*omeSOM: A software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Milone, Diego H ^a,d   Stegmayer, Georgina S ^b   Kamenetzky, Laura ^b   López, Mariana ^b   Lee, Je M ^c   Giovannoni, James J ^c   Carrari, Fernando ^b  

^a UNIVERSIDAD NACIONAL DEL LITORAL   (Argentina)

^b Facultad de Ciencias Naturales e Instituto M Lillo   (Argentina)

^c MAX PLANCK INSTITUTE OF MOLECULAR PLANT PHYSIOLOGY   (Germany)

^d Boyce Thompson Institute for Plant Research   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL TOOLS; EXPERIMENTAL SYSTEM; INTERSPECIFIC CROSS; METABOLIC PATHWAYS; METABOLITE PROFILES; NUMBER OF CLUSTERS; PHENOTYPIC VARIATIONS; USER FRIENDLY INTERFACE;

BIOMOLECULES; CLUSTERING ALGORITHMS; CROPS; DATA MINING; GENES; METABOLISM; METABOLITES; SELF ORGANIZING MAPS; VISUALIZATION;

DATA VISUALIZATION;

ARTICLE; CLUSTER ANALYSIS; COMPUTER PROGRAM; CROP; CROSS BREEDING; DATA MINING; FACTUAL DATABASE; GENETIC TRANSCRIPTION; GENETICS; METABOLISM; METHODOLOGY; PLANT GENOME;

CLUSTER ANALYSIS; CROPS, AGRICULTURAL; CROSSES, GENETIC; DATA MINING; DATABASES, FACTUAL; GENOME, PLANT; METABOLIC NETWORKS AND PATHWAYS; SOFTWARE; TRANSCRIPTION, GENETIC;

EID: 77957007197     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-438     Document Type: Article

References (42)

