Joint bayesian inference of condition-specific miRNA and transcription factor activities from combined gene and microRNA expression data

Bioinformatics

Volumn 28, Issue 13, 2012, Pages 1714-1720

  Zacher, Benedikt ^a   Abnaof, Khalid ^b   Gade, Stephan ^c   Younesi, Erfan ^b   Tresch, Achim ^a,d   Fröhlich, Holger ^b  

^a UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF BONN   (Germany)

^c GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^d UNIVERSITY OF COLOGNE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ESCHERICHIA COLI;

MESSENGER RNA; MICRORNA; TRANSCRIPTION FACTOR;

ARTICLE; BAYES THEOREM; BIOLOGY; ESCHERICHIA COLI; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; HUMAN; METABOLISM; METHODOLOGY; OVARY TUMOR; PANCREAS TUMOR;

BAYES THEOREM; COMPUTATIONAL BIOLOGY; ESCHERICHIA COLI; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; HUMANS; MICRORNAS; OVARIAN NEOPLASMS; PANCREATIC NEOPLASMS; RNA, MESSENGER; TRANSCRIPTION FACTORS;

EID: 84864013930     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts257     Document Type: Article

References (61)

1. Aerts, S. et al. (2005) Toucan 2: the all-inclusive open source workbench for regulatory sequence analysis. Nucleic Acids Res., 33, W393-W396.
2. Aerts, S. et al. (2010) Robust target gene discovery through transcriptome perturbations and genome-wide enhancer predictions in drosophila uncovers a regulatory basis for sensory specification. PLoS Biol., 8, e1000435.
3. Asif, H.M.S. and Sanguinetti, G. (2011) Large-scale learning of combinatorial transcriptional dynamics from gene expression. Bioinformatics, 27, 1277-1283.
4. Atlung, T. et al. (1996) The histone-like protein h-ns acts as a transcriptional repressor for expression of the anaerobic and growth phase activator appy of escherichia coli. J. Bacteriol, 178, 3418-3425.
5. Bauer, S. et al. (2010) GOing Bayesian: model-based gene set analysis of genome-scale data. Nucleic Acids Res., 38, 3523-3532.
6. Bauer, T. et al. (2011) Rip: the regulatory interaction predictor-a machine learning-based approach for predicting target genes of transcription factors. Bioinformatics, 27, 2239-2247.
7. Betel, D. et al. (2008) The microrna.org resource: targets and expression. Nucleic Acids Res., 36, D149-D153.
8. Boulesteix, A.-L. and Strimmer, K. (2005) Predicting transcription factor activities from combined analysis of microarray and chip data: a partial least squares approach. Theor. Biol. Med. Model., 2, 23.
9. Brondsted, L. and Atlung, T. (1994) Anaerobic regulation of the hydrogenase 1 (hya) operon of escherichia coli. J. Bacteriol, 176, 5423-5428.
10. Castelo, R. and Roverato, A. (2009) Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J. Comput. Biol., 16, 213-227.
11. Cheng, C. and Li, L.M. (2008) Inferring microrna activities by combining gene expression with microrna target prediction. PLoS One, 3, e1989.
12. Chodavarapu, S. et al. (2008) Escherichia coli dps interacts with dnaa protein to impede initiation: a model of adaptive mutation. Mol. Microbiol., 67, 1331-1346.
13. Covert, M.W. et al. (2004) Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429, 92-96.
14. Ding, H. and Demple, B. (1998) Thiol-mediated disassembly and reassembly of [2fe-2s] clusters in the redox-regulated transcription factor soxr. Biochemistry, 37, 17280-17286.
15. Ferretti, G. et al. (2006) Controversies on autoimmunity and prognosis in cancer. Br. J. Cancer, 95, 763; author reply 764-763; author reply 765.
16. Foat, B.C. et al. (2005) Profiling condition-specific, genome-wide regulation of mrna stability in yeast. Proc. Natl Acad. Sci. USA, 102, 17675-17680.
17. Foat, B.C. et al. (2006) Statistical mechanical modeling of genome-wide transcription factor occupancy data by matrixreduce. Bioinformatics, 22, e141-e149.
18. Goh, E.-B. et al. (2005) Hierarchical control of anaerobic gene expression in escherichia coli k-12: the nitrate-responsive narx-narl regulatory system represses synthesis of the fumarate-responsive dcus-dcur regulatory system. J. Bacteriol, 187, 4890-4899.
19. Griffiths-Jones, S. et al. (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res., 36, D154-D158.
20. Heckerman, D. (1997) A Tutorial on Learning With Bayesian Networks. Data Mining and Knowledge Discovery, 1, 79-119.
21. Hochreiter, S. et al. (2006) A new summarization method for Affymetrix probe level data. Bioinformatics (Oxford, England), 22, 943-9.
22. Honda, H. et al. (2006) Crucial roles of sp1 and epigenetic modifications in the regulation of the cldn4 promoter in ovarian cancer cells. J. Biol. Chem., 281, 21433-21444.
23. Huang, G. et al. (2011) mirconnx: condition-specific mrna-microrna network integrator. Nucleic Acids Res., 39, W416-W423.
24. Huang, J. et al. (2007) Using expression profiling data to identify human microrna targets. Nature Methods, 4, 1045-1049.
25. Iwig, J.S. et al. (2006) Nickel homeostasis in escherichia coli - the rcnr-rcna efflux pathway and its linkage to nikr function. Mol. Microbiol., 62, 252-262.
26. John, B. et al. (2004) Human MicroRNA targets. PLoS Biol., 2, e363.
27. Jones, S. et al. (2008) Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science, 321, 1801-1806.
28. Kayed, H. et al. (2005) Localization of the human hedgehog-interacting protein (hip) in the normal and diseased pancreas. Mol. Carcinog, 42, 183-192.
29. Khatun, S. et al. (2003) Clinical implications of expression of ets-1 in relation to angiogenesis in ovarian cancers. Cancer Sci., 94, 769-773.
30. Kittleson, M.M. et al. (2009) Genomic and Personalized Medicine, chapter Transcriptomics: Translation of Global Expression Analysis to Genomic Medicine, pp. 143-156. Elsevier, London, UK.
31. Lamark, T. et al. (1996) The complex bet promoters of escherichia coli: regulation by oxygen (arca), choline (beti), and osmotic stress. J. Bacteriol, 178, 1655-1662.
32. Latchman, D. (2005) Gene regulation: a eukaryotic perspective. Advanced text. Taylor & Francis, London, UK.
33. Liang, Z. et al. (2011) miract: a web tool for evaluating microrna activity based on gene expression data. Nucleic Acids Res., 39, W139-W144.
34. Lim, W. et al. (2009) Master regulators used as breast cancer metastasis classifier. In Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, p. 504. NIH Public Access.
35. Lin, H. et al. (2005) Genetic reconstruction of the aerobic central metabolism in escherichia coli for the absolute aerobic production of succinate. Biotechnol Bioeng., 89, 148-156.
36. Li, H. et al. (2005) Signaling mechanisms responsible for lysophosphatidic acid-induced urokinase plasminogen activator expression in ovarian cancer cells. J. Biol. Chem., 280, 10564-10571.
37. Loh, Y.-H. et al. (2006) The oct4 and nanog transcription network regulates pluripotency in mouse embryonic stem cells. Nat. Genet., 38, 431-440.
38. Martinez, N.J. and Walhout, A.J.M. (2009) The interplay between transcription factors and micrornas in genome-scale regulatory networks. Bioessays, 31, 435-445.
39. Matsubara, M. et al. (2000) Tuning of the porin expression under anaerobic growth conditions by his-to-asp cross-phosphorelay through both the envz-osmosensor and arcb-anaerosensor in escherichia coli. Genes. Cells, 5, 555-569.
40. Nam, S. et al. (2009) Microrna and mrna integrated analysis (mmia): a web tool for examining biological functions of microrna expression. Nucleic Acids Res., 37, W356-W362.
41. Nanjundan, M. et al. (2007) Identification of a novel splice variant of aml1b in ovarian cancer patients conferring loss of wild-type tumor suppressive functions. Oncogene, 26, 2574-2584.
42. Ocone, A. and Sanguinetti, G. (2011) Reconstructing transcription factor activities in hierarchical transcription network motifs. Bioinformatics, 27, 2873-2879.
43. Opper, M. and Sanguinetti, G. (2010) Learning combinatorial transcriptional dynamics from gene expression data. Bioinformatics (Oxford, England), 26, 1623-1629.
44. Outten, F.W. et al. (2001) The independent cue and cus systems confer copper tolerance during aerobic and anaerobic growth in escherichia coli. J. Biol. Chem., 276, 30670-30677.
45. Papadopoulos, G.L. et al. (2009) The database of experimentally supported targets: a functional update of tarbase. Nucleic Acids Res., 37, D155-D158.
46. Pham, N.-A. et al. (2008) Immunohistochemical analysis of changes in signaling pathway activation downstream of growth factor receptors in pancreatic duct cell carcinogenesis. BMC Cancer, 8, 43.
47. Rowe, J.L. et al. (2005) Complex transcriptional control links nikabcde-dependent nickel transport with hydrogenase expression in escherichia coli. J. Bacteriol, 187, 6317-6323.
48. Rozenblum, E. et al. (2002)Agenomic map of a 6-mb region at 13q21-q22 implicated in cancer development: identification and characterization of candidate genes. Hum. Genet., 110, 111-121.
49. Sales, G. et al. (2010) Magia, a web-based tool for mirna and genes integrated analysis. Nucleic Acids Res., 38, W352-W359.
50. Shepherd, M. et al. (2010) Compensations for diminished terminal oxidase activity in escherichia coli: cytochrome bd-ii-mediated respiration and glutamate metabolism. J. Biol. Chem., 285, 18464-18472.
51. Smyth, G.K. (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, 3.
52. Stim-Herndon, K.P. et al. (1996) Molecular characterization of adiy, a regulatory gene which affects expression of the biodegradative acid-induced arginine decarboxylase gene (adia) of escherichia coli. Microbiology, 142 (Pt 5), 1311-1320.
53. Tran, L.M. et al. (2005) gnca: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation. Metab. Eng., 7, 128-141.
54. Wang, W. et al. (2005) Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation. Proc. Natl Acad. Sci. USA, 102, 1998-2003.
55. Wang, X. and Naqa, I.M.E. (2008) Prediction of both conserved and nonconserved microrna targets in animals. Bioinformatics, 24, 325-332.
56. Wiener, Z. et al. (2004) Synergistic induction of the fas (cd95) ligand promoter by max and nfkappab in human non-small lung cancer cells. Exp. Cell Res., 299, 227-235.
57. Wuchty, S. et al. (2011) Prediction of associations between micrornas and gene expression in glioma biology. PLoS One, 6, e14681.
58. Wu, M. and Chan, C. (2011) Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data. Brief. Bioinform.
59. Yuan, M. and Lin, Y. (2007) Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, 68, 49-67.
60. Zhou, Z. et al. (2011) Genome-wide transcriptome and proteome analysis of escherichia coli expressing irre, a global regulator of deinococcus radiodurans. Mol. Biosyst., 7, 1613-1620.
61. Zientz, E. et al. (1998) Fumarate regulation of gene expression in escherichia coli by the dcusr (dcusr genes) two-component regulatory system. J. Bacteriol, 180, 5421-5425.