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Computational Surprisal Analysis Speeds-Up Genomic Characterization of Cancer Processes

Kravchenko-Balasha, Nataly and Simon, Simcha and Levine, R. D. and Remacle, F. and Exman, Iaakov (2014) Computational Surprisal Analysis Speeds-Up Genomic Characterization of Cancer Processes. PLoS ONE, 9 (11). Art. No. e108549. ISSN 1932-6203. PMCID PMC4236016. doi:10.1371/journal.pone.0108549.

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Surprisal analysis is increasingly being applied for the examination of transcription levels in cellular processes, towards revealing inner network structures and predicting response. But to achieve its full potential, surprisal analysis should be integrated into a wider range computational tool. The purposes of this paper are to combine surprisal analysis with other important computation procedures, such as easy manipulation of the analysis results – e.g. to choose desirable result sub-sets for further inspection –, retrieval and comparison with relevant datasets from public databases, and flexible graphical displays for heuristic thinking. The whole set of computation procedures integrated into a single practical tool is what we call Computational Surprisal Analysis. This combined kind of analysis should facilitate significantly quantitative understanding of different cellular processes for researchers, including applications in proteomics and metabolomics. Beyond that, our vision is that Computational Surprisal Analysis has the potential to reach the status of a routine method of analysis for practitioners. The resolving power of Computational Surprisal Analysis is here demonstrated by its application to a variety of cellular cancer process transcription datasets, ours and from the literature. The results provide a compact biological picture of the thermodynamic significance of the leading gene expression phenotypes in every stage of the disease. For each transcript we characterize both its inherent steady state weight, its correlation with the other transcripts and its variation due to the disease. We present a dedicated website to facilitate the analysis for researchers and practitioners.

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Additional Information:© 2014 Kravchenko-Balasha et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: February 18, 2014; Accepted: August 31, 2014; Published: November 18, 2014. This work was supported by an EMBO postdoctoral fellowship to N.K.B. and European Commission FP7 Future and Emerging Technologies–Open Project BAMBI 618024 (to FR and RDL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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European Molecular Biology Organization (EMBO)UNSPECIFIED
European Commission FP7 Future and Emerging Technologies–Open Project BAMBI618024
Issue or Number:11
PubMed Central ID:PMC4236016
Record Number:CaltechAUTHORS:20150129-160422213
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Official Citation:Kravchenko-Balasha N, Simon S, Levine RD, Remacle F, Exman I (2014) Computational Surprisal Analysis Speeds-Up Genomic Characterization of Cancer Processes. PLoS ONE 9(11): e108549. doi:10.1371/journal.pone.0108549
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:54232
Deposited By: Jason Perez
Deposited On:30 Jan 2015 02:27
Last Modified:10 Nov 2021 20:30

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