doi:10.1002/biot.201400063
Altschul SF, Gish W, Miller W, et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410.
Ansong C, Purvine SO, Adkins JN, et al (2008) Proteogenomics: needs and roles to be filled by proteomics in genome annotation. Br Funct Genomic Proteomic 7:50–62.
Arentz G, Weiland F, Oehler MK, Hoffmann P (2014) State of the art of 2D DIGE. Proteomics Clin Appl 9:277-288. doi:10.1002/prca.201400119
Armengaud J (2010) Proteogenomics and systems biology: quest for the ultimate missing parts. Expert Rev Proteomics 7:65–77. doi:10.1586/epr.09.104
Baerenfaller K, Grossmann J, Grobei MA, et al (2008) Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320:938–941
Batley J, Edwards D (2009) Genome sequence data: management, storage, and visualization. Biotechniques 46:333–334., 336. doi:10.2144/000113134
Behnke K, Kaiser A, Zimmer I, et al (2010) RNAi-mediated suppression of isoprene emission in poplar transiently impacts phenolic metabolism under high temperature and high light intensities: a transcriptomic and metabolomic analysis. Plant Mol Biol 74:61–75. doi:10.1007/s11103-010-9654-z
Bindschedler L V, Burgis TA, Mills DJS, et al (2009) In planta proteomics and proteogenomics of the biotrophic barley fungal pathogen Blumeria graminis f. sp. hordei. Mol Cell Proteomics MCP 8:2368–2381. doi:10.1074/mcp.M900188-MCP200
Borchert N, Dieterich C, Krug K, et al (2010) Proteogenomics of Pristionchus pacificus reveals distinct proteome structure of nematode models. Genome Res 20:837–846. doi:10.1101/gr.103119.109
Borkotoky S, Saravanan V, Jaiswal A, et al (2013) The Arabidopsis stress responsive gene database. Int J Plant Genomics 2013:949564. doi:10.1155/2013/949564
Bringans S, Hane JK, Casey T, et al (2009) Deep proteogenomics; high throughput gene validation by multidimensional liquid chromatography and mass spectrometry of proteins from the fungal wheat pathogen Stagonospora nodorum. BMC Bioinformatics 10:301. doi:10.1186/1471-2105-10-301
Broughton WJ, Hernández G, Blair M, et al (2003) Beans (Phaseolus spp.)—model food legumes. Plant and Soil 252:55–128. doi:10.1023/A:1024146710611
Cao X, Zhou P, Zhang X, et al (2005) Identification of an RNA silencing suppressor from a plant double-stranded RNA virus. J Virol 79:13018–13027. doi:10.1128/JVI.79.20.13018-13027.2005
Cargile BJ, Bundy JL, Freeman TW, Stephenson Jr. JL (2004) Gel based isoelectric focusing of peptides and the utility of isoelectric point in protein identification. J Proteome Res 3:112–119.
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.
Castellana NE, Shen Z, He Y, et al (2014) An automated proteogenomic method uses mass spectrometry to reveal novel genes in Zea mays. Mol Cell Proteomics MCP 13:157–167. doi:10.1074/mcp.M113.031260
Chapman B, Bellgard M (2014) High-throughput parallel proteogenomics: a bacterial case study. Proteomics 14:2780–2789. doi:10.1002/pmic.201400185
Chen S, Jiang J, Li H, Liu G (2012) The salt-responsive transcriptome of Populus simonii × Populus nigra via DGE. Gene 504:203–212. doi:10.1016/j.gene.2012.05.023
Cheng H, Deng W, Wang Y, et al (2014) dbPPT: a comprehensive database of protein phosphorylation in plants. Database 2014:bau121. doi:10.1093/database/bau121
Claudine Chaouiya (2012). Logical Modelling of Gene Regulatory Networks with GINsim in Methods in molecular biology Edited by N.J. Clifton, Humana Press, Print ISBN 1940-6029
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. doi:10.1073/pnas.0406069101
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. doi:10.1021/pr500176c
