Format

Send to

Choose Destination
PLoS One. 2014 Oct 14;9(10):e109569. doi: 10.1371/journal.pone.0109569. eCollection 2014.

Inferring protein modulation from gene expression data using conditional mutual information.

Author information

1
Department of Systems Biology, Columbia University, New York, New York, United States of America; Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America.
2
Department of Systems Biology, Columbia University, New York, New York, United States of America; Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America; Columbia Genome Center, High Throughput Screening facility, Columbia University, New York, New York, United States of America; Department of Biomedical Informatics, Columbia University, New York, New York, United States of America; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America; Institute for Cancer Genetics, Columbia University, New York, New York, United States of America; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, United States of America.

Abstract

Systematic, high-throughput dissection of causal post-translational regulatory dependencies, on a genome wide basis, is still one of the great challenