目前有許多高通量技術，例如生物晶片和次世代定序，被應用於分析並了解植物在不同生長發育階段與逆境之下的基因表達的變化情形。ㄧ般而言，具有差異性表達的基因在許多生物途徑上都扮演非常重要的調控角色。現今已有許多生物資源收錄這些高通量的基因表達資料，然而，要如何從這些基因表現大數據中找出具有生物價值的調控機制則顯得格外重要。先前實驗室的成員建立了EXPath資料庫，可以針對植物高通量基因表達資料與代謝途徑進行整合性的分析，以了解不同生物或非生物逆境下，差異表達基因所參與的代謝途徑與功能。可惜的是，EXPath目前僅能分析阿拉伯芥、水稻和玉米，無法對其他植物進行分析。另一方面，也無法透過EXPath了解有哪些轉錄因子參與在代謝途徑的調控網路上。因此，本研究的目的是希望能改善EXPath的缺點以提高它的實用性。其主要的改善項目分別為：(1)增加三個模式經濟作物（苜蓿、大豆、番茄）的相關資訊。(2)增加植物不同生長發育階段的基因表現數據。(3)提供轉錄因子調控資訊，例如：了解有哪些轉錄因子參與在同一代謝途徑或是共表達(co-expression)基因群中。(4)建構一群基因在不同條件下之相關性網路(correlation networks)。(5)提供使用者上傳表現量資料進行代謝途徑與轉錄因子調節機制的比較分析。我們希望透過EXPath資料庫的更新，能夠對植物代謝途徑的調控機制有更多的了解，此研究成果能透過http://expath.itps.ncku.edu.tw/網頁中取得相關資訊。

In order to have a healthy life cycle and adapt to environmental stresses, gene expression is accurately regulated in plant. Generally, genes with differentially expressed values or similar expression pattern (co-expression) among different samples might play critical roles in some specific biological pathways. In addition, transcription factor (TF) and their binding sites (TFBSs) are essential regulator in numerous biological processes. Therefore, investigation of TF/TFBSs in a regulatory network can help to understand plant adaptation strategy. Although co-expressed genes and their related metabolic pathways could be easily identified from previous resources such as EXPath and EXPath Tool. However, the analysis of TFs regulation and organ/tissue/stage- specific genes cannot be retrieved from those platforms. In this EXPath update version (EXPath 2.0), 1881 microarray samples from various developmental stages and stresses from six plants including Arabidopsis, rice, maize, Medicago, soybean and tomato were integrated, respectively. Besides updating the pre-existing five functions (Gene Search, Pathway Search, DEGs Search, Pathway/GO Enrichment, Co-expression Analysis), five significant improvements were developed and differed from previous version. (i) Extending high-throughput data from three to six plants. (ii) Stage-specific genes can be retrieved from various developmental stages. (iii) A correlation network according to a group of input genes can be constructed. (iv) TFs in a specific pathway or co-expression network can be investigated. (v) Comparative analysis of metabolic pathways and TF regulatory mechanisms in an input gene group are available. EXPath 2.0 is a conveniently resource for investigating gene expression in metabolic pathways under specific conditions and facilitates users to access the regulatory mechanisms of the critical bio-pathways from plant high-throughput data. This database is freely available at http://EXPath.itps.ncku.edu.tw.

Aya, K., Ueguchi-Tanaka, M., Kondo, M., Hamada, K., Yano, K., Nishimura, M., & Matsuoka, M. (2009). Gibberellin modulates anther development in rice via the transcriptional regulation of GAMYB. Plant Cell, 21(5), 1453-1472. doi:10.1105/tpc.108.062935
Bak, S., Beisson, F., Bishop, G., Hamberger, B., Hofer, R., Paquette, S., & Werck-Reichhart, D. (2011). Cytochromes p450. Arabidopsis Book, 9, e0144. doi:10.1199/tab.0144
Barah, P., B, N. M., Jayavelu, N. D., Sowdhamini, R., Shameer, K., & Bones, A. M. (2016). Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses. Nucleic Acids Res, 44(7), 3147-3164. doi:10.1093/nar/gkv1463
Beltrame, L., Bianco, L., Fontana, P., & Cavalieri, D. (2013). Pathway Processor 2.0: a web resource for pathway-based analysis of high-throughput data. Bioinformatics, 29(14), 1825-1826. doi:10.1093/bioinformatics/btt292
Bombarely, A., Menda, N., Tecle, I. Y., Buels, R. M., Strickler, S., Fischer-York, T., . . . Mueller, L. A. (2011). The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl. Nucleic Acids Res, 39(Database issue), D1149-1155. doi:10.1093/nar/gkq866
Boratyn, G. M., Camacho, C., Cooper, P. S., Coulouris, G., Fong, A., Ma, N., . . . Zaretskaya, I. (2013). BLAST: a more efficient report with usability improvements. Nucleic Acids Res, 41(Web Server issue), W29-33. doi:10.1093/nar/gkt282
Cazzonelli, C. I., & Pogson, B. J. (2010). Source to sink: regulation of carotenoid biosynthesis in plants. Trends Plant Sci, 15(5), 266-274. doi:10.1016/j.tplants.2010.02.003
Cheadle, C., Vawter, M. P., Freed, W. J., & Becker, K. G. (2003). Analysis of microarray data using Z score transformation. J Mol Diagn, 5(2), 73-81. doi:10.1016/S1525-1578(10)60455-2
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 Suppl 2, S6. doi:10.1186/1471-2164-16-S2-S6
Chow, C. N., Zheng, H. Q., Wu, N. Y., Chien, C. H., Huang, H. D., Lee, T. Y., . . . Chang, W. C. (2016). PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants. Nucleic Acids Res, 44(D1), D1154-1160. doi:10.1093/nar/gkv1035
Cunningham, F. X. (2002). Regulation of carotenoid synthesis and accumulation in plants. Pure and Applied Chemistry, 74(8), 1409-1417. doi:DOI 10.1351/pac200274081409
Edgar, R., Domrachev, M., & Lash, A. E. (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 30(1), 207-210.
Garg, R., Tyagi, A. K., & Jain, M. (2012). Microarray analysis reveals overlapping and specific transcriptional responses to different plant hormones in rice. Plant Signal Behav, 7(8), 951-956. doi:10.4161/psb.20910
Garrity, G. M., Banfield, J., Eisen, J., van der Lelie, N., McMahon, T., Rusch, D., . . . Stepanauskas, R. (2013). Prokaryotic Super Program Advisory Committee DOE Joint Genome Institute, Walnut Creek, CA, March 27, 2013. Stand Genomic Sci, 8(3), 561-570. doi:10.4056/sigs.4638348
Garwin, J. L., Klages, A. L., & Cronan, J. E. (1980). Structural, Enzymatic, and Genetic-Studies of Beta-Ketoacyl-Acyl Carrier Protein Synthases I and Ii Escherichia-Coli. Journal of Biological Chemistry, 255(24), 1949-1956.
Gautier, L., Cope, L., Bolstad, B. M., & Irizarry, R. A. (2004). affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 20(3), 307-315. doi:10.1093/bioinformatics/btg405
Goda, H., Sasaki, E., Akiyama, K., Maruyama-Nakashita, A., Nakabayashi, K., Li, W. Q., . . . Shimada, Y. (2008). The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access. Plant Journal, 55(3), 526-542. doi:10.1111/j.1365-313X.2008.03510.x
Goel, A. K., Lundberg, D., Torres, M. A., Matthews, R., Akimoto-Tomiyama, C., Farmer, L., . . . Grant, S. R. (2008). The Pseudomonas syringae type III effector HopAM1 enhances virulence on water-stressed plants. Mol Plant Microbe Interact, 21(3), 361-370. doi:10.1094/MPMI-21-3-0361
Grant, D., Nelson, R. T., Cannon, S. B., & Shoemaker, R. C. (2010). SoyBase, the USDA-ARS soybean genetics and genomics database. Nucleic Acids Res, 38(Database issue), D843-846. doi:10.1093/nar/gkp798
Guo M, L. J., Ma X, Luo DX, Gong ZH, Lu MH. (2016). The Plant Heat Stress Transcription Factors (HSFs): Structure, Regulation, and Function in Response to Abiotic Stresses. Front Plant Sci., 7, 114.
