Utilizing RNA-Seq data for cancer network inference

Ying, Cai, Fendler, B., Atwal, G. S. (2012) Utilizing RNA-Seq data for cancer network inference. 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, (GENSIPS). pp. 46-49. ISSN 2150-3001

URL: http://ieeexplore.ieee.org/document/6507723/
DOI: 10.1109/GENSIPS.2012.6507723


An important challenge in cancer systems biology is to uncover the complex network of interactions between genes (tumor suppressor genes and oncogenes) implicated in cancer. Next generation sequencing provides unparalleled ability to probe the expression levels of the entire set of cancer genes and their transcript isoforms. However, there are onerous statistical and computational issues in interpreting high-dimensional sequencing data and inferring the underlying genetic network. In this study, we analyzed RNA-Seq data from lymphoblastoid cell lines derived from a population of 69 human individuals and implemented a probabilistic framework to construct biologically-relevant genetic networks. In particular, we employed a graphical lasso analysis, motivated by considerations of the maximum entropy formalism, to estimate the sparse inverse covariance matrix of RNA-Seq data. Gene ontology, pathway enrichment and protein-protein path length analysis were all carried out to validate the biological context of the predicted network of interacting cancer gene isoforms.

Item Type: Paper
Additional Information: Meeting Abstract
Uncontrolled Keywords: RNA biology computing cancer cellular biophysics data analysis genetics genomics maximum entropy methods molecular biophysics molecular configurations probability proteins sequential estimation statistical analysis tumours RNA-Seq data analysis biologically-relevant genetic networks cancer network inference cancer systems biology complex network interactions computational issues expression levels gene ontology graphical lasso analysis high-dimensional sequencing data human individuals lymphoblastoid cell lines maximum entropy formalism next generation sequencing oncogenes onerous statistical issues probabilistic framework protein-protein path length analysis sparse inverse covariance matrix estimation transcript isoforms tumor suppressor genes RNA-Seq graphical lasso maximum entropy
Subjects: diseases & disorders > cancer
bioinformatics > computational biology
Publication Type > Meeting Abstract
Investigative techniques and equipment > assays > RNA-seq
CSHL Authors:
Communities: CSHL labs > Atwal lab
Depositing User: Matt Covey
Date: 2012
Date Deposited: 30 Jan 2015 15:40
Last Modified: 21 Feb 2018 21:26
URI: https://repository.cshl.edu/id/eprint/31144

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