Cramer, Rainer, Westermeier R (2012) Difference Gel Electrophoresis (DIGE) - Methods and Protocols. Humana Press, Print ISBN: 9781617795732
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. doi:10.1111/pce.12106
de Groot A, Dulermo R, Ortet P, et al (2009) Alliance of proteomics and genomics to unravel the specificities of Sahara bacterium Deinococcus deserti. PLoS Genet 5:e1000434. doi:10.1371/journal.pgen.1000434
Deborde C, Jacob D (2014) MeRy-B, a metabolomic database and knowledge base for exploring plant primary metabolism. Methods Mol Biol 1083:3–16. doi:10.1007/978-1-62703-661-0_1
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. doi:10.1073/pnas.0905240106
Denef VJ, Kalnejais LH, Mueller RS, et al (2010) Proteogenomic basis for ecological divergence of closely related bacteria in natural acidophilic microbial communities. Proc Natl Acad Sci U S A 107:2383–2390. doi:10.1073/pnas.0907041107
Duque AS, de Almeida AM, da Silva AB, da Silva JM, et al (2013) Abiotic stress—plant responses and applications in agriculture. doi:10.5772/45842
El-Metwally S, Hamza T, Zakaria M, Helmy M (2013) Next-generation sequence assembly: four stages of data processing and computational challenges. PLoS Comput Biol 9:e1003345. doi:10.1371/journal.pcbi.1003345
El-Metwally S, Ouda OM, Helmy M (2014a) Next generation sequencing technologies and challenges in sequence assembly.
El-Metwally S, Ouda OM, Helmy M (2014b) First- and next-generations sequencing methods. Next Gener Seq Technol Challenges Seq Assem. doi:10.1007/978-1-4939-0715-1_3
El-Metwally S, Ouda OM, Helmy M (2014c) New horizons in next-generation sequencing. Next Gener Seq Technol Challenges Seq Assem. doi:10.1007/978-1-4939-0715-1_6
El-Metwally S, Ouda OM, Helmy M (2014d) Assessment of next-generation sequence assembly. Next Gener Seq Technol Challenges Seq Assem. doi:10.1007/978-1-4939-0715-1_10
El-Metwally S, Ouda OM, Helmy M (2014e) Novel next-generation sequencing applications. Next Gener Seq Technol Challenges Seq Assem. doi:10.1007/978-1-4939-0715-1_7
El-Metwally S, Ouda OM, Helmy M (2014f) Next-generation sequence assembly overview. Next Gener Seq Technol Challenges Seq Assem. doi:10.1007/978-1-4939-0715-1_8
El-Metwally S, Ouda OM, Helmy M (2014g) Next-generation sequence assemblers. Next Gener Seq Technol Challenges Seq Assem. doi:10.1007/978-1-4939-0715-1_11
Eng JK, McCormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:976–989. doi:10.1016/1044-0305(94)80016-2
Evers D, Legay S, Lamoureux D, et al (2012) Towards a synthetic view of potato cold and salt stress response by transcriptomic and proteomic analyses. Plant Mol Biol 78:503–514. doi:10.1007/s11103-012-9879-0
Falda M, Toppo S, Pescarolo A, et al (2012) Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms. BMC Bioinformatics 13(Suppl 4):S14. doi:10.1186/1471-2105-13-S4-S14
Franceschini A, Szklarczyk D, Frankild S, et al (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41:D808–15. doi:10.1093/nar/gks1094
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 Genet Genomics 281:607. doi:10.1007/s00438-009-0441-y
Helmy M, Tomita M, Ishihama Y (2011) OryzaPG-DB: rice proteome database based on shotgun proteogenomics. BMC Plant Biol 11:63. doi:10.1186/1471-2229-11-63
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. doi:10.1111/j.1365-2443.2012.01615.x
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. doi:10.3389/fpls.2012.00065
Helmy M, Tomita M, Ishihama Y (2012c) Peptide identification by searching large-scale tandem mass spectra against large databases: bioinformatics methods in proteogenomics. Gene Genome Genomics 6:76–85.