Harper, L., Gardiner, J., Andorf, C., & Lawrence, C. J. (2016). MaizeGDB: The Maize Genetics and Genomics Database. Methods Mol Biol, 1374, 187-202. doi:10.1007/978-1-4939-3167-5_9
Irizarry, R. A., Bolstad, B. M., Collin, F., Cope, L. M., Hobbs, B., & Speed, T. P. (2003). Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res, 31(4), e15.
Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., & Speed, T. P. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2), 249-264. doi:10.1093/biostatistics/4.2.249
Jin, J., Tian, F., Yang, D. C., Meng, Y. Q., Kong, L., Luo, J., & Gao, G. (2017). PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants. Nucleic Acids Res, 45(D1), D1040-D1045. doi:10.1093/nar/gkw982
Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K. F., Itoh, M., Kawashima, S., . . . Hirakawa, M. (2006). From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res, 34(Database issue), D354-357. doi:10.1093/nar/gkj102
Kapushesky, M., Adamusiak, T., Burdett, T., Culhane, A., Farne, A., Filippov, A., . . . Brazma, A. (2012). Gene Expression Atlas update--a value-added database of microarray and sequencing-based functional genomics experiments. Nucleic Acids Res, 40(Database issue), D1077-1081. doi:10.1093/nar/gkr913
Kardailsky, I., Shukla, V. K., Ahn, J. H., Dagenais, N., Christensen, S. K., Nguyen, J. T., . . . Weigel, D. (1999). Activation tagging of the floral inducer FT. Science, 286(5446), 1962-1965. doi:DOI 10.1126/science.286.5446.1962
Khachik, F., London, E., de Moura, F. F., Johnson, M., Steidl, S., Detolla, L., . . . Fowler, B. (2006). Chronic ingestion of (3R,3'R,6'R)-lutein and (3R,3'R)-zeaxanthin in the female rhesus macaque. Invest Ophthalmol Vis Sci, 47(12), 5476-5486. doi:10.1167/iovs.06-0194
Kobayashi, Y., Kaya, H., Goto, K., Iwabuchi, M., & Araki, T. (1999). A pair of related genes with antagonistic roles in mediating flowering signals. Science, 286(5446), 1960-1962. doi:DOI 10.1126/science.286.5446.1960
Koornneef, M., Alonso-Blanco, C., Blankestijn-de Vries, H., Hanhart, C. J., & Peeters, A. J. M. (1998). Genetic interactions among late-flowering mutants of Arabidopsis. Genetics, 148(2), 885-892.
Kosma, D. K., Bourdenx, B., Bernard, A., Parsons, E. P., Lu, S., Joubes, J., & Jenks, M. A. (2009). The Impact of Water Deficiency on Leaf Cuticle Lipids of Arabidopsis. Plant Physiology, 151(4), 1918-1929. doi:10.1104/pp.109.141911
Krishnakumar, V., Kim, M., Rosen, B. D., Karamycheva, S., Bidwell, S. L., Tang, H., & Town, C. D. (2015). MTGD: The Medicago truncatula genome database. Plant Cell Physiol, 56(1), e1. doi:10.1093/pcp/pcu179
Kuan, J. (2015). Learning highcharts 4: Packt Publishing Ltd.
Lamesch, P., Berardini, T. Z., Li, D., Swarbreck, D., Wilks, C., Sasidharan, R., . . . Huala, E. (2012). The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res, 40(Database issue), D1202-1210. doi:10.1093/nar/gkr1090
Li, J., Zhu, L., Hull, J. J., Liang, S., Daniell, H., Jin, S., & Zhang, X. (2016). Transcriptome analysis reveals a comprehensive insect resistance response mechanism in cotton to infestation by the phloem feeding insect Bemisia tabaci (whitefly). Plant Biotechnol J, 14(10), 1956-1975. doi:10.1111/pbi.12554
Lopes, C. T., Franz, M., Kazi, F., Donaldson, S. L., Morris, Q., & Bader, G. D. (2010). Cytoscape Web: an interactive web-based network browser. Bioinformatics, 26(18), 2347-2348. doi:10.1093/bioinformatics/btq430
Lu, S., & Li, L. (2008). Carotenoid metabolism: biosynthesis, regulation, and beyond. J Integr Plant Biol, 50(7), 778-785. doi:10.1111/j.1744-7909.2008.00708.x
Ma, G., Zhang, L., Yungyuen, W., Tsukamoto, I., Iijima, N., Oikawa, M., . . . Kato, M. (2016). Expression and functional analysis of citrus carotene hydroxylases: unravelling the xanthophyll biosynthesis in citrus fruits. Bmc Plant Biology, 16(1), 148. doi:10.1186/s12870-016-0840-2
Ma, L., Li, J., Qu, L., Hager, J., Chen, Z., Zhao, H., & Deng, X. W. (2001). Light control of Arabidopsis development entails coordinated regulation of genome expression and cellular pathways. Plant Cell, 13(12), 2589-2607.
