Simulating systems genetics data with SysGenSIM

Bioinformatics

Volumn 27, Issue 17, 2011, Pages 2459-2462

  Pinna, Andrea ^a   Soranzo, Nicola ^a   Hoeschele, Ina ^b,c   de la Fuente, Alberto ^a  

^a CRS4 Bioinformatica ^*  (Italy)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^c VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER PROGRAM; COMPUTER SIMULATION; GENE EXPRESSION; GENE REGULATORY NETWORK; GENOTYPE; PHENOTYPE;

COMPUTER SIMULATION; GENE EXPRESSION; GENE REGULATORY NETWORKS; GENOTYPE; PHENOTYPE; SOFTWARE;

EID: 80051929549     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr407     Document Type: Article

References (39)

1. Aten,J.E. et al. (2008) Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Syst. Biol., 2, 34.
2. Bansal,M. et al. (2007) How to infer gene networks from expression profiles. Mol. Syst. Biol., 3, 78.
3. Barabasi,A.L. and Albert,R. (1999) Emergence of scaling in random networks. Science, 286, 509-512.
4. Barabasi,A.L. and Oltvai,Z.N. (2004) Network biology: understanding the cell's functional organization. Nat. Rev. Genet., 5, 101-113.
5. Brem,R.B. and Kruglyak,L. (2005) The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA, 102, 1572-1577.
6. Brem,R.B. et al. (2002) Genetic dissection of transcriptional regulation in budding yeast. Science, 296, 752-755.
7. Chaibub Neto,E. et al. (2008) Inferring causal phenotype networks from segregating populations. Genetics, 179, 1089-1100.
8. Chun,H. and Keles,S. (2009) Expression quantitative trait Loci mapping with multivariate sparse partial least squares regression. Genetics, 182, 79-90.
9. Cupples,L.A. et al. (2009) Genetic AnalysisWorkshop 16: Strategies for genome-wide association study analyses. BMC Proc., 3 (Suppl. 7), S1.
10. de la Fuente,A. et al. (2002) Linking the genes: inferring quantitative gene networks from microarray data. Trends Genet., 18, 395-398.
11. Dudek,S.M. et al. (2006) Data simulation software for whole-genome association and other studies in human genetics. Pac. Symp. Biocomput., 11, 499-510.
12. Erdös,P. and Renyi,A. (1959) On Random Graphs. Publ. Math. Debrecen, 6, 290-297.
13. Fisher,R.A. (1926) The arrangement of field experiments. J. Ministry Agric. Great Britain, 33, 503-511.
14. Guelzim,N. et al. (2002) Topological and causal structure of the yeast transcriptional regulatory network. Nat. Genet., 31, 60-63.
15. Hartwell,L.H. et al. (1999) From molecular to modular cell biology. Nature, 402, C47-C52.
16. Jansen,R.C. (2003) Studying complex biological systems using multifactorial perturbation. Nat. Rev. Genet., 4, 145-151.
17. Jansen,R.C. and Nap,J.P. (2001) Genetical genomics: the added value from segregation. Trends Genet., 17, 388-391.
18. Keurentjes,J.J. et al. (2006) The genetics of plant metabolism. Nat. Genet., 38, 842-849.
19. Lee,S.I. et al. (2006) Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification. Proc. Natl Acad. Sci. USA, 103, 14062-14067.
20. Lee,S.I. et al. (2009) Learning a prior on regulatory potential from eQTL data. PLoS Genet., 5, e1000358.
21. Li,R. et al. (2006) Structural model analysis of multiple quantitative traits. PLoS Genet., 2, e114.
22. Liu,B. et al. (2008) Gene network inference via structural equation modeling in genetical genomics experiments. Genetics, 178, 1763-1776.
23. Ma'ayan,A. et al. (2008) Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks. Proc. Natl Acad. Sci. USA, 105, 19235-19240.
24. Ma,H.W. and Zeng,A.P. (2003) The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics, 19, 1423-1430.
25. Marbach,D. et al. (2009) Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J. Comput. Biol., 16, 229-239.
26. Meir,E. et al. (2002) Ingeneue: a versatile tool for reconstituting genetic networks, with examples from the segment polarity network. J. Exp. Zool., 294, 216-251.
27. Mendes,P. et al. (2003) Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics, 19 (Suppl. 2), ii122-ii129.
28. Parkhomenko,E. et al. (2007) Genome-wide sparse canonical correlation of gene expression with genotypes. BMC Proc., 1 (Suppl. 1), S119.
29. Rockman,M.V. (2008) Reverse engineering the genotype-phenotype map with natural genetic variation. Nature, 456, 738-744.
30. Schadt,E.E. (2009) Molecular networks as sensors and drivers of common human diseases. Nature, 461, 218-223.
31. Schadt,E.E. et al. (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature, 422, 297-302.
32. Schadt,E.E. et al. (2005) An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet., 37, 710-717.
33. Schaffter,T. et al. (2011) GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods. Bioinformatics [Epub ahead of print June 22, 2011, doi:10.1093/bioinformatics/btr373].
34. Stolovitzky,G. et al. (2007) Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann. NY Acad. Sci., 1115, 1-22.
35. Stolovitzky,G. et al. (2009) Lessons from the DREAM2 Challenges. Ann. N Y Acad. Sci., 1158, 159-195.
36. Waaijenborg,S. et al. (2008) Quantifying the association between gene expressions and DNA-markers by penalized canonical correlation analysis. Stat. Appl. Genet. Mol. Biol., 7, Article3.
37. Wright,F.A. et al. (2007) Simulating association studies: a data-based resampling method for candidate regions or whole genome scans. Bioinformatics, 23, 2581-2588.
38. Zhang,W. et al. (2010) A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules. PLoS Comput. Biol., 6, e1000642.
39. Zhu,J. et al. (2004) An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet. Genome Res., 105, 363-374.