Helmy M, Crits-Christoph A, Bader GD, et al (2016) Ten simple rules for developing public biological databases. PLoS Comput Biol 12:e1005128. doi:10.1371/journal.pcbi.1005128
Henry VJ, Bandrowski AE, Pepin A-S, et al (2014) OMICtools: an informative directory for multi-omic data analysis. Database (Oxford) 2014:bau069. doi:10.1093/database/bau069
Hernández G, Ramírez M, Valdés-López O, et al (2007) Phosphorus stress in common bean: root transcript and metabolic responses. Plant Physiol 144:752–767. doi:10.1104/pp.107.096958
Hopff D, Wienkoop S, Lüthje S (2013) The plasma membrane proteome of maize roots grown under low and high iron conditions. J Proteomics 91:605–618. doi:10.1016/j.jprot.2013.01.006
Ilian Badjakov, Violeta Kondakova and Atanas Atanassov (2012). Current View on Fruit Quality in Relation to Human Health in Sustainable Agriculture and New Biotechnologies, Edited by Noureddine Benkeblia, CRC Press, Boca Raton, Pages 303–319, Print ISBN: 978-1-4398-2504-4, eBook ISBN: 978-1-4398-2505-1. doi: 10.1201/b10977-14
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. doi:10.3109/07388551.2012.659174
Jung S, Main D (2014) Genomics and bioinformatics resources for translational science in Rosaceae. Plant Biotechnol Rep 8:49–64. doi:10.1007/s11816-013-0282-3
Karányi Z, Holb I, Hornok L, et al (2013) FSRD: fungal stress response database. Database (Oxford) 2013:bat037. doi:10.1093/database/bat037
Karolchik D, Baertsch R, Diekhans M, et al (2003) The UCSC genome browser database. Nucleic Acids Res 31:51–54.
Kawahara Y, Oono Y, Kanamori H, et al (2012) Simultaneous RNA-seq analysis of a mixed transcriptome of rice and blast fungus interaction. PLoS One 7:e49423. doi:10.1371/journal.pone.0049423
Kim P-G, Cho H-G, Park K (2008) A scaffold analysis tool using mate-pair information in genome sequencing. J Biomed Biotechnol 2008:675741. doi:10.1155/2008/675741
Komatsu S, Tanaka N (2005) Rice proteome analysis: a step toward functional analysis of the rice genome. Proteomics 5:938–949.
Komatsu S, Kamal AHM, Hossain Z (2014) Wheat proteomics: proteome modulation and abiotic stress acclimation. Front Plant Sci 5:684. doi:10.3389/fpls.2014.00684
Kuo T-C, Tian T-F, Tseng YJ (2013) 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst Biol 7:64. doi:10.1186/1752-0509-7-64
Lasonder E, Ishihama Y, Andersen JS, et al (2002) Analysis of the Plasmodium falciparum proteome by high-accuracy mass spectrometry. Nature 419:537–542.
Lassowskat I, Böttcher C, Eschen-Lippold L, et al (2014) Sustained mitogen-activated protein kinase activation reprograms defense metabolism and phosphoprotein profile in Arabidopsis thaliana. Front Plant Sci 5:554. doi:10.3389/fpls.2014.00554
Le DT, Nishiyama R, Watanabe Y, 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:e49522. doi:10.1371/journal.pone.0049522
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. doi:10.1016/j.plaphy.2014.12.003
Loevenich SN, Brunner E, King NL, et al (2009) The Drosophila melanogaster PeptideAtlas facilitates the use of peptide data for improved fly proteomics and genome annotation. BMC Bioinformatics 10:59. doi:10.1186/1471-2105-10-59
Lopes CT, Franz M, Kazi F, et al (2010) Cytoscape web: an interactive web-based network browser. Bioinformatics 26:2347–2348. doi:10.1093/bioinformatics/btq430
Margaria P, Abbà S, Palmano S (2013) Novel aspects of grapevine response to phytoplasma infection investigated by a proteomic and phospho-proteomic approach with data integration into functional networks. BMC Genomics 14:38. doi:10.1186/1471-2164-14-38
Matthews DE, Lazo GR, Anderson OD (2009) Plant and crop databases. Methods Mol Biol 513:243–262. doi:10.1007/978-1-59745-427-8_13
McDowall MD, Scott MS, Barton GJ (2009) PIPs: human protein-protein interaction prediction database. Nucleic Acids Res 37:D651–6. doi:10.1093/nar/gkn870
Mochida K, Shinozaki K (2010) Genomics and bioinformatics resources for crop improvement. Plant Cell Physiol 51:497–523. doi:10.1093/pcp/pcq027