1. Bino R, Hall R, Fiehn O, Kopka J, Saito K, Draper J, Nikolau B, Mendes P, Roessner-Tunali U, Beale M, Trethewey R, Lange B, Wurtele E, Sumner L. Potential of metabolomics as a functional genomics tool. Trends in Plant Science 2004, 9:418-425. 10.1016/j.tplants.2004.07.004, 15337491.
2. Carrari F, Baxter C, Usadel B, Urbanczyk-Wochniak E, Zanor M, Nunes-Nesi A, Nikiforova V, Centero D, Ratzka A, Pauly M, Sweetlove L, Fernie A. Integrated Analysis of Metabolite and Transcript Levels Reveals the Metabolic Shifts That Underlie Tomato Fruit Development and Highlight Regulatory Aspects of Metabolic Network Behavior. Plant Physiology 2006, 142:1380-1396. 10.1104/pp.106.088534, 1676044, 17071647.
3. Bylesj M, Eriksson D, Kusano M, Moritz T, Trygg J. Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data. Plant Journal 2007, 52(6):1181-1189.
4. Li Z, Fu B, Gao Y, Xu J, Ali J, Lafitte H, Jiang Y, Rey JD, Vijayakumar C, Maghirang R, Zheng T, Zhu L. Genome-wide Introgression Lines and their Use in Genetic and Molecular Dissection of Complex Phenotypes in Rice (Oryza sativa L.). Plant Molecular Biology 2005, 59:33-52. 10.1007/s11103-005-8519-3, 16217600.
5. Rieseberg L, Wendel J. Introgression and its consequences in plants 1993, 1. Oxford University Press.
6. Lippman Z, Semel Y, Zamir D. An integrated view of quantitative trait variation using tomato interspecific introgression lines. Current Opinion in Genetics and Development 2007, 17:1-8.
7. Jingyuan JF, Joost JK, Bouwmeester H, America T, Francel WV, Jane LW, Michael HB, de Vos Ric C, Dijkstra M, Richard AS, Johannes F, Koornneef M, Vreugdenhil D, Breitling R, Ritsert CJ. System-wide molecular evidence for phenotypic buffering in Arabidopsis. Nature genetics 2009, 41(2):166-167. 10.1038/ng.308, 19169256.
8. Joung J, Corbett A, Fellman S, Tieman D, Klee H, Giovannoni J, Fei Z. Plant MetGenMAP: an integrative analysis system for plant systems biology. Plant Physiology 2009, 151:1758-1768. 10.1104/pp.109.145169, 2786002, 19819981.
9. Keedwell E, Narayanan A. Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems 2005, Wiley.
10. Polanski A, Kimmel M. Bioinformatics 2007, Springer-Verlag, NY.
11. Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J, Coller H, Loh M, Downing J, Caligiuri M, Bloomfield C, Lander E. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286:531-537. 10.1126/science.286.5439.531, 10521349.
12. Olson D, Delen D. Advanced Data Mining 2008, Springer.
13. Xu R, II DW. Clustering 2009, Wiley and IEEE Press.
14. Larose D. Discovering Knowledge in Data: An Introduction to Data Mining 2005, Wiley-Interscience.
15. Wolfe C, Kohane I, Butte A. Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks. BMC Bioinformatics 2005, 6:227-237. 10.1186/1471-2105-6-227, 1239911, 16162296.
16. Usadel B, Obayashi T, Mutwil M, Giorgi F, Bassel G, Tanimoto M, Chow A, Steinhauser D, Persson S, Provart N. Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant, Cell & Environment 2009, 32(12):1633-1651.
17. Tasoulis D, Plagianakos V, Vrahatis M. Computational Intelligence in Bioinformatics of Studies in Computational Intelligence 2008, 94. Springer.
18. Fogel G, Corne D, Pan Y. Computational Intelligence in Bioinformatics 2007, Wiley-IEEE Press.
19. Kelemen A, Abraham A, Chen Y. Computational Intelligence in Bioinformatics 2008, Springer.
20. Kohonen T, Schroeder M, Huang T. Self-Organizing Maps 2005, Springer-Verlag New York, Inc.
21. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander E, Golub T. Interpreting patterns of gene expression with self-organizing maps: Methods and applications to hematopoietic differentiation 1999, 96(6):2907-2912. Proc Natl Acad Sci .
22. Wang J, Delabie J, Aasheim H, Smeland E, Myklebost O. Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study. BMC Bioinformatics 2002, 3:36-46. 10.1186/1471-2105-3-36, 138792, 12445336.
23. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov J. GenePattern 2.0. Nature Genetics 2006, 38(5):500-501.
24. Murer AP, Mehlitz A, Mollenkopf HJ, Meyer TF. Gene Expression Profiles of Chlamydophila pneumoniae during the Developmental Cycle and Iron DepletionMediated Persistence. PLoS Pathology 2007, 3(6):83-93.
25. Newman A, Cooper J. AutoSOME: A clustering method for identifying gene expression modules without prior knowledge of cluster number. BMC Bioinformatics 2010, 11:117. 10.1186/1471-2105-11-117, 2846907, 20202218.
26. Allen E, Moing A, Ebbels TM, Maucourt M, Tomos AD, Rolin D, Hooks MA. Correlation Network Analysis reveals a sequential reorganization of metabolic and transcriptional states during germination and gene-metabolite relationships in developing seedlings of Arabidopsis. BMC Systems Biology 2010, 4:62-72. 10.1186/1752-0509-4-62, 2890501, 20465807.
27. Hirai M, Yano M, Goodenowe D, Kanaya S, Kimura T, Awazuhara M, Arita M, Fujiwara T, Saito K. Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the United States of America 2004, 101:10205-10210. 10.1073/pnas.0403218101, 454188, 15199185.
28. Stegmayer G, Milone D, Kamenetzky L, Lopez M, Carrari F. Neural Network Model for Integration and Visualization of Introgressed Genome and Metabolite Data. IEEE International Joint Conference on Neural Networks 2009, 1:2983-2989.
29. Kaever A, Lingner T, Feussner K, Gobel C, Feussner I, Meinicke P. MarVis: a tool for clustering and visualization of metabolic biomarkers. BMC Bioinformatics 2009, 10:92-100. 2666665, 19302701.
30. Yano M, Kanaya S, Altaf-Ul-Amin M, Kurokawa K, Hirai M, Saito K. Integrated Data Mining of Transcriptome and Metabolome Based on BL-SOM. Journal of Computer Aided Chemistry 2006, 7:125-136.
31. Junker B, Klukas C, Schreiber F. VANTED: A system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 2006, 7:109-121. 10.1186/1471-2105-7-109, 1413562, 16519817.
32. Eshed Y, Zamir D. An introgression line population of Lycopersicon pennellii in the cultivated tomato enables the identification and fine mapping of yield associated QTL. Genetics 1995, 141:1147-1162. 1206837, 8582620.
33. SOM Toolbox. , http://www.cis.hut.fi/projects/somtoolbox/
34. Arabidopsis annotations. , http://www.arabidopsis.org
35. Solanaceae Unigene annotations. , http://www.sgn.cornell.edu
36. KEGG Kyoto Encyclopedia of Genes and Genomes. KEGG., http://www.genome.jp/kegg/
37. Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie A. Gas chromatography mass spectrometry-based metabolite profiling in plants. Nature Protocols 2006, 1:387-396. 10.1038/nprot.2006.59, 17406261.
38. Causton C, Quackenbush J, Brazma A. Microarray Gene Expression Data Analysis: A Beginner's Guide 2003, Blackwell Publishers.
39. Haykin S. Neural Networks: A Comprehensive Foundation 2007, Prentice-Hall, Inc.
40. Ultsch A. Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series in Kohonen Maps. 1999, Elsevier.
41. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 2000, 28:27-30. 10.1093/nar/28.1.27, 102409, 10592173.
42. Handl J, Knowles J, Kell D. Computational cluster validation in post-genomic data analysis. Bioinformatics 2005, 21(15):3201-3212. 10.1093/bioinformatics/bti517, 15914541.