Ma, N. N., Feng, H. L., Meng, X., Li, D., Yang, D. Y., Wu, C. G., & Meng, Q. W. (2014). Overexpression of tomato SlNAC1 transcription factor alters fruit pigmentation and softening. Bmc Plant Biology, 14. doi:ARTN 351 10.1186/s12870-014-0351-y
Malone, J. H., & Oliver, B. (2011). Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol, 9, 34. doi:10.1186/1741-7007-9-34
Matousek, J., Piernikarczyk, R. J. J., Tycova, A., Duraisamy, G. S., Kocabek, T., & Steger, G. (2015). Expression of SANT/HTH Myb mRNA, a plant morphogenesis-regulating transcription factor, changes due to viroid infection. Journal of Plant Physiology, 183, 85-94. doi:10.1016/j.jplph.2015.06.001
Matys, V., Fricke, E., Geffers, R., Gossling, E., Haubrock, M., Hehl, R., . . . Wingender, E. (2003). TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res, 31(1), 374-378.
Mukaka, M. M. (2012). Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71.
Murray, F., Kalla, R., Jacobsen, J., & Gubler, F. (2003). A role for HvGAMYB in anther development. Plant J, 33(3), 481-491.
Naika, M., Shameer, K., Mathew, O. K., Gowda, R., & Sowdhamini, R. (2013). STIFDB2: an updated version of plant stress-responsive transcription factor database with additional stress signals, stress-responsive transcription factor binding sites and stress-responsive genes in Arabidopsis and rice. Plant Cell Physiol, 54(2), e8. doi:10.1093/pcp/pcs185
Naoumkina, M. A., He, X. Z., & Dixon, R. A. (2008). Elicitor-induced transcription factors for metabolic reprogramming of secondary metabolism in Medicago truncatula. Bmc Plant Biology, 8. doi:Artn 132 10.1186/1471-2229-8-132
Pandey, P., Ramegowda, V., & Senthil-Kumar, M. (2015). Shared and unique responses of plants to multiple individual stresses and stress combinations: physiological and molecular mechanisms. Front Plant Sci, 6, 723. doi:10.3389/fpls.2015.00723
Pandey, S. P., & Somssich, I. E. (2009). The role of WRKY transcription factors in plant immunity. Plant Physiol, 150(4), 1648-1655. doi:10.1104/pp.109.138990
Paponov, I. A., Paponov, M., Teale, W., Menges, M., Chakrabortee, S., Murray, J. A., & Palme, K. (2008). Comprehensive transcriptome analysis of auxin responses in Arabidopsis. Molecular Plant, 1(2), 321-337. doi:10.1093/mp/ssm021
Parkinson, H., Kapushesky, M., Shojatalab, M., Abeygunawardena, N., Coulson, R., Farne, A., . . . Brazma, A. (2007). ArrayExpress--a public database of microarray experiments and gene expression profiles. Nucleic Acids Res, 35(Database issue), D747-750. doi:10.1093/nar/gkl995
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
Probert, R. J., Daws, M. I., & Hay, F. R. (2009). Ecological correlates of ex situ seed longevity: a comparative study on 195 species. Annals of Botany, 104(1), 57-69. doi:10.1093/aob/mcp082
Quackenbush, J. (2002). Microarray data normalization and transformation. Nat Genet, 32 Suppl, 496-501. doi:10.1038/ng1032
Ran, X., Liu, J., Qi, M., Wang, Y., Cheng, J., & Zhang, Y. (2018). GSHR, a Web-Based Platform Provides Gene Set-Level Analyses of Hormone Responses in Arabidopsis. Front Plant Sci, 9, 23. doi:10.3389/fpls.2018.00023
Rejeb, I. B., Pastor, V., & Mauch-Mani, B. (2014). Plant Responses to Simultaneous Biotic and Abiotic Stress: Molecular Mechanisms. Plants (Basel), 3(4), 458-475. doi:10.3390/plants3040458
Righetti, K., Vu, J. L., Pelletier, S., Vu, B. L., Glaab, E., Lalanne, D., . . . Buitink, J. (2015). Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways. Plant Cell, 27(10), 2692-2708. doi:10.1105/tpc.15.00632
Rodriguez-Villalon, A., Gas, E., & Rodriguez-Concepcion, M. (2009). Phytoene synthase activity controls the biosynthesis of carotenoids and the supply of their metabolic precursors in dark-grown Arabidopsis seedlings. Plant Journal, 60(3), 424-435. doi:10.1111/j.1365-313X.2009.03966.x
RuizGarcia, L., Madueno, F., Wilkinson, M., Haughn, G., Salinas, J., & MartinezZapater, J. M. (1997). Different roles of flowering-time genes in the activation of floral initiation genes in Arabidopsis. Plant Cell, 9(11), 1921-1934.
Sakai, H., Lee, S. S., Tanaka, T., Numa, H., Kim, J., Kawahara, Y., . . . Itoh, T. (2013). Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics. Plant Cell Physiol, 54(2), e6. doi:10.1093/pcp/pcs183
Sato, Y., Takehisa, H., Kamatsuki, K., Minami, H., Namiki, N., Ikawa, H., . . . Nagamura, Y. (2013). RiceXPro Version 3.0: expanding the informatics resource for rice transcriptome. Nucleic Acids Research, 41(D1), D1206-D1213. doi:10.1093/nar/gks1125
Sharma, N., Bhalla, P. L., & Singh, M. B. (2013). Transcriptome-wide profiling and expression analysis of transcription factor families in a liverwort, Marchantia polymorpha. BMC Genomics, 14, 915. doi:10.1186/1471-2164-14-915
Suzuki, T., Maeda, A., Hirose, M., Ichinose, Y., Shiraishi, T., & Toyoda, K. (2017). Ultrastructural and Cytological Studies on Mycosphaerella pinodes Infection of the Model Legume Medicago truncatula. Front Plant Sci, 8, 1132. doi:10.3389/fpls.2017.01132
Tautz, D. (2000). Evolution of transcriptional regulation. Curr Opin Genet Dev, 10(5), 575-579.
Toledo-Ortiz, G., Huq, E., & Rodriguez-Concepcion, M. (2010). Direct regulation of phytoene synthase gene expression and carotenoid biosynthesis by phytochrome-interacting factors. Proc Natl Acad Sci U S A, 107(25), 11626-11631. doi:10.1073/pnas.0914428107
Welsch, R., Maass, D., Voegel, T., DellaPenna, D., & Beyer, P. (2007). Transcription factor RAP2.2 and its interacting partner SINAT2: Stable elements in the carotenogenesis of Arabidopsis leaves. Plant Physiology, 145(3), 1073-1085. doi:10.1104/pp.107.104828
Xu, P., & Cai, W. (2017). Functional characterization of the BnNCED3 gene in Brassica napus. Plant Science, 256, 16-24. doi:10.1016/j.plantsci.2016.11.012
Zheng, H. Q., Chiang-Hsieh, Y. F., Chien, C. H., Hsu, B. K., Liu, T. L., Chen, C. N., & Chang, W. C. (2014). AlgaePath: comprehensive analysis of metabolic pathways using transcript abundance data from next-generation sequencing in green algae. BMC Genomics, 15, 196. doi:10.1186/1471-2164-15-196
Zheng, H. Q., Wu, N. Y., Chow, C. N., Tseng, K. C., Chien, C. H., Hung, Y. C., . . . Chang, W. C. (2017). EXPath tool-a system for comprehensively analyzing regulatory pathways and coexpression networks from high-throughput transcriptome data. DNA Res. doi:10.1093/dnares/dsx009