Mochida K, Shinozaki K (2011) Advances in omics and bioinformatics tools for systems analyses of plant functions. Plant Cell Physiol 52:2017–2038. doi:10.1093/pcp/pcr153
Naika M, Shameer K, Mathew OK, et al (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:e8. doi:10.1093/pcp/pcs185
Nakagami H, Sugiyama N, Ishihama Y, Shirasu K (2012) Shotguns in the front line: phosphoproteomics in plants. Plant Cell Physiol 53:118–124. doi:10.1093/pcp/pcr148
Ono K, Demchak B, Ideker T (2014) Cytoscape tools for the web age: D3.js and cytoscape.js exporters. F1000Research 3:143. doi:10.12688/f1000research.4510.2
Orozco A, Morera J, Jiménez S, Boza R (2013) A review of bioinformatics training applied to research in molecular medicine, agriculture and biodiversity in Costa Rica and Central America. Brief Bioinform 14:661–670. doi:10.1093/bib/bbt033
Pang CNI, Tay AP, Aya C, et al (2014) Tools to covisualize and coanalyze proteomic data with genomes and transcriptomes: validation of genes and alternative mRNA splicing. J Proteome Res 13:84–98. doi:10.1021/pr400820p
Perkins DN, Pappin DJ, Creasy DM, Cottrell JS (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551–3567.
Polpitiya AD, Qian W-J, Jaitly N, et al (2008) DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 24:1556–1558. doi:10.1093/bioinformatics/btn217
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 (Oxford) 2013:bat027. doi:10.1093/database/bat027
Ramegowda V, Senthil-kumar M, Udayakumar M, Mysore KS (2013) A high-throughput virus-induced gene silencing protocol identifies genes involved in multi-stress tolerance. BMC Plant Biol 13:193. doi:10.1186/1471-2229-13-193
Ramegowda V, Mysore KS, Senthil-Kumar M (2014) Virus-induced gene silencing is a versatile tool for unraveling the functional relevance of multiple abiotic-stress-responsive genes in crop plants. Front Plant Sci 5:323. doi:10.3389/fpls.2014.00323
Risk BA, Spitzer WJ, Giddings MC (2013) Peppy: proteogenomic search software. J Proteome Res 12:3019–3025. doi:10.1021/pr400208w
Saeed AI, Sharov V, White J, et al (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34:374–378.
Saito K, Matsuda F (2010) Metabolomics for functional genomics, systems biology, and biotechnology. Annu Rev Plant Biol 61:463–489. doi:10.1146/annurev.arplant.043008.092035
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. doi:10.1371/journal.pone.0070321
Shannon P, Markiel A, Ozier O, et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. doi:10.1101/gr.1239303
Shao S, Guo T, Aebersold R (2014) Mass spectrometry-based proteomic quest for diabetes biomarkers. Biochim Biophys Acta doi:10.1016/j.bbapap.2014.12.012
Shinozaki K, Sakakibara H (2009) Omics and bioinformatics: an essential toolbox for systems analyses of plant functions beyond 2010. Plant Cell Physiol 50:1177–1180. doi:10.1093/pcp/pcp085
Sicher RC, Barnaby JY (2012) Impact of carbon dioxide enrichment on the responses of maize leaf transcripts and metabolites to water stress. Physiol Plant 144:238–253. doi:10.1111/j.1399-3054.2011.01555.x
Smalter Hall A, Shan Y, Lushington G, Visvanathan M (2013) An overview of computational life science databases & exchange formats of relevance to chemical biology research. Comb Chem High Throughput Screen 16:189–198
Smita S, Lenka SK, Katiyar A, et al (2011) QlicRice: a web interface for abiotic stress responsive QTL and loci interaction channels in rice. Database (Oxford) 2011:bar037. doi:10.1093/database/bar037
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.
Srivastava V, Obudulu O, Bygdell J, et al (2013) OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants. BMC Genomics 14:893. doi:10.1186/1471-2164-14-893
Stanke M, Morgenstern B (2005) AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res 33:W465–7. doi:10.1093/nar/gki458
Stein LD, Mungall C, Shu S, et al (2002) The generic genome browser: a building block for a model organism system database. Genome Res 12:1599–1610.
Sugiyama N, Nakagami H, Mochida K, et al (2008) Large-scale phosphorylation mapping reveals the extent of tyrosine phosphorylation in Arabidopsis. Mol Syst Biol 4:193.
Tatusova TA, Madden TL (1999) BLAST 2 Sequences, a new tool for comparing protein and nucleotide sequences. FEMS Microbiol Lett 174:247–250.
Tress ML, Bodenmiller B, Aebersold R, Valencia A (2008) Proteomics studies confirm the presence of alternative protein isoforms on a large scale. Genome Biol 9:R162. doi:10.1186/gb-2008-9-11-r162
Tyers M, Mann M (2003) From genomics to proteomics. Nature 422:193–197.
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. doi:10.1016/j.pbi.2009.12.006
Wang R, Fabregat A, Ríos D, et al (2012) PRIDE Inspector: a tool to visualize and validate MS proteomics data. Nat Biotechnol 30:135–137. doi:10.1038/nbt.2112
Wang M, Wang Q, Zhang B (2013) Evaluation and selection of reliable reference genes for gene expression under abiotic stress in cotton (Gossypium hirsutum L.). Gene 530:44–50. doi:10.1016/j.gene.2013.07.084
Wilkinson PA, Winfield MO, Barker GLA, et al (2012) CerealsDB 2.0: an integrated resource for plant breeders and scientists. BMC Bioinformatics 13:219. doi:10.1186/1471-2105-13-219
Wright JC, Sugden D, Francis-McIntyre S, et al (2009) Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger. BMC Genomics 10:61.
Yachdav G, Kloppmann E, Kajan L, et al (2014) PredictProtein—an open resource for online prediction of protein structural and functional features. Nucleic Acids Res 42:W337–43. doi:10.1093/nar/gku366
Yadav R, Arora P, Kumar S, Chaudhury A (2010) Perspectives for genetic engineering of poplars for enhanced phytoremediation abilities. Ecotoxicology 19:1574–1588. doi:10.1007/s10646-010-0543-7
Yan S, Du X, Wu F, et al (2014) Proteomics insights into the basis of interspecific facilitation for maize (Zea mays) in faba bean (Vicia faba)/maize intercropping. J Proteomics 109:111–124. doi:10.1016/j.jprot.2014.06.027
Yang F, Melo-Braga MN, Larsen MR, et al (2013) Battle through signaling between wheat and the fungal pathogen Septoria tritici revealed by proteomics and phosphoproteomics. Mol Cell Proteomics MCP 12:2497–2508. doi:10.1074/mcp.M113.027532
Yao D, Zhang X, Zhao X, et al (2011) Transcriptome analysis reveals salt-stress-regulated biological processes and key pathways in roots of cotton (Gossypium hirsutum L.). Genomics 98:47–55. doi:10.1016/j.ygeno.2011.04.007
Yu J, Zhao M, Wang X, et al (2013) Bolbase: a comprehensive genomics database for Brassica oleracea. BMC Genomics 14:664. doi:10.1186/1471-2164-14-664
Zhang M, Lv D, Ge P, et al (2014a) Phosphoproteome analysis reveals new drought response and defense mechanisms of seedling leaves in bread wheat (Triticum aestivum L.). J Proteomics 109:290–308. doi:10.1016/j.jprot.2014.07.010
Zhang Y, Cheng Y, Guo J, et al (2014b) Comparative transcriptome analysis to reveal genes involved in wheat hybrid necrosis. Int J Mol Sci 15:23332–23344. doi:10.3390/ijms151223332
Zhao H, Peng Z, Fei B, et al (2014) BambooGDB: a bamboo genome database with functional annotation and an analysis platform. Database (Oxford) 2014:bau006. doi:10.1093/database/bau006
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Mosa, K.A., Ismail, A., Helmy, M. (2017). Omics and System Biology Approaches in Plant Stress Research. In: Plant Stress Tolerance. SpringerBriefs in Systems Biology. Springer, Cham. https://doi.org/10.1007/978-3-319-59379-1_2
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