How to predict molecular interactions between species?

Frontiers in Microbiology

Volumn 7, Issue MAR, 2016, Pages

  Schulze, Sylvie ^a   Schleicher, Jana ^a   Guthke, Reinhard ^a   Linde, Jörg ^a  

^a HANS KNÖLL INSTITUTE   (Germany)

Author keywords

Dual RNA seq; Dual transcriptomics; Gene regulatory network; Host pathogen interaction; Molecular inter species interaction; Network inference

Indexed keywords

BAYES THEOREM; GENE EXPRESSION; GENE INTERACTION; GENE REGULATORY NETWORK; HOST PATHOGEN INTERACTION; HUMAN; LINEAR REGRESSION ANALYSIS; MICROARRAY ANALYSIS; MOLECULAR INTERACTION; NEXT GENERATION SEQUENCING; NONHUMAN; PROTEIN EXPRESSION; REVIEW; RNA INTERFERENCE; RNA SEQUENCE; SEQUENCE ANALYSIS; SYSTEMS BIOLOGY; TRANSCRIPTOMICS;

EID: 84964374779     PISSN: None     EISSN: 1664302X     Source Type: Journal    
DOI: 10.3389/fmicb.2016.00442     Document Type: Review

References (102)

1. Altay, G., and Emmert-Streib, F. (2010). Inferring the conservative causal core of gene regulatory networks. BMC Syst. Biol. 4:132. doi: 10.1186/1752-0509-4-132
2. Altwasser, R., Linde, J., Buyko, E., Hahn, U., and Guthke, R. (2012). Genome-wide scale-free network inference for Candida albicans. Front. Microbiol. 3:51. doi: 10.3389/fmicb.2012.00051
3. Andrews, S. (2010). Fastqc: A Quality Control Tool for High Throughput Sequence Data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc.
4. Asai, S., Rallapalli, G., Piquerez, S. J. M., Caillaud, M.-C., Furzer, O. J., Ishaque, N., et al. (2014). Expression profiling during arabidopsis/downy mildew interaction reveals a highly-expressed effector that attenuates responses to salicylic acid. PLoS Pathog. 10:e1004443. doi: 10.1371/journal.ppat.1004443
5. 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
6. Barker, K. S., Liu, T., and Rogers, P. D. (2005). Coculture of THP-1 human mononuclear cells with Candida albicans results in pronounced changes in host gene expression. J. Infect. Dis. 192, 901-912. doi: 10.1086/432487
7. Basso, K., Margolin, A. A., Stolovitzky, G., Klein, U., Dalla-Favera, R., and Califano, A. (2005). Reverse engineering of regulatory networks in human B cells. Nat. Genet. 37, 382-390. doi: 10.1038/ng1532
8. Battle, A., Mostafavi, S., Zhu, X., Potash, J. B., Weissman, M. M., McCormick, C., et al. (2014). Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 24, 14-24. doi: 10.1101/gr.155192.113
9. Begon, M., Townsend, C. R., and Harper, J. L. (2006). Ecology: From individuals to Ecosystems, 4th Edn. Hoboken, NJ: Wiley-Blackwell.
10. Bikel, S., Valdez-Lara, A., Cornejo-Granados, F., Rico, K., Canizales-Quinteros, S., Soberón, X., et al. (2015). Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome. Comput. Struct. Biotechnol. J. 13, 390-401. doi: 10.1016/j.csbj.2015.06.001
11. Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 30, 2114-2120. doi: 10.1093/bioinformatics/btu170
12. Brogaard, L., Klitgaard, K., Heegaard, P. M. H., Hansen, M. S., Jensen, T. K., and Skovgaard, K. (2015). Concurrent host-pathogen gene expression in the lungs of pigs challenged with Actinobacillus pleuropneumoniae. BMC Genomics 16:417. doi: 10.1186/s12864-015-1557-6
13. Brundrett, M. C. (2009). Mycorrhizal associations and other means of nutrition of vascular plants: understanding the global diversity of host plants by resolving conflicting information and developing reliable means of diagnosis. Plant Soil 320, 37-77. doi: 10.1007/s11104-008-9877-9
14. Bruno, V. M., Shetty, A. C., Yano, J., Fidel, P. L., Noverr, M. C., and Peters, B. M. (2015). Transcriptomic analysis of vulvovaginal candidiasis identifies a role for the NLRP3 inflammasome. mBio 6:e00182-15. doi: 10.1128/mBio.00182-15
15. Bruno, V. M., Wang, Z., Marjani, S. L., Euskirchen, G. M., Martin, J., Sherlock, G., et al. (2010). Comprehensive annotation of the transcriptome of the human fungal pathogen Candida albicans using RNA-seq. Genome Res. 20, 1451-1458. doi: 10.1101/gr.109553.110
16. Butte, A. J., and Kohane, I. S. (2000). Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 5, 418-429.
17. Cheng, S. C., Joosten, L. A. B., Kullberg, B. J., and Netea, M. G. (2012). Interplay between Candida albicans and the mammalian innate host defense. Infect. Immun. 80, 1304-1313. doi: 10.1128/IAI.06146-11
18. Choi, Y.-J., Aliota, M. T., Mayhew, G. F., Erickson, S. M., and Christensen, B. M. (2014). Dual RNA-seq of parasite and host reveals gene expression dynamics during filarial worm-mosquito interactions. PLoS Negl. Trop. Dis. 8:e2905. doi: 10.1371/journal.pntd.0002905
19. Du, P., Kibbe, W. A., and Lin, S. M. (2008). lumi: a pipeline for processing illumina microarray. Bioinformatics 24, 1547-1548. doi: 10.1093/bioinformatics/btn224
20. Durmus, S., Çakir, T., Özgür, A., and Guthke, R. (2015). A review on computational systems biology of pathogen-host interactions. Front. Microbiol. 6:235. doi: 10.3389/fmicb.2015.00235
21. Eaton, C. J., Cox, M. P., Ambrose, B., Becker, M., Hesse, U., Schardl, C. L., et al. (2010). Disruption of signaling in a fungal-grass symbiosis leads to pathogenesis. Plant Physiol. 153, 1780-1794. doi: 10.1104/pp.110.158451
22. Eaton, C. J., Cox, M. P., and Scott, B. (2011). What triggers grass endophytes to switch from mutualism to pathogenism? Plant Sci. 180, 190-195. doi: 10.1016/j.plantsci.2010.10.002
23. Emmert-Streib, F., Dehmer, M., and Haibe-Kains, B. (2014). Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks. Front. Cell Dev. Biol. 2:38. doi: 10.3389/fcell.2014.00038
24. Engström, P. G., Steijger, T., Sipos, B., Grant, G. R., Kahles, A., Alioto, T., et al. (2013). Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185-1191. doi: 10.1038/nmeth.2722
25. Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., et al. (2007). Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5:e8. doi: 10.1371/journal.pbio.0050008
26. Fernández-Arenas, E., Cabezón, V., Bermejo, C., Arroyo, J., Nombela, C., Diez-Orejas, R., et al. (2007). Integrated proteomics and genomics strategies bring new insight into Candida albicans response upon macrophage interaction. Mol. Cell Proteomics 6, 460-478. doi: 10.1074/mcp.M600210-MCP200
27. Finotello, F., Lavezzo, E., Bianco, L., Barzon, L., Mazzon, P., Fontana, P., et al. (2014). Reducing bias in RNA sequencing data: a novel approach to compute counts. BMC Bioinformatics 15(Suppl. 1):S7. doi: 10.1186/1471-2105-15-S1-S7
28. Foth, B. J., Tsai, I. J., Reid, A. J., Bancroft, A. J., Nichol, S., Tracey, A., et al. (2014). Whipworm genome and dual-species transcriptome analyses provide molecular insights into an intimate host-parasite interaction. Nat. Genet. 46, 693-700. doi: 10.1038/ng.3010
29. Fradin, C., Kretschmar, M., Nichterlein, T., Gaillardin, C., D'Enfert, C., and Hube, B. (2003). Stage-specific gene expression of Candida albicans in human blood. Mol. Microbiol. 47, 1523-1543. doi: 10.1046/j.1365-2958.2003.03396.x
30. Fradin, C., Mavor, A. L., Weindl, G., Schaller, M., Hanke, K., Kaufmann, S. H. E., et al. (2007). The early transcriptional response of human granulocytes to infection with Candida albicans is not essential for killing but reflects cellular communications. Infect. Immun. 75, 1493-1501. doi: 10.1128/IAI.01651-06
31. Franzosa, E. A., Morgan, X. C., Segata, N., Waldron, L., Reyes, J., Earl, A. M., et al. (2014). Relating the metatranscriptome and metagenome of the human gut. Proc. Natl. Acad. Sci. U.S.A. 111, E2329-E2338. doi: 10.1073/pnas.1319284111
32. Gardner, T. S., di Bernardo, D., Lorenz, D., and Collins, J. J. (2003). Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102-105. doi: 10.1126/science.1081900
33. Gautier, L., Cope, L., Bolstad, B. M., and Irizarry, R. A. (2004). affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307-315. doi: 10.1093/bioinformatics/btg405
34. Giannoukos, G., Ciulla, D. M., Huang, K., Haas, B. J., Izard, J., Levin, J. Z., et al. (2012). Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol. 13:r23. doi: 10.1186/gb-2012-13-3-r23
35. Greenfield, A., Hafemeister, C., and Bonneau, R. (2013). Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks. Bioinformatics 29, 1060-1067. doi: 10.1093/bioinformatics/btt099
36. Groisman, E. A., and Mouslim, C. (2006). Sensing by bacterial regulatory systems in host and non-host environments. Nat. Rev. Microbiol. 4, 705-709. doi: 10.1038/nrmicro1478
37. Gustafsson, M., and Hörnquist, M. (2010). Gene expression prediction by soft integration and the Elastic Net-best performance of the DREAM3 gene expression challenge. PLoS ONE 5:e9134. doi: 10.1371/journal.pone.0009134
38. Gustafsson, M., Hörnquist, M., and Lombardi, A. (2005). Constructing and analyzing a large-scale gene-to-gene regulatory network-Lasso-constrained inference and biological validation. IEEE/ACM Trans. Comput. Biol. Bioinform. 2, 254-261. doi: 10.1109/TCBB.2005.35
39. Guthke, R., Möller, U., Hoffmann, M., Thies, F., and Töpfer, S. (2005). Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics 21, 1626-1634. doi: 10.1093/bioinformatics/bti226
40. Haider, S., and Pal, R. (2013). Integrated analysis of transcriptomic and proteomic data. Curr. Genomics 14, 91-110. doi: 10.2174/1389202911314020003
41. Handa, Y., Nishide, H., Takeda, N., Suzuki, Y., Kawaguchi, M., and Saito, K. (2015). RNA-Seq transcriptional profiling of an arbuscular mycorrhiza provides insights into regulated and coordinated gene expression in Lotus japonicus and Rhizophagus irregularis. Plant Cell Physiol. 56, 1490-1511. doi: 10.1093/pcp/pcv071
42. Hartemink, A. J., Gifford, D. K., Jaakkola, T. S., and Young, R. A. (2001). Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomput. 422-433.
43. Hayden, K. J., Garbelotto, M., Knaus, B. J., Cronn, R. C., Rai, H., and Wright, J. W. (2014). Dual RNA-Seq of the plant pathogen Phytophthora ramorum and its tanoak host. Tree Gen. Genomes 10, 489-502. doi: 10.1007/s11295-014-0698-0
44. Hecker, M., Goertsches, R., Engelmann, R., Thiesen, H.-J., and Guthke, R. (2009a). Integrative modeling of transcriptional regulation in response to antirheumatic therapy. BMC Bioinformatics 10:262. doi: 10.1186/1471-2105-10-262
45. Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., and Guthke, R. (2009b). Gene regulatory network inference: data integration in dynamic models-a review. Biosystems 96, 86-103. doi: 10.1016/j.biosystems.2008.12.004
46. Humphrys, M. S., Creasy, T., Sun, Y., Shetty, A. C., Chibucos, M. C., Drabek, E. F., et al. (2013). Simultaneous transcriptional profiling of bacteria and their host cells. PLoS ONE 8:e80597. doi: 10.1371/journal.pone.0080597
47. Irizarry, R. A., Hobbs, B., Collin, F., Beazer-Barclay, Y. D., Antonellis, K. J., Scherf, U., et al. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249-264. doi: 10.1093/biostatistics/4.2.249
48. Ithal, N., Recknor, J., Nettleton, D., Hearne, L., Maier, T., Baum, T. J., et al. (2007). Parallel genome-wide expression profiling of host and pathogen during soybean cyst nematode infection of soybean. Mol. Plant Microbe Interact. 20, 293-305. doi: 10.1094/MPMI-20-3-0293
49. Jenner, R. G., and Young, R. A. (2005). Insights into host responses against pathogens from transcriptional profiling. Nat. Rev. Microbiol. 3, 281-294. doi: 10.1038/nrmicro1126
50. Johnson, L. J., Voisey, R. D., Johnson, A. K., Park, Z. A., Ramakrishna, M., Cao, D., et al. (2007). "Dual Affymetrix GeneChip(R) analysis of the perennial ryegrass-endophyte symbiosis, " in NZ Grassland Association: Endophyte Symposium (Wairakei), 509-513.
51. Kammenga, J. E., Herman, M. A., Ouborg, N. J., Johnson, L., and Breitling, R. (2007). Microarray challenges in ecology. Trends Ecol. Evol. 22, 273-279. doi: 10.1016/j.tree.2007.01.013
52. Kim, H. S., Choi, E. H., Khan, J., Roilides, E., Francesconi, A., Kasai, M., et al. (2005). Expression of genes encoding innate host defense molecules in normal human monocytes in response to Candida albicans. Infect. Immun. 73, 3714-3724. doi: 10.1128/IAI.73.6.3714-3724.2005
53. Koshland, D. E. Jr. (2002). Special essay. The seven pillars of life. Science 295, 2215-2216. doi: 10.1126/science.1068489
54. Kupfer, P., Huber, R., Weber, M., Vlaic, S., Häupl, T., Koczan, D., et al. (2014). Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients. BMC Med. Genomics 7:40. doi: 10.1186/1755-8794-7-40
55. Leroy, Q., and Raoult, D. (2010). Review of microarray studies for host-intracellular pathogen interactions. J. Microbiol. Methods 81, 81-95. doi: 10.1016/j.mimet.2010.02.008
56. Liao, Y., Smyth, G. K., and Shi, W. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923-930. doi: 10.1093/bioinformatics/btt656
57. Linde, J., Hortschansky, P., Fazius, E., Brakhage, A. A., Guthke, R., and Haas, H. (2012). Regulatory interactions for iron homeostasis in Aspergillus fumigatus inferred by a systems biology approach. BMC Syst. Biol. 6:6. doi: 10.1186/1752-0509-6-6
58. Linde, J., Schulze, S., Henkel, S., and Guthke, R. (2015). Data-and knowledge-based modeling of gene regulatory networks: an update. EXCLI J. 14, 346-378. doi: 10.17179/excli2015-168
59. Linde, J., Wilson, D., Hube, B., and Guthke, R. (2010). Regulatory network modelling of iron acquisition by a fungal pathogen in contact with epithelial cells. BMC Syst. Biol. 4:148. doi: 10.1186/1752-0509-4-148
60. Liu, Y., Shetty, A. C., Schwartz, J. A., Bradford, L. L., Xu, W., Phan, Q. T., et al. (2015). New signaling pathways govern the host response to C. albicans infection in various niches. Genome Res. 25, 679-689. doi: 10.1101/gr.187427.114
61. Liu, Y., Zhou, J., and White, K. P. (2013). RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301-304. doi: 10.1093/bioinformatics/btt688
62. Lowe, R. G. T., Cassin, A., Grandaubert, J., Clark, B. L., de Wouw, A. P. V., Rouxel, T., et al. (2014). Genomes and transcriptomes of partners in plant-fungal-interactions between canola (Brassica napus) and two Leptosphaeria species. PLoS ONE 9:e103098. doi: 10.1371/journal.pone.0103098
63. Lu, Y.-F., Goldstein, D. B., Angrist, M., and Cavalleri, G. (2014). Personalized medicine and human genetic diversity. Cold Spring Harb. Perspect. Med. 4:a008581. doi: 10.1101/cshperspect.a008581
64. Madar, A., Greenfield, A., Vanden-Eijnden, E., and Bonneau, R. (2010). DREAM3: network inference using dynamic context likelihood of relatedness and the Inferelator. PLoS ONE 5:e9803. doi: 10.1371/journal.pone.0009803
65. Mardis, E. R. (2008). Next-generation DNA sequencing methods. Annu. Rev. Genomics Hum. Genet. 9, 387-402. doi: 10.1146/annurev.genom.9.081307.164359
66. Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10. doi: 10.14806/ej.17.1.200
67. Meyer, P. E., Kontos, K., Lafitte, F., and Bontempi, G. (2007). Information-theoretic inference of large transcriptional regulatory networks. EURASIP J. Bioinform. Syst. Biol. 2007, 1-9. doi: 10.1155/2007/79879
68. Motley, S. T., Morrow, B. J., Liu, X., Dodge, I. L., Vitiello, A., Ward, C. K., et al. (2004). Simultaneous analysis of host and pathogen interactions during an in vivo infection reveals local induction of host acute phase response proteins, a novel bacterial stress response, and evidence of a host-imposed metal ion limited environment. Cell. Microbiol. 6, 849-865. doi: 10.1111/j.1462-5822.2004.00407.x
69. Moy, P., Qutob, D., Chapman, B. P., Atkinson, I., and Gijzen, M. (2004). Patterns of gene expression upon infection of soybean plants by Phytophthora sojae. Mol. Plant Microbe Interact. 17, 1051-1062. doi: 10.1094/MPMI.2004.17.10.1051
70. Murphy, K., and Mian, S. (1999). Modelling Gene Expression Data Using Dynamic Bayesian Networks. Technical Report, University of California at Berkeley.
71. Oosthuizen, J. L., Gomez, P., Ruan, J., Hackett, T. L., Moore, M. M., Knight, D. A., et al. (2011). Dual organism transcriptomics of airway epithelial cells interacting with conidia of Aspergillus fumigatus. PLoS ONE 6:e20527. doi: 10.1371/journal.pone.0020527
72. Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., et al. (2013). Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 14:R95. doi: 10.1186/gb-2013-14-9-r95
73. Rau, A., Jaffrézic, F., Foulley, J.-L., and Doerge, R. W. (2010). An empirical Bayesian method for estimating biological networks from temporal microarray data. Stat. Appl. Genet. Mol. Biol. 9, 1544-6115. doi: 10.2202/1544-6115.1513
74. Rosani, U., Varotto, L., Domeneghetti, S., Arcangeli, G., Pallavicini, A., and Venier, P. (2014). Dual analysis of host and pathogen transcriptomes in ostreid herpesvirus 1-positive Crassostrea gigas. Environ. Microbiol. 17, 4200-4212. doi: 10.1111/1462-2920.12706
75. Rosenthal, A. Z., Matson, E. G., Eldar, A., and Leadbetter, J. R. (2011). RNA-seq reveals cooperative metabolic interactions between two termite-gut spirochete species in co-culture. ISME J. 5, 1133-1142. doi: 10.1038/ismej.2011.3
76. Roux, B., Rodde, N., Jardinaud, M.-F., Timmers, T., Sauviac, L., Cottret, L., et al. (2014). An integrated analysis of plant and bacterial gene expression in symbiotic root nodules using laser-capture microdissection coupled to RNA sequencing. Plant J. 77, 817-837. doi: 10.1111/tpj.12442
77. Satinsky, B. M., Crump, B. C., Smith, C. B., Sharma, S., Zielinski, B. L., Doherty, M., et al. (2014). Microspatial gene expression patterns in the amazon river plume. Proc. Natl. Acad. Sci. U.S.A. 111, 11085-11090. doi: 10.1073/pnas.1402782111
78. Schleicher, J., Conrad, T., Gustafsson, M., Cedersund, G., Guthke, R., and Linde, J. (2016). Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions. Brief Funct. Genomics. doi: 10.1093/bfgp/elv064. [Epub ahead of print].
79. Schulze, S., Henkel, S. G., Driesch, D., Guthke, R., and Linde, J. (2015). Computational prediction of molecular pathogen-host interactions based on dual transcriptome data. Front. Microbiol. 6:65. doi: 10.3389/fmicb.2015.00065
80. Segarra, A., Mauduit, F., Faury, N., Trancart, S., Dégremont, L., Tourbiez, D., et al. (2014). Dual transcriptomics of virus-host interactions: comparing two pacific oyster families presenting contrasted susceptibility to ostreid herpesvirus 1. BMC Genomics 15:580. doi: 10.1186/1471-2164-15-580
81. SEQC/MAQC-III Consortium. (2014). A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903-914. doi: 10.1038/nbt.2957
82. Shang, J., Zhu, F., Vongsangnak, W., Tang, Y., Zhang, W., and Shen, B. (2014). Evaluation and comparison of multiple aligners for next-generation sequencing data analysis. BioMed Res. Int. 2014:309650. doi: 10.1155/2014/309650
83. Shapiro, E., Biezuner, T., and Linnarsson, S. (2013). Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618-630. doi: 10.1038/nrg3542
84. Smyth, G. (2005). "limma: Linear models for microarray data, " in Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Stat Biol Health, eds R. Gentleman, V. Carey, W. Huber, R. Irizarry, and S. Dudoit (New York, NY: Springer), 397-420.
85. Soneson, C., and Delorenzi, M. (2013). A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14:91. doi: 10.1186/1471-2105-14-91
86. Stewart, F. J., Ulloa, O., and DeLong, E. F. (2011). Microbial metatranscriptomics in a permanent marine oxygen minimum zone. Environ. Microbiol. 14, 23-40. doi: 10.1111/j.1462-2920.2010.02400.x
87. Stolovitzky, G., Monroe, D., and Califano, A. (2007). Dialogue on reverse-engineering assessment and methods: the dream of high-throughput pathway inference. Ann. N.Y. Acad. Sci. 1115, 1-22. doi: 10.1196/annals.1407.021
88. Teixeira, P. J. P. L., de Toledo Thomazella, D. P., Reis, O., do Prado, P. F. V., do Rio, M. C. S., Fiorin, G. L., et al. (2014). High-resolution transcript profiling of the atypical biotrophic interaction between Theobroma cacao and the fungal pathogen Moniliophthora perniciosa. Plant Cell 26, 4245-4269. doi: 10.1105/tpc.114.130807
89. Tierney, L., Linde, J., Müller, S., Brunke, S., Molina, J. C., Hube, B., et al. (2012). An interspecies regulatory network inferred from simultaneous RNA-seq of Candida albicans invading innate immune cells. Front. Microbiol. 3:85. doi: 10.3389/fmicb.2012.00085
90. Toepfer, S., Guthke, R., Driesch, D., Woetzel, D., and Pfaff, M. (2007). "The NetGenerator algorithm: reconstruction of gene regulatory networks, " in Knowledge Discovery and Emergent Complexity in Bioinformatics, Lecture Notes in Bioinformatics, Vol. 4366 (Ghent; Berlin; Heidelberg: Springer Verlag), 119-130.
91. Valles-Colomer, M., Darzi, Y., Vieira-Silva, S., Falony, G., Joossens, M., and Raes, J. (2016). Meta-omics in ibd research: applications, challenges and guidelines. J. Crohns Colitis. doi: 10.1093/ecco-jcc/jjw024. [Epub ahead of print].
92. Vojvodic, S., Johnson, B. R., Harpur, B. A., Kent, C. F., Zayed, A., Anderson, K. E., et al. (2015). The transcriptomic and evolutionary signature of social interactions regulating honey bee caste development. Ecol. Evol. 5, 4795-4807. doi: 10.1002/ece3.1720
93. Wang, Z., Gerstein, M., and Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57-63. doi: 10.1038/nrg2484
94. Weber, M., Henkel, S. G., Vlaic, S., Guthke, R., van Zoelen, E. J., and Driesch, D. (2013). Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0. BMC Syst. Biol. 7:1. doi: 10.1186/1752-0509-7-1
95. Westermann, A. J., Gorski, S. A., and Vogel, J. (2012). Dual RNA-seq of pathogen and host. Nat. Rev. Microbiol. 10, 618-630. doi: 10.1038/nrmicro2852
96. Wu, M., and Chan, C. (2012). Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data. Brief Bioinform. 13, 150-161. doi: 10.1093/bib/bbr029
97. Yamagishi, J., Natori, A., Tolba, M., Mongan, A. E., Sugimoto, C., Katayama, T., et al. (2014). Interactive transcriptome analysis of malaria patients and infecting Plasmodium falciparum. Genome Res. 24, 1433-1444. doi: 10.1101/gr.158980.113
98. Yang, X., Liu, D., Liu, F., Wu, J., Zou, J., Xiao, X., et al. (2013). HTQC: a fast quality control toolkit for Illumina sequencing data. BMC Bioinformatics 14:33. doi: 10.1186/1471-2105-14-33
99. Yeung, K. Y., Dombek, K. M., Lo, K., Mittler, J. E., Zhu, J., Schadt, E. E., et al. (2011). Construction of regulatory networks using expression time-series data of a genotyped population. Proc. Natl. Acad. Sci. U.S.A 108, 19436-19441. doi: 10.1073/pnas.1116442108
100. Zhang, X., Liu, K., Liu, Z.-P., Duval, B., Richer, J.-M., Zhao, X.-M., et al. (2013). Narromi: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics 29, 106-113. doi: 10.1093/bioinformatics/bts619
101. Zhang, Z. H., Jhaveri, D. J., Marshall, V. M., Bauer, D. C., Edson, J., Narayanan, R. K., et al. (2014). A comparative study of techniques for differential expression analysis on RNA-Seq data. PLoS ONE 9:e103207. doi: 10.1371/journal.pone.0103207
102. Zou, H., and Hastie, T. (2005). Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301-320. doi: 10.1111/j.1467-9868.2005.00503.x