Learning from co-expression networks: Possibilities and challenges

Frontiers in Plant Science

Volumn 7, Issue APR2016, 2016, Pages

  Serin, Elise A R ^a   Nijveen, Harm ^a   Hilhorst, Henk W M ^a   Ligterink, Wilco ^a  

^a WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

Co expression; Gene expression; Gene networks; Gene prioritization; Transcriptomics

Indexed keywords

EID: 84964258777     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2016.00444     Document Type: Review

References (166)

1. Albert, R. (2005). Scale-free networks in cell biology. J. Cell Sci. 118, 4947-4957. doi: 10. 1242/jcs. 02714
2. Alon, U. (2007). Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450-461. doi: 10. 1038/nrg2102
3. Aoki, K., Ogata, Y., and Shibata, D. (2007). Approaches for extracting practical information from gene co-expression networks in plant biology. Plant Cell Physiol. 48, 381-390. doi: 10. 1093/pcp/pcm013
4. Ashburner, M., Ball, C. A., Blake, J. A., Bolstein, D., Butler, H., Cherry, J. M., et al. (2000). Gene ontology: tool for unification of biology. Nat. Genet. 25, 25-29. doi: 10. 1038/75556
5. Atias, O., Chor, B., and Chamovitz, D. A. (2009). Large-scale analysis of Arabidopsis transcription reveals a basal co-regulation network. BMC Syst. Biol. 3: 86. doi: 10. 1186/1752-0509-3-86
6. Aya, K., Suzuki, G., Suwabe, K., Hobo, T., Takahashi, H., Shiono, K., et al. (2011). Comprehensive network analysis of anther-expressed genes in rice by the combination of 33 laser microdissection and 143 spatiotemporal microarrays. PLoS ONE 6: e26162. doi: 10. 1371/journal. pone. 0026162
7. Ballouz, S., Verleyen, W., and Gillis, J. (2015). Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics 31, 2123-2130. doi: 10. 1093/bioinformatics/btv118
8. Barabási, A. L., and Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113. doi: 10. 1038/nrg1272
9. Bar-Joseph, Z., Gitter, A., and Simon, I. (2012). Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genet. 13, 552-564. doi: 10. 1038/nrg3244
10. Bassel, G. W., Lan, H., Glaab, E., Gibbs, D. J., Gerjets, T., Krasnogor, N., et al. (2011). Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions. Proc. Natl. Acad. Sci. U. S. A. 108, 9709-9714. doi: 10. 1073/pnas. 1100958108
11. Becker, J. D., Takeda, S., Borges, F., Dolan, L., and Feijó, J. A. (2014). Transcriptional profiling of Arabidopsis root hairs and pollen defines an apical cell growth signature. BMC Plant Biol. 14: 197. doi: 10. 1186/s12870-014-0197-3
12. Belmonte, M. F., Kirkbride, R. C., Stone, S. L., Pelletier, J. M., Bui, A. Q., Yeung, E. C., et al. (2013). Comprehensive developmental profiles of gene activity in regions and subregions of the Arabidopsis seed. Proc. Natl. Acad. Sci. U. S. A. 110, E435-E444. doi: 10. 1073/pnas. 1222061110
13. Brady, S. M., Orlando, D. A., Lee, J.-Y., Koch, J., Dinneny, J. R., Mace, D., et al. (2007). A high-resolution root spatiotemporal map reveals dominant expression patterns. Science 318, 801-806 doi: 10. 1126/science. 1146265
14. Brady, S. M., and Provart, N. J. (2009). Web-queryable large-scale data sets for hypothesis generation in plant biology. Plant Cell 21, 1034-1051. doi: 10. 1105/tpc. 109. 066050
15. Brazma, A. (2003). ArrayExpress-a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68-71. doi: 10. 1093/nar/gkg091
16. Breitling, R., Armengaud, P., Amtmann, A., and Herzyk, P. (2004). Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83-92. doi: 10. 1016/j. febslet. 2004. 07. 055
17. Brückner, A., Polge, C., Lentze, N., Auerbach, D., and Schlattner, U. (2009). Yeast two-hybrid, a powerful tool for systems biology. Int. J. Mol. Sci. 10, 2763-2788. doi: 10. 3390/ijms10062763
18. Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., et al. (2015). Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155-160. doi: 10. 1038/nbt. 3102
