Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2011

Open Access Meeting abstract

Comparative studies of high-throughput biological graphs

Jonathan Reyles1* and Charles Phillips2

Author affiliations

1 Graduate School of Genome Science & Technology, UT-ORNL, Oak Ridge, TN, 37830, USA

2 Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Knoxville, TN , 37996, USA

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2011, 12(Suppl 7):A12  doi:10.1186/1471-2105-12-S7-A12

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/12/S7/A12


Published:5 August 2011

© 2011 Reyles and Phillips; licensee BioMed Central Ltd.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background

The exponential growth of biological data has given rise to new and difficult challenges. Because large data is often dealt with, it is inefficient to infer from each individual characteristics of a given dataset. Bioinformaticists are developing quantitative techniques to analyze and interpret key data properties. Graph algorithms can provide powerful and intuitive insight on such properties [1]. Using this approach, we collect biological data from transcriptomic and protein-protein interaction (PPI) sources. These data can be represented as a correlation matrix, where the rows are the vertices and the columns are the edges. We will analyze these graphs, and describe their differing structural characteristics.

Materials and methods

We are using a high throughput method for graphical exploration of genomic and proteomic data. Experimental datasets are extracted from the public databases Biomart and Gene Expression Omnibus (GEO) [2,3]. R [4] and MATLAB are used to develop algorithms that compute and compare various structural characteristics. We specifically developed an in-house script used to output essential histograms and unweighted/weighted edges. We are currently developing protocols to analyze the comparison of transcriptomes and PPI sources.

Acknowledgements

We express gratitude towards Jay Snoddy and Michael Langston for the ideas that led us to pursue this bioinformatics investigation.

References

  1. Tor-Kristian Jenssen AL, Komorowski J, Hovig E: A literature network of human genes for high-throughput analysis of gene expression.

    Nature Genetics 2001, 28:21-28. PubMed Abstract | Publisher Full Text OpenURL

  2. Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G, Kasprzyk A: BioMart – biological queries made easy.

    BMC Genomics 2009, 10:22. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  3. Barrett TD, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Edgar R: NCBI GEO: archive for high-throughput functional genomic data.

    Nucleic Acids Res 2009, 37:D5-15. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  4. Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria;