Abstract
“-Omics” is a current suffix for numerous types of large-scale biological data generation procedures, which naturally demand the development of novel algorithms for data storage and analysis. With next generation genome sequencing burgeoning, it is pivotal to decipher a coding site on the genome, a gene’s function, and information on transcripts next to the pure availability of sequence information. To explore a genome and downstream molecular processes, we need umpteen results at the various levels of cellular organization by utilizing different experimental designs, data analysis strategies and methodologies. Here comes the need for controlled vocabularies and data integration to annotate, store, and update the flow of experimental data. This chapter explores key methodologies to merge Omics data by semantic data carriers, discusses controlled vocabularies as eXtensible Markup Languages (XML), and provides practical guidance, databases, and software links supporting the integration of Omics data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Caspi, R., Foerster, H., Fulcher, C.A., Kaipa, P., Krummenacker, M., Latendresse, M., Paley, S., Rhee, S.Y., Shearer, A.G., and Tissier, C. (2008) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 36, D623–31.
Srinubabu, G. (2009) Computational systems biology of – Omics data: integration, warehousing and validation. BIT Life Sciences’ 2nd Annual World Summit of Antivirals, July 18–20, 2009, Beijing, China.
Hanuman, T., Raghava, N.M., Siva, P.A., Mrithyunjaya, R.K., Chandra, S.V., Allam, A.R., and Srinubabu, G. (2009) Performance comparative in classification algorithms using real datasets. J Comput Sci Syst Biol 2, 97–100.
Tetsuro, T., Yoshiki M., Keith, P., Naohiko, H., Norio, K., and Yoshiyuki, S. (2007) OmicBrowse: a browser of multidimensional omics annotations. Bioinformatics 23, 524–26.
Avraham, S., Tung, C.W., Ilic, K., Jaiswal, P., Kellogg, E.A., McCouch, S., Pujar, A., Reiser, L., Rhee, S.Y., Sachs, M.M., Schaeffer, M., Stein, L., Stevens, P., Vincent, L., Zapata, F., and Ware, D. (2008) The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations. Nucleic Acids Res 36, D449.
Sidhu, A.S., Dillon, T.S., and Chang, E. (2006) Advances in Protein Ontology Project. Computer-Based Medical Systems CBMS 19th IEEE International Symposium 588–92.
Ashburner M. et al. (2000) Gene ontology: tool for the unification of biology. Nat Genet 25, 25–29.
Satya, S.S., Christopher, T., Amit, S., Cory, H., and William, S. (2005) GLYDE – An expressive XML standard for the representation of glycan structure. Carbohydr Res 18, 2802–7.
Syed, S.H., Benoit, B., Richard, H., Darin, L., Gudmundur, T., and Arek, K. (2009) BioMart – biological queries made easy. BMC Genomics 10, 22.
Vandervalk, B.P., McCarthy, E.L., and Wilkinson, M.D. (2009) Moby and Moby 2: creatures of the deep (web). Brief Bioinform 10, 114–28.
Burgun, A., and Bodenreider, O. (2008) Accessing and integrating data and knowledge for biomedical research. France Yearb Med Inform 91–101.
Akula, S.P., Miriyala, R.N., Thota, H., Rao, A.A., and Srinubabu, G. (2009) Techniques for integrating -omics data. Bioinformation 3, 284–86.
Wei, W., Michael, C. J., Yigal, N., Emmitt, J., David, B., and Hao, L. (2005) Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation. Proc Natl Acad Sci USA 102, 1998–03.
Crispin, R., and Harmen, J.B. (2008) REDUCE: an online tool for inferring cis-regulatory elements and transcriptional module activities from microarray data. Nucleic Acids Res 31, 3487–90.
Bar-Joseph, Z., Gerber, G.K., Lee, T.I., Rinaldi, N.J., Yoo, J.Y., Robert, F., Gordon, D.B., Fraenkel, E., Jaakkola, T.S., Young, R.A., and Gifford, D.K. (2003) Computational discovery of gene modules and regulatory networks. Nat Biotechnol 21, 1337–42.
Scott, A.B., Adam, M.F., Monica, L.M., Gregory, H., Bernhard, P., and Markus, J.H. (2007) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protoc 2, 227–38.
Longabaugh, W.J.R., Eric, H.D., and Hamid, B. (2005) Computational representation of developmental genetic regulatory networks. Dev Biol 283, 1–16.
Ljudmilla, B., Mohammad-Reza, H., Christian, K., Hardy, R., and Falk, S. (2005) Integrating data from biological experiments into metabolic networks with the DBE information system. In Silico Biol 5, 93–102.
Denong, W, and Srinubabu, G. (2008) Insights of new tools in glycomics research. J Proteomics Bioinform 1, 374–78.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Gedela, S. (2011). Integration, Warehousing, and Analysis Strategies of Omics Data. In: Mayer, B. (eds) Bioinformatics for Omics Data. Methods in Molecular Biology, vol 719. Humana Press. https://doi.org/10.1007/978-1-61779-027-0_18
Download citation
DOI: https://doi.org/10.1007/978-1-61779-027-0_18
Published:
Publisher Name: Humana Press
Print ISBN: 978-1-61779-026-3
Online ISBN: 978-1-61779-027-0
eBook Packages: Springer Protocols