19. Butte, A. J., and Kohane, I. S. (2000). Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 5, 415-426.
20. Carlson, M. R., Zhang, B., Fang, Z., Mischel, P. S., Horvath, S., and Nelson, S. F. (2006). Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks. BMC Genomics 7: 40. doi: 10. 1186/1471-2164-7-40
21. Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C. A., et al. (2014). The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 42, D459-D471. doi: 10. 1093/nar/gkt1103
22. Chan, E. K., Rowe, H. C., Corwin, J. A., Joseph, B., and Kliebenstein, D. J. (2011). Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLoS Biol. 9: e1001125. doi: 10. 1371/journal. pbio. 1001125
23. Chen, X., Ernst, K., Soman, F., Borowczak, M., and Weirauch, M. T. (2015). CressInt: a user-friendly web resource for genome-scale exploration of gene regulation in Arabidopsis thaliana. Curr. Plant Biol. 3-4, 48-55. doi: 10. 1016/j. cpb. 2015. 09. 001
24. Childs, K. L., Davidson, R. M., and Buell, C. R. (2011). Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS ONE 6: e22196. doi: 10. 1371/journal. pone. 0022196
25. Conant, G. C., and Wolfe, K. H. (2006). Functional partitioning of yeast co-expression networks after genome duplication. PLoS Biol. 4: e109. doi: 10. 1371/journal. pbio. 0040109
26. Conesa, A., Götz, S., García-Gómez, J. M., Terol, J., Talón, M., and Robles, M. (2005). Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674-3676. doi: 10. 1093/bioinformatics/bti610
27. Costa, M. C., Righetti, K., Nijveen, H., Yazdanpanah, F., Ligterink, W., Buitink, J., et al. (2015). A gene co-expression network predicts functional genes controlling the re-establishment of desiccation tolerance in germinated Arabidopsis thaliana seeds. Planta 242, 435-449. doi: 10. 1007/s00425-015-2283-7
28. Cubillos, F. A., Coustham, V., and Loudet, O. (2012). Lessons from eQTL mapping studies: non-coding regions and their role behind natural phenotypic variation in plants. Curr. Opin. Plant Biol. 15, 192-198. doi: 10. 1016/j. pbi. 2012. 01. 005
29. Cubillos, F. A., Stegle, O., Grondin, C., Canut, M., Tisné, S., Gy, I., et al. (2014). Extensive cis-regulatory variation robust to environmental perturbation in Arabidopsis. Plant Cell 26, 4298-4310. doi: 10. 1105/tpc. 114. 130310
30. De Bodt, S., Carvajal, D., Hollunder, J., Van Den Cruyce, J., Movahedi, S., and Inzé, D. (2010). CORNET: a user-friendly tool for data mining and integration. Plant Physiol. 152, 1167-1179. doi: 10. 1104/pp. 109. 147215
31. De Smet, R., and Marchal, K. (2010). Advantages and limitations of current network inference methods. Nat. Rev. Microbiol. 8, 717-729. doi: 10. 1038/nrmicro2419
32. De Smet, R., and Van De Peer, Y. (2012). Redundancy and rewiring of genetic networks following genome-wide duplication events. Curr. Opin. Plant Biol. 15, 168-176. doi: 10. 1016/j. pbi. 2012. 01. 003
33. Dobzhansky, T. (1973). Nothing in biology makes sense except in the light of evolution. Am. Biol. Teach. 35, 125-129. doi: 10. 2307/4444260
34. Drost, D. R., Benedict, C. I., Berg, A., Novaes, E., Novaes, C. R., Yu, Q., et al. (2010). Diversification in the genetic architecture of gene expression and transcriptional networks in organ differentiation of Populus. Proc. Natl. Acad. Sci. U. S. A. 107, 8492-8497. doi: 10. 1073/pnas. 0914709107
35. Edgar, R., Domrachev, M., and Lash, A. E. (2001). Gene expression omnibus: NCBI gene hybridization array data repository. Nucleic Acids Res. 30, 207-210. doi: 10. 1093/nar/30. 1. 207
36. Efroni, I., Ip, P. L., Nawy, T., Mello, A., and Birnbaum, K. D. (2015). Quantification of cell identity from single-cell gene expression profiles. Genome Biol. 16, 9. doi: 10. 1186/s13059-015-0580-x
37. Emms, D. M., and Kelly, S. (2015). OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 16, 157. doi: 10. 1186/s13059-015-0721-2
38. Enright, A. J., Van Dongen, S., and Ouzounis, C. A. (2002). An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575-1584. doi: 10. 1093/nar/30. 7. 1575
39. Feltus, F. A., Ficklin, S. P., Gibson, S. M., and Smith, M. C. (2013). Maximising capture of gene co-expression network relationships through pre-clustering of input expression samples: an Arabidopsis case study. BMC Syst. Biol. 7: 44. doi: 10. 1186/1752-0509-7-44
