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. 2011 Mar 30:12:86.
doi: 10.1186/1471-2105-12-86.

Joint analysis of transcriptional and post- transcriptional brain tumor data: searching for emergent properties of cellular systems

Raffaele Fronza  1 , Michele Tramonti, William R Atchley, Christine Nardini
Affiliations

Joint analysis of transcriptional and post- transcriptional brain tumor data: searching for emergent properties of cellular systems

Raffaele Fronza et al. BMC Bioinformatics. .

Abstract

Background: Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to date, most computational and algorithmic efforts have been directed at mining data from each of these molecular levels (genomic, transcriptional, etc.) separately. In view of the rapid advances in technology (new generation sequencing, high-throughput proteomics) it is important to address the problem of analyzing these data as a whole, i.e. preserving the emergent properties that appear in the cellular system when all molecular levels are interacting. We analyzed one of the (currently) few datasets that provide both transcriptional and post-transcriptional data of the same samples to investigate the possibility to extract more information, using a joint analysis approach.

Results: We use Factor Analysis coupled with pre-established knowledge as a theoretical base to achieve this goal. Our intention is to identify structures that contain information from both mRNAs and miRNAs, and that can explain the complexity of the data. Despite the small sample available, we can show that this approach permits identification of meaningful structures, in particular two polycistronic miRNA genes related to transcriptional activity and likely to be relevant in the discrimination between gliosarcomas and other brain tumors.

Conclusions: This suggests the need to develop methodologies to simultaneously mine information from different levels of biological organization, rather than linking separate analyses performed in parallel.

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Figures

Figure 1
Figure 1
Organization of miRNA. clusters miR-17-92 and miR-106-363. Structure of the two polycistronic miRNA gene and the relations between miRNAs.
Figure 2
Figure 2
Schematic view of the complex analysis performed jointly on mRNA and miRNAs from the same 12 tumor samples.
See this image and copyright information in PMC

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