Inferring the perturbation time from biological time course data

Bioinformatics

Volumn 32, Issue 19, 2016, Pages 2956-2964

  Yang, Jing ^a   Penfold, Christopher A ^b   Grant, Murray R ^c   Rattray, Magnus ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSITY OF WARWICK   (United Kingdom)

^c UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARABIDOPSIS; BAYES THEOREM; BIOLOGICAL MODEL; GENETIC TRANSCRIPTION; GENETICS; PSEUDOMONAS SYRINGAE; STATISTICAL MODEL;

ALGORITHMS; ARABIDOPSIS; BAYES THEOREM; MODELS, GENETIC; MODELS, STATISTICAL; PSEUDOMONAS SYRINGAE; TRANSCRIPTION, GENETIC;

EID: 84990898428     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw329     Document Type: Article

References (42)

1. Adams-Phillips, L. et al. (2010) Disruption of poly (adp-ribosyl) ation mechanisms alters responses of arabidopsis to biotic stress. Plant Physiol., 152, 267-280.
2. Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biol., 11, R106.
3. Angelini, C. et al. (2008) Bats: a Bayesian user-friendly software for analyzing time series microarray experiments. BMC Bioinformatics, 9, 415.
4. Bar-Joseph, Z. (2004) Analyzing time series gene expression data. Bioinformatics, 20, 2493-2503.
5. Brewer, H.C. et al. (2014) Mutations in the arabidopsis homoserine kinase gene dmr1 confer enhanced resistance to F. culmorum and F. graminearum. BMC Plant Biol., 14, 317.
6. Conesa, A. et al. (2006) masigpro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics, 22, 1096-1102.
7. Crawford, N.M. and Guo, F.Q. (2005) New insights into nitric oxide metabolism and regulatory functions. Trends Plant Sci., 10, 195-200.
8. de Torres, Z.M. et al. (2015) Novel JAZ co-operativity and unexpected ja dynamics underpin Arabidopsis defence responses to Pseudomonas syringae infection. New Phytol, 209, 1120-1134.
9. de Torres-Zabala, M. et al. (2007) Pseudomonas syringae pv. tomato hijacks the Arabidopsis abscisic acid signalling pathway to cause disease. EMBO J., 26, 1434-1443.
10. de Torres Zabala, M. et al. (2009) Antagonism between salicylic and abscisic acid reflects early host-pathogen conflict and moulds plant defence responses. Plant J., 59, 375-386.
11. de Torres Zabala, M. et al. (2015) Chloroplasts play a central role in plant defence and are targeted by pathogen effectors. Nat. Plants, 1, 15074.
12. DebRoy, S. et al. (2004) A family of conserved bacterial effectors inhibits salicylic acid-mediated basal immunity and promotes disease necrosis in plants. Proc. Natl. Acad. Sci. U. S. A., 101, 9927-9932.
13. Dudoit, S. et al. (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat. Sin., 12, 111-140.
14. Gao, P. et al. (2008) Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. Bioinformatics, 24, i70-i75.
15. Grant, M. et al. (2000) The RPM1 plant disease resistance gene facilitates a rapid and sustained increase in cytosolic calcium that is necessary for the oxidative burst and hypersensitive cell death. Plant J., 23, 441-450.
16. Hardcastle, T.J. and Kelly, K.A. (2010) bayseq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 11, 422.
17. Heinonen, M. et al. (2014) Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction. Bioinformatics.
18. Honkela, A. et al. (2010) Model-based method for transcription factor target identification with limited data. Proc. Natl. Acad. Sci., 107, 7793-7798.
19. Hua, Z.M. et al. (2006) Activation of the nacl-and drought-induced RD29A and RD29B promoters by constitutively active Arabidopsis MAPKK or MAPK proteins. Plant, Cell Environ., 29, 1761-1770.
20. Huibers, R.P. et al. (2013) Powdery mildew resistance in tomato by impairment of SlPMR4 and SlDMR1. PLoS ONE, 8, e67467.
21. Kalaitzis, A.A. and Lawrence, N.D. (2011) A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression. BMC Bioinformatics, 12, 180.
22. Kerr, M.K. et al. (2000) Analysis of variance for gene expression microarray data. J. Comput. Biol., 7, 819-837.
23. Kim, J. et al. (2013) A method to identify differential expression profiles of time-course gene data with Fourier transformation. BMC Bioinformatics, 14, 310.
24. Kong, Q. et al. (2012) The MEKK1-MKK1/MKK2-MPK4 kinase cascade negatively regulates immunity mediated by a mitogen-activated protein kin-ase kinase kinase in Arabidopsis. Plant Cell, 24, 2225-2236.
25. Lawrence, N. (2005) Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res., 6, 1783-1816.
26. Lewis, L. A. et al. (2015) Transcriptional dynamics driving MAMP-triggered immunity and pathogen effector-mediated immunosuppression in Arabidopsis leaves following infection with Pseudomonas syringae pv tomato dc3000. Plant Cell, 27, 3038-3064.
27. Macho, A. P. et al. (2012) Aspartate oxidase plays an important role in Arabidopsis stomatal immunity. Plant Physiol., 159, 1845-1856.
28. Mano, J. et al. (2005) Protection against photooxidative injury of tobacco leaves by 2-alkenal reductase. Detoxication of lipid peroxide-derived reactive carbonyls. Plant Physiol., 139, 1773-1783.
29. Mukhtar, M. S. et al. (2011) Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science, 333, 596-601.
30. Rasmussen, C.E. and Williams, C.K. (2006) Gaussian Processes for Machine Learning, vol. 2. The MIT Press.
31. Robinson, M. D. et al. (2010) edger: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140.
32. Rodrigues, A. et al. (2013) ABI1 and PP2CA phosphatases are negative regulators of snf1-related protein kinase1 signaling in Arabidopsis. Plant Cell, 25, 3871-3884.
33. Ryrie, I. and Scott, K. (1969) Nicotinate, quinolinate and nicotinamide as precursors in the biosynthesis of nicotinamide-adenine dinucleotide in barley. Biochem. J., 115, 679-685.
34. Song, J. et al. (2015) PARP2 is the predominant poly (ADP-ribose) polymerase in Arabidopsis DNA damage and immune responses. PLoS Genet., 11, e1005200.
35. Stegle, O. et al. (2010) A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series. J. Comput. Biol., 17, 355-367.
36. Storey, J. D. et al. (2005) Significance analysis of time course microarray experiments. Proc. Natl. Acad. Sci. U. S. A., 102, 12837-12842.
37. van Damme, M. et al. (2009) Downy mildew resistance in arabidopsis by mutation of homoserine kinase. Plant Cell, 21, 2179-2189.
38. Wang, Y. et al. (2010) A Pseudomonas syringae ADP-ribosyltransferase inhibits Arabidopsis mitogen-activated protein kinase kinases. Plant Cell, 22, 2033-2044.
39. Weßling, R. et al. (2014) Convergent targeting of a common host protein-network by pathogen effectors from three kingdoms of life. Cell Host Microbe, 16, 364-375.
40. Yuan, M. (2006) Flexible temporal expression profile modelling using the Gaussian process. Comput. Stat. Data Anal., 51, 1754-1764.
41. Zhang, X. et al. (2008) The arabidopsis map kinase kinase 7: a crosstalk point between auxin signaling and defense responses? Plant Signal. Behav., 3, 272-274.
42. Zipfel, C. (2014) Plant pattern-recognition receptors. Trends Immunol., 35, 345-351.