40. Ficklin, S. P., and Feltus, F. A. (2011). Gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice. Plant Physiol. 156, 1244-1256. doi: 10. 1104/pp. 111. 173047
41. Ficklin, S. P., Luo, F., and Feltus, F. A. (2010). The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks. Plant Physiol. 154, 13-24. doi: 10. 1104/pp. 110. 159459
42. Flassig, R. J., Heise, S., Sundmacher, K., and Klamt, S. (2013). An effective framework for reconstructing gene regulatory networks from genetical genomics data. Bioinformatics 29, 246-254. doi: 10. 1093/bioinformatics/bts679
43. Fu, J., Keurentjes, J. J., Bouwmeester, H., America, T., Verstappen, F. W., Ward, J. L., et al. (2009). System-wide molecular evidence for phenotypic buffering in Arabidopsis. Nat. Genet. 41, 166-167. doi: 10. 1038/ng. 308
44. Fukushima, A., Nishizawa, T., Hayakumo, M., Hikosaka, S., Saito, K., Goto, E., et al. (2012). Exploring tomato gene functions based on coexpression modules using graph clustering and differential coexpression approaches. Plant Physiol. 158, 1487-1502. doi: 10. 1104/pp. 111. 188367
45. Gansner, E. R., and North, S. C. (2000). An open graph visualization system and its applications to software engineering. Softw. Pract. Exp. 30, 1203-1233. doi: 10. 1002/1097-024X(200009)
46. Gaudinier, A., Tang, M., and Kliebenstein, D. J. (2015). Transcriptional networks governing plant metabolism. Curr. Plant Biol. 3-4, 56-64. doi: 10. 1016/j. cpb. 2015. 07. 002
47. Gifford, M. L., Dean, A., Gutierrez, R. A., Coruzzi, G. M., and Birnbaum, K. D. (2008). Cell-specific nitrogen responses mediate developmental plasticity. Proc. Natl. Acad. Sci. U. S. A. 105, 803-808. doi: 10. 1073/pnas. 0709559105
48. Goodstein, D. M., Shu, S., Howson, R., Neupane, R., Hayes, R. D., Fazo, J., et al. (2012). Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 40, D1178-D1186. doi: 10. 1093/nar/gkr944
49. Hakala, K., Van Landeghem, S., Salakoski, T., Van De Peer, Y., and Ginter, F. (2015). Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis. BMC Bioinformatics 16(suppl. 16): S3. doi: 10. 1186/1471-2105-16-S16-S3
50. Hamada, K., Hongo, K., Suwabe, K., Shimizu, A., Nagayama, T., Abe, R., et al. (2011). OryzaExpress: an integrated database of gene expression networks and omics annotations in rice. Plant Cell Physiol. 52, 220-229. doi: 10. 1093/pcp/pcq195
51. Hansen, B. G., Halkier, B. A., and Kliebenstein, D. J. (2008). Identifying the molecular basis of QTLs: eQTLs add a new dimension. Trends Plant Sci. 13, 72-77. doi: 10. 1016/j. tplants. 2007. 11. 008
52. Hansen, B. O., Vaid, N., Musialak-Lange, M., Janowski, M., and Mutwil, M. (2014). Elucidating gene function and function evolution through comparison of co-expression networks of plants. Front. Plant Sci. 5: 394. doi: 10. 3389/fpls. 2014. 00394
53. Higo, K., Ugawa, Y., Iwamoto, M., and Koregana, T. (1999). Plant cis-acting regulatory DNA elements (PLACE) database: 1999. Nucleic Acids Res. 27, 297-300. doi: 10. 1093/nar/27. 1. 297
54. Hruz, T., Laule, O., Szabo, G., Wessendorp, F., Bleuler, S., Oertle, L., et al. (2008). Genevestigator V3: a reference expression database for the meta-analysis of transcriptomes. Adv. Bioinformatics 2008, 1-5. doi: 10. 1155/2008/420747
55. Iancu, O. D., Kawane, S., Bottomly, D., Searles, R., Hitzemann, R., and McWeeney, S. (2012). Utilizing RNA-Seq data for de novo coexpression network inference. Bioinformatics 28, 1592-1597. doi: 10. 1093/bioinformatics/bts245
56. Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., and Miyano, S. (2004). Combining microarrays and biological knowledge for estimating gene networks via bayesian networks. J. Bioinform. Comput. Biol. 2, 77-98. doi: 10. 1142/S021972000400048X
57. Itkin, M., Heinig, U., Tzfadia, O., Bhide, A. J., Shinde, B., Carnedas, P. D., et al. (2013). Biosynthesis of Antinutritional alkaloids in Solanaceaous crops is mediated by clustered genes. Science 341, 175-179. doi: 10. 1126/science. 1240230
58. Jansen, R. C., and Nap, J.-P. (2001). Genetical genomics: the added value from segregation. Trends Genet. 11, 388-391. doi: 10. 1016/S0168-9525(01)02310-1
59. Jeong, H., Mason, S. P., Barabasi, A. L., and Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature 441, 41-42. doi: 10. 1038/35075138
