Genomic, proteomic, and metabolomic data integration strategies

Biomarker Insights

Volumn 10, Issue , 2015, Pages 1-6

  Wanichthanarak, Kwanjeera ^a   Fahrmann, Johannes F ^a   Grapov, Dmitry ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b CDS Creative Data Solutions ^*  (United States)

Author keywords

Bioinformatics; Data analysis; Data integration; Genomics; Metabolomics; Networks; Omics; Proteomics

Indexed keywords

ARTICLE; CORRELATIONAL STUDY; DATA ANALYSIS; DATA INTEGRATION; GENOMICS; INFORMATION PROCESSING; METABOLOMICS; ONTOLOGY; PROTEOMICS; SIGNAL TRANSDUCTION;

EID: 84941365835     PISSN: None     EISSN: 11772719     Source Type: Journal    
DOI: 10.4137/BMI.S29511     Document Type: Article

References (33)

1. Gracie S, Pennell C, Ekman-Ordeberg G, et al; PREBIC “-Omics” Research Group. An integrated systems biology approach to the study of preterm birth using “-omic” technology-a guideline for research. BMC Pregnancy Childbirth. 2011;11:71.
2. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet. 2003;33(suppl):245-54.
3. Fabian MR, Sonenberg N, Filipowicz W. Regulation of mRNA translation and stability by microRNAs. Annu Rev Biochem. 2010;79:351-79.
4. Wang YC, Peterson SE, Loring JF. Protein post-translational modifications and regulation of pluripotency in human stem cells. Cell Res. 2014;24(2):143-60.
5. Friedrich N. Metabolomics in diabetes research. J Endocrinol. 2012;215(1):29-42.
6. Spratlin JL, Serkova NJ, Eckhardt SG. Clinical applications of metabolomics in oncology: a review. Clin Cancer Res. 2009;15(2):431-40.
7. Fahrmann J, Grapov D, Yang J, et al. Systemic alterations in the metabolome of diabetic nod mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceridemia. Am J Physiol Endocrinol Metab. 2015;308(11):E978-89.
8. Wikoff WR, Grapov D, Fahrmann JF, et al. Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma. Cancer Prev Res (Phila). 2015;8(5):410-8.
9. Grapov D, Wanichthanarak K, Fiehn O. MetaMapR: pathway independent metabolomic network analysis incorporating unknowns. Bioinformatics. 2015; 31(16):2757-60.
10. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50.
11. Kamburov A, Cavill R, Ebbels TM, Herwig R, Keun HC. Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA. Bioinformatics. 2011;27(20):2917-8.
12. Sun H, Wang H, Zhu R, et al. iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis. Bioinformatics. 2014;30(5):737-9.
13. Xia J, Sinelnikov IV, Han B, Wishart DS. MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res. 2015;43(W1):W251-7.
14. Sung J, Wang Y, Chandrasekaran S, Witten DM, Price ND. Molecular signatures from omics data: from chaos to consensus. Biotechnol J. 2012;7(8): 946-57.
15. Gosline SJ, Oh C, Fraenkel E. SAMNetWeb: identifying condition-specific networks linking signaling and transcription. Bioinformatics. 2015;31(7):1124-6.
16. Wachter A, Beissbarth T. pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge. Bioinformatics. 2015. doi:10.1093/bioinformatics/btv323
17. Karnovsky A, Weymouth T, Hull T, et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics. 2012;28(3):373-80.
18. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-504.
19. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40(Database issue):D109-114.
20. Bolton EE, Chen J, Kim S, et al. PubChem3D: a new resource for scientists. J Cheminform. 2011;3(1):32.
21. Grapov D, Fahrmann J, Hwang J, et al. Diabetes associated metabolomic perturbations in NOD mice. Metabolomics. 2015;11(2):425-37.
22. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
23. González I, Lé Cao K, Déjean S. mixOmics: Omics Data Integration Project. 2011. http://www.mixomics.org
24. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.
25. Fukushima A. DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene. 2013;518(1):209-14.
26. Wanichthanarak K. Grinn: Graph Database and R Package for Omics Integration. Available at http://github.com/kwanjeera/grinn
27. Neo Technology Inc. Neo4j: The World’s Leading Graph Database. 2014. http://neo4j.com/
28. Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008;9(3):432-41.
29. Castelo R, Roverato A. Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J Comput Biol. 2009;16(2):213-27.
30. Zhao T, Liu H, Roeder K, Lafferty J, Wasserman L. The huge Package for High-dimensional Undirected Graph Estimation in R. J Mach Learn Res. 2012;13:1059-62.
31. Mukherjee S, Speed TP. Network inference using informative priors. Proc Natl Acad Sci U S A. 2008;105(38):14313-8.
32. Wang J, Zuo Y, Man YG, et al. Pathway and network approaches for identification of cancer signature markers from omics data. J Cancer. 2015;6(1):54-65.
33. DeviumWeb: Dynamic Multivariate Data Analysis and Visualization Platform [Computer Program]; 2014. http://github.com/dgrapov/DeviumWeb