60. Jiménez-Gómez, J. M. (2014). Network types and their application in natural variation studies in plants. Curr. Opin. Plant Biol. 18, 80-86. doi: 10. 1016/j. pbi. 2014. 02. 010
61. Jimenez-Gomez, J. M., Wallace, A. D., and Maloof, J. N. (2010). Network analysis identifies ELF3 as a QTL for the shade avoidance response in Arabidopsis. PLoS Genet. 6: e1001100. doi: 10. 1371/journal. pgen. 1001100
62. Jin, J., Zhang, H., Kong, L., Gao, G., and Luo, J. (2014). PlantTFDB 3. 0: a portal for the functional and evolutionary study of plant transcription factors. Nucleic Acids Res. 42, D1182-D1187. doi: 10. 1093/nar/gkt1016
63. Joosen, R. V., Ligterink, W., Hilhorst, H. W., and Keurentjes, J. J. (2009). Advances in genetical genomics of plants. Curr. Genomics 10, 540-549. doi: 10. 2174/138920209789503914
64. Kanehisa, M., and Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30. doi: 10. 1093/nar/28. 1. 27
65. Kerwin, R. E., Jimenez-Gomez, J. M., Fulop, D., Harmer, S. L., Maloof, J. N., and Kliebenstein, D. J. (2011). Network quantitative trait loci mapping of circadian clock outputs identifies metabolic pathway-to-clock linkages in Arabidopsis. Plant Cell 23, 471-485. doi: 10. 1105/tpc. 110. 082065
66. Keurentjes, J. J., Fu, J., Terpstra, I. R., Garcia, J. M., Van Den Ackerveken, G., Snoek, L. B., et al. (2007). Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci. Proc. Natl. Acad. Sci. U. S. A. 104, 1708-1713. doi: 10. 1073/pnas. 0610429104
67. Khanin, R., and Wit, E. (2006). How scale-free networks are biological networks. J. Comput. Biol. 13, 810-818. doi: 10. 1089/cmb. 2006. 13. 810
68. Kliebenstein, D. (2009). Quantitative genomics: analyzing intraspecific variation using global gene expression polymorphisms or eQTLs. Annu. Rev. Plant Biol. 60, 93-114. doi: 10. 1146/annurev. arplant. 043008. 092114
69. Kliebenstein, D. J. (2012). Exploring the shallow end; estimating information content in transcriptomics studies. Front. Plant Sci. 3: 213. doi: 10. 3389/fpls. 2012. 00213
70. Kliebenstein, D. J., West, M. A., Van Leeuwen, H., Loudet, O., Doerge, R. W., and St Clair, D. A. (2006). Identification of QTLs controlling gene expression networks defined a priori. BMC Bioinformatics 7: 308. doi: 10. 1186/1471-2105-7-308
71. Kloosterman, B., Anithakumari, A. M., Chibon, P. Y., Oortwijn, M., Van Der Linden, G. C., Visser, R. G., et al. (2012). Organ specificity and transcriptional control of metabolic routes revealed by expression QTL profiling of source-sink tissues in a segregating potato population. BMC Plant Biol. 12: 17. doi: 10. 1186/1471-2229-12-17
72. Kourmpetis, Y. A., Van Dijk, A. D., Van Ham, R. C., and Ter Braak, C. J. (2011). Genome-wide computational function prediction of Arabidopsis proteins by integration of multiple data sources. Plant Physiol. 155, 271-281. doi: 10. 1104/pp. 110. 162164
73. Krouk, G., Mirowski, P., Lecun, Y., Shasha, D. E., and Coruzzi, G. M. (2010). Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate. Genome Biol. 11: R123. doi: 10. 1186/gb-2010-11-12-r123
74. Kuzniar, A., Van Ham, R. C., Pongor, S., and Leunissen, J. A. (2008). The quest for orthologs: finding the corresponding gene across genomes. Trends Genet. 24, 539-551. doi: 10. 1016/j. tig. 2008. 08. 009
75. Lachowiec, J., Queitsch, C., and Kliebenstein, D. J. (2015). Molecular mechanisms governing differential robustness of development and environmental responses in plants. Ann. Bot. doi: 10. 1093/aob/mcv151. [Epub ahead of print].
76. Lamesch, P., Berardini, T. Z., Li, D., Swarbreck, D., Wilks, C., Sasidharan, R., et al. (2012). The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 40, D1202-D1210. doi: 10. 1093/nar/gkr1090
77. Langfelder, P., and Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9: 559. doi: 10. 1186/1471-2105-9-559
78. Lavenus, J., Goh, T., Guyomarc'h, S., Hill, K., Lucas, M., Voss, U., et al. (2015). Inference of the Arabidopsis lateral root gene regulatory network suggests a bifurcation mechanism that defines primordia flanking and central zones. Plant Cell 27, 1368-1388. doi: 10. 1105/tpc. 114. 132993
79. Le, B. H., Cheng, C., Bui, A. Q., Wagmaister, J. A., Henry, K. F., Pelletier, J., et al. (2010). Global analysis of gene activity during Arabidopsis seed development and identification of seed-specific transcription factors. Proc. Natl. Acad. Sci. U. S. A. 107, 8063-8070. doi: 10. 1073/pnas. 1003530107
80. Leal, L. G., López, C., and López-Kleine, L. (2014). Construction and comparison of gene co-expression networks shows complex plant immune responses. PeerJ 2: e610. doi: 10. 7717/peerj. 610
81. Lee, H. K., Hsu, A. K., Sajdak, J., Qin, J., and Pavlidis, P. (2004). Coexpression analysis of human genes across many microarray data sets. Genome Res. 14, 1085-1094. doi: 10. 1101/gr. 1910904
82. Lee, I., Ambaru, B., Thakkar, P., Marcotte, E. M., and Rhee, S. Y. (2010). Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana. Nat. Biotechnol. 28, 149-156. doi: 10. 1038/nbt. 1603
83. Lee, I., Seo, Y.-S., Coltrane, D., Hwang, S., Oh, T., Marcotte, E. M., et al. (2011). Genetic dissection of the biotic stress response using a genome-scale gene network for rice. Proc. Natl. Acad. Sci. U. S. A. 108, 18548-18553. doi: 10. 1073/pnas. 1110384108
84. Lee, T., Oh, T., Yang, S., Shin, J., Hwang, S., Kim, C. Y., et al. (2015a). RiceNet v2: an improved network prioritization server for rice genes. Nucleic Acids Res. 43, W122-W127. doi: 10. 1093/nar/gkv253
85. Lee, T., Yang, S., Kim, E., Ko, Y., Hwang, S., Shin, J., et al. (2015b). 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-D1002. doi: 10. 1093/nar/gku1053
86. Levy, S. F., and Siegal, M. L. (2008). Network hubs buffer environmental variation in Saccharomyces cerevisiae. PLoS Biol. 6: e264. doi: 10. 1371/journal. pbio. 0060264
87. Li, L., Stoeckert, C. J., and Roos, D. S. (2003). OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 13, 2178-2189. doi: 10. 1101/gr. 1224503
88. Li, G., Ma, Q., Tang, H., Paterson, A. H., and Xu, Y. (2009). QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic Acids Res. 37: e101-e101. doi: 10. 1093/nar/gkp491
89. Li, M., Chen, J. E., Wang, J. X., Hu, B., and Chen, G. (2008). Modifying the DPClus algorithm for identifying protein complexes based on new topological structures. BMC Bioinformatics 9: 398. doi: 10. 1186/1471-2105-9-398
90. Li, Y., Breitling, R., and Jansen, R. C. (2008). Generalizing genetical genomics: getting added value from environmental perturbation. Trends Genet. 24, 518-524. doi: 10. 1016/j. tig. 2008. 08. 001
91. Li, Y., Pearl, S. A., and Jackson, S. A. (2015). Gene networks in plant biology: approaches in reconstruction and analysis. Trends Plant Sci. 20, 664-675. doi: 10. 1016/j. tplants. 2015. 06. 013
92. Ligterink, W., Joosen, R. V. L., and Hilhorst, H. W. M. (2012). Unravelling the complex trait of seed quality: using natural variation through a combination of physiology, genetics and-omics technologies. Seed Sci. Res. 22, S45-S52. doi: 10. 1017/S0960258511000328
93. Lima-Mendez, G., and Van Helden, J. (2009). The powerful law of the power law and other myths in network biology. Mol. Biosyst. 5, 1482-1493. doi: 10. 1039/b908681a
94. Liseron-Monfils, C., and Ware, D. (2015). Revealing gene regulation and associations through biological networks. Curr. Plant Biol. 3-4, 30-39. doi: 10. 1016/j. cpb. 2015. 11. 001
95. Lysenko, A., Defoin-Platel, M., Hassani-Pak, K., Taubert, J., Hodgman, C., Rawlings, C. J., et al. (2011). Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis. BMC Bioinformatics 12: 203. doi: 10. 1186/1471-2105-12-203
96. Ma, S., Shah, S., Bohnert, H. J., Snyder, M., and Dinesh-Kumar, S. P. (2013). Incorporating motif analysis into gene co-expression network reveals novel modular expression pattern and new signaling pathways. PLoS Genet 9: e1003840. doi: 10. 1371/journal. pgen. 1003840
97. Maere, S., Heymans, K., and Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448-3449. doi: 10. 1093/bioinformatics/bti551
98. Mao, L., Van Hemert, J. L., Dash, S., and Dickerson, J. A. (2009). Arabidopsis gene co-expression network and its functional modules. BMC Bioinformatics 10: 346. doi: 10. 1186/1471-2105-10-346
99. Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., et al. (2012). Wisdom of crowds for robust gene network inference. Nat. Meth. 9, 796-804. doi: 10. 1038/nmeth. 2016
100. Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R. D., et al. (2006). ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7: S7. doi: 10. 1186/1471-2105-7-S1-S7
101. Meyer, P. E., Lafitte, F., and Bontempi, G. (2008). minet: AR/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics 9: 461. doi: 10. 1186/1471-2105-9-461
102. Mochida, K., and 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
103. Morris, J. H., Knudsen, G. M., Verschueren, E., Johnson, J. R., Cimermancic, P., Greninger, A. L., et al. (2014). Affinity purification-mass spectrometry and network analysis to understand protein-protein interactions. Nat. Protoc. 9, 2539-2554. doi: 10. 1038/nprot. 2014. 164
104. Movahedi, S., Van Bel, M., Heyndrickx, K. S., and Vandepoele, K. (2012). Comparative co-expression analysis in plant biology. Plant Cell Environ. 35, 1787-1798. doi: 10. 1111/j. 1365-3040. 2012. 02517. x
105. Movahedi, S., Van De Peer, Y., and Vandepoele, K. (2011). Comparative network analysis reveals that tissue specificity and gene function are important factors influencing the mode of expression evolution in Arabidopsis and rice. Plant Physiol. 156, 1316-1330. doi: 10. 1104/pp. 111. 177865
106. Mutwil, M., Klie, S., Tohge, T., Giorgi, F. M., Wilkins, O., Campbell, M. M., et al. (2011). PlaNet: combined sequence and expression comparisons across plant networks derived from seven species. Plant Cell 23, 895-910. doi: 10. 1105/tpc. 111. 083667
107. Mutwil, M., Usadel, B., Schütte, M., Loraine, A., Ebenhöh, O., and Persson, S. (2010). Assembly of an interactive correlation network for the Arabidopsis genome using a novel heuristic clustering algorithm. Plant Physiol. 152, 29-43. doi: 10. 1104/pp. 109. 145318
108. Nelson, T., Gandotra, N., and Tausta, S. L. (2008). Plant cell types: reporting and sampling with new technologies. Curr. Opin. Plant Biol. 11, 567-573. doi: 10. 1016/j. pbi. 2008. 06. 006
109. Netotea, S., Sundell, D., Street, N. R., and Hvidsten, T. R. (2014). ComPlEx: conservation and divergence of co-expression networks in A. thaliana, Populus and O. sativa. BMC Genomics 15: 106. doi: 10. 1186/1471-2164-15-106
110. Obayashi, T., Kinoshita, K., Nakai, K., Shibaoka, M., Hayashi, S., Saeki, M., et al. (2007). ATTED-II: a database of co-expressed genes and cis elements for identifying co-regulated gene groups in Arabidopsis. Nucleic Acids Res. 35, D863-D869. doi: 10. 1093/nar/gkl783
111. Obayashi, T., Okamura, Y., Ito, S., Tadaka, S., Aoki, Y., Shirota, M., et al. (2014). ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants. Plant Cell Physiol. 55, e6. doi: 10. 1093/pcp/pct178
112. Obertello, M., Shrivastava, S., Katari, M. S., and Coruzzi, G. M. (2015). Cross-species network analysis uncovers conserved nitrogen-regulated network modules in rice. Plant Physiol. 168, 1830-1843. doi: 10. 1104/pp. 114. 255877
113. Opgen-Rhein, R., and Strimmer, K. (2007). From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst. Biol. 1: 37. doi: 10. 1186/1752-0509-1-37
114. Palaniswamy, S. K., James, S., Sun, H., Lamb, R. S., Davuluri, R. V., and Grotewold, E. (2006). AGRIS and AtRegNet. A platform to link cis-regulatory elements and transcription factors into regulatory networks. Plant Physiol 140, 818-829. doi: 10. 1104/pp. 105. 072280
115. Parkinson, H. (2004). ArrayExpress-a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 33, D553-D555. doi: 10. 1093/nar/gki056
116. Pavlopoulos, G. A., Secrier, M., Moschopoulos, C. N., Soldatos, T. G., Kossida, S., Aerts, J., et al. (2011). Using graph theory to analyze biological networks. BioData Min. 4: 10. doi: 10. 1186/1756-0381-4-10
117. Petretto, E., Mangion, J., Dickens, N. J., Cook, S. A., Kumaran, M. K., Lu, H., et al. (2006). Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet. 2: e172. doi: 10. 1371/journal. pgen. 0020172
118. Polanski, K., Rhodes, J., Hill, C., Zhang, P., Jenkins, D. J., Kiddle, S. J., et al. (2014). Wigwams: identifying gene modules co-regulated across multiple biological conditions. Bioinformatics 30, 962-970. doi: 10. 1093/bioinformatics/btt728
119. Proost, S., Van Bel, M., Vaneechoutte, D., Van De Peer, Y., Inzé, D., Mueller-Roeber, B., et al. (2015). PLAZA 3. 0: an access point for plant comparative genomics. Nucleic Acids Res. 43, D974-D981. doi: 10. 1093/nar/gku986
120. Provero, P. (2002). Gene networks from DNA microarray data: centrality and lethality. arXiv preprint cond-mat/0207345.
121. Quevillon, E., Silventoinen, V., Pillai, S., Harte, N., Mulder, N., Apweiler, R., et al. (2005). InterProScan: protein domains identifier. Nucleic Acids Res. 33, W116-W120. doi: 10. 1093/nar/gki442
122. Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., et al. (2013). A large-scale evaluation of computational protein function prediction. Nat. Methods 10, 221-227. doi: 10. 1038/nmeth. 2340
123. Ransbotyn, V., Yeger-Lotem, E., Basha, O., Acuna, T., Verduyn, C., Gordon, M., et al. (2014). A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes. Plant Biotechnol. J. 13, 501-513. doi: 10. 1111/pbi. 12274
124. Rhee, S. Y., and Mutwil, M. (2014). Towards revealing the functions of all genes in plants. Trends Plant Sci. 19, 212-221. doi: 10. 1016/j. tplants. 2013. 10. 006
125. Righetti, K., Vu, J. L., Pelletier, S., Vu, B. L., Glaab, E., Lalanne, D., et al. (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, 2692-2708. doi: 10. 1105/tpc. 15. 00632
126. Rivera, C. G., Vakil, R., and Bader, J. S. (2010). NeMo: network Module identification in Cytoscape. BMC Bioinformatics 11(suppl. 1): S61. doi: 10. 1186/1471-2105-11-S1-S61
127. Rockman, M. V., and Kruglyak, L. (2006). Genetics of global gene expression. Nat. Rev. Genet. 7, 862-872. doi: 10. 1038/nrg1964
128. Rung, J., and Brazma, A. (2013). Reuse of public genome-wide gene expression data. Nat. Rev. Genet. 14, 89-99. doi: 10. 1038/nrg3394
129. Saito, K., Hirai, M. Y., and Yonekura-Sakakibara, K. (2008). Decoding genes with coexpression networks and metabolomics-'majority report by precogs'. Trends Plant Sci. 13, 36-43. doi: 10. 1016/j. tplants. 2007. 10. 006
130. Saito, R., Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L., Lotia, S., et al. (2012). A travel guide to Cytoscape plugins. Nat. Methods 9, 1069-1076. doi: 10. 1038/nmeth. 2212
131. Sato, Y., Namiki, N., Takehisa, H., Kamatsuki, K., Minami, H., Ikawa, H., et al. (2013). RiceFREND: a platform for retrieving coexpressed gene networks in rice. Nucleic Acids Res. 41, D1214-D1221. doi: 10. 1093/nar/gks1122
132. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504. doi: 10. 1101/gr. 1239303
133. Silva, A. T., Ribone, P. A., Chan, R. L., Ligterink, W., and Hilhorst, H. W. (2016). A predictive co-expression network identifies novel genes controlling the seed-to-seedling phase transition in Arabidopsis thaliana. Plant Physiol. 170, 2218-2231. doi: 10. 1104/pp. 15. 01704
134. Slane, D., Kong, J., Berendzen, K. W., Kilian, J., Henschen, A., Kolb, M., et al. (2014). Cell type-specific transcriptome analysis in the early Arabidopsis thaliana embryo. Development 141, 4831-4840. doi: 10. 1242/dev. 116459
135. Song, L., Langfelder, P., and Horvath, S. (2012). Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics 13: 328. doi: 10. 1186/1471-2105-13-328
136. Srinivasasainagendra, V., Page, G. P., Mehta, T., Coulibaly, I., and Loraine, A. E. (2008). CressExpress: a tool for large-scale mining of expression data from Arabidopsis. Plant Physiol. 147, 1004-1016. doi: 10. 1104/pp. 107. 115535
137. Stuart, J. M., Segal, E., Koller, D., and Kim, S. K. (2003). A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249-255. doi: 10. 1126/science. 1087447
138. Szakonyi, D., Van Landeghem, S., Baerenfaller, K., Baeyens, L., Blomme, J., Casanova-Sáez, R., et al. (2015). The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining. Curr. Plant Biol. 2, 1-11. doi: 10. 1016/j. cpb. 2014. 12. 002
139. 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-D452. doi: 10. 1093/nar/gku1003
140. Tamada, Y., Kim, S., Bannai, H., Imoto, S., Tashiro, K., Kuhara, S., et al. (2003). Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics 19, ii227-ii236. doi: 10. 1093/bioinformatics/btg1082
141. Tang, F., Lao, K., and Surani, M. A. (2011). Development and applications of single-cell transcriptome analysis. Nat. Methods 8, S6-S11. doi: 10. 1038/nmeth. 1557
142. Terpstra, I. R., Snoek, L. B., Keurentjes, J. J., Peeters, A. J., and Van Den Ackerveken, G. (2010). Regulatory network identification by genetical genomics: signaling downstream of the Arabidopsis receptor-like kinase ERECTA. Plant Physiol. 154, 1067-1078. doi: 10. 1104/pp. 110. 159996
143. Thimm, O., Bläsing, O., Gibon, Y., Nagel, A., Meyer, S., Kruger, P., et al. (2004). MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 37, 914-939. doi: 10. 1111/j. 1365-313X. 2004. 02016. x
144. Tzfadia, O., Amar, D., Bradbury, L. M., Wurtzel, E. T., and Shamir, R. (2012). The MORPH algorithm: ranking candidate genes for membership in Arabidopsis and tomato pathways. Plant Cell 24, 4389-4406. doi: 10. 1105/tpc. 112. 104513
145. Tzfadia, O., Diels, T., De Meyer, S., Vandepoele, K., Aharoni, A., and Van De Peer, Y. (2015). CoExpNetViz: comparative co-expression networks construction and visualization tool. Front. Plant Sci. 6: 1194. doi: 10. 3389/fpls. 2015. 01194
146. Ulitsky, I., and Shamir, R. (2009). Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics 25, 1158-1164. doi: 10. 1093/bioinformatics/btp118
147. Usadel, B., Obayashi, T., Mutwil, M., Giorgi, F. M., Bassel, G. W., Tanimoto, M., et al. (2009). Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ. 32, 1633-1651. doi: 10. 1111/j. 1365-3040. 2009. 02040. x
148. Vandepoele, K., Quimbaya, M., Casneuf, T., De Veylder, L., and Van De Peer, Y. (2009). Unraveling transcriptional control in Arabidopsis using cis-regulatory elements and coexpression networks. Plant Physiol. 150, 535-546. doi: 10. 1104/pp. 109. 136028
149. Vashishtha, S., Broderick, G., Craddock, T. J., Fletcher, M. A., and Klimas, N. G. (2015). Inferring broad regulatory biology from time course data: have we reached an upper bound under constraints typical of in vivo studies? PLoS ONE 10: e0127364. doi: 10. 1371/journal. pone. 0127364
150. Vignes, M., Vandel, J., Allouche, D., Ramadan-Alban, N., Cierco-Ayrolles, C., Schiex, T., et al. (2011). Gene regulatory network reconstruction using bayesian networks, the dantzig selector, the lasso and their meta-analysis. PLoS ONE 6: e29165. doi: 10. 1371/journal. pone. 0029165
151. Wang, J., Yu, H., Weng, X., Xie, W., Xu, C., Li, X., et al. (2014). An expression quantitative trait loci-guided co-expression analysis for constructing regulatory network using a rice recombinant inbred line population. J. Exp. Bot. 65, 1069-1079. doi: 10. 1093/jxb/ert464
152. Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., et al. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38, W214-W220. doi: 10. 1093/nar/gkq537
153. Wei, C. H., Harris, B. R., Li, D., Berardini, T. Z., Huala, E., Kao, H. Y., et al. (2012). Accelerating literature curation with text-mining tools: a case study of using PubTator to curate genes in PubMed abstracts. Database 2012: bas041. doi: 10. 1093/database/bas041
154. Wei, H., Yordanov, Y. S., Georgieva, T., Li, X., and Busov, V. (2013). Nitrogen deprivation promotes Populus root growth through global transcriptome reprogramming and activation of hierarchical genetic networks. New Phytol. 200, 483-497. doi: 10. 1111/nph. 12375
155. Wentzell, A. M., Rowe, H. C., Hansen, B. G., Ticconi, C., Halkier, B. A., and Kliebenstein, D. J. (2007). Linking metabolic QTLs with network and cis-eQTLs controlling biosynthetic pathways. PLoS Genet. 3, 1687-1701. doi: 10. 1371/journal. pgen. 0030162
156. Werhli, A. V., and Husmeier, D. (2008). Gene regulatory network reconstruction by bayesian integration of prior knowledge and/or different experimental conditions. J. Bioinform. Comput. Biol. 6, 543-572. doi: 10. 1142/S0219720008003539
157. West, M. A., Kim, K., Kliebenstein, D. J., Van Leeuwen, H., Michelmore, R. W., Doerge, R. W., et al. (2007). Global eQTL mapping reveals the complex genetic architecture of transcript-level variation in Arabidopsis. Genetics 175, 1441-1450. doi: 10. 1534/genetics. 106. 064972
158. Windram, O., Madhou, P., McHattie, S., Hill, C., Hickman, R., Cooke, E., et al. (2012). Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis. Plant Cell 24, 3530-3557. doi: 10. 1105/tpc. 112. 102046
159. Winter, D., Vinegar, B., Nahal, H., Ammar, R., Wilson, G. V., and Provart, N. J. (2007). An "Electronic Fluorescent Pictograph" browser for exploring and analyzing large-scale biological data sets. PLoS ONE 2: e718. doi: 10. 1371/journal. pone. 0000718
160. Wolfe, C. J., Kohane, I. S., and Butte, A. J. (2005). Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks. BMC Bioinformatics 6: 227. doi: 10. 1186/1471-2105-6-227
161. Yang, X., Ye, C. Y., Bisaria, A., Tuskan, G. A., and Kalluri, U. C. (2011). Identification of candidate genes in Arabidopsis and Populus cell wall biosynthesis using text-mining, co-expression network analysis and comparative genomics. Plant Sci. 181, 675-687. doi: 10. 1016/j. plantsci. 2011. 01. 020
162. Yim, W. C., Yu, Y., Song, K., Jang, C. S., and Lee, B.-M. (2012). PLANEX: the plant co-expression database. BMC Plant Biol. 13: 83. doi: 10. 1186/1471-2229-13-83
163. Zarrineh, P., Fierro, A. C., Sánchez-Rodríguez, A., De Moor, B., Engelen, K., and Marchal, K. (2011). COMODO: an adaptive coclustering strategy to identify conserved coexpression modules between organisms. Nucleic Acids Res. 39, e41. doi: 10. 1093/nar/gkq1275
164. Zhan, J., Thakare, D., Ma, C., Lloyd, A., Nixon, N. M., Arakaki, A. M., et al. (2015). RNA sequencing of laser-capture microdissected compartments of the maize kernel identifies regulatory modules associated with endosperm cell differentiation. Plant Cell 27, 513-531. doi: 10. 1105/tpc. 114. 135657
165. Zhang, B., and Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17. doi: 10. 2202/1544-6115. 1128
166. Zhang, Y., Zha, H., and Chu, C. H. (2005). "A time-series biclustering algorithm for revealing co-regulated genes, " in Information Technology: Coding and Computing, 2005. itcc 2005. International Conference on Vol. 1, (IEEE), 32-37. doi: 10. 1109/ITCC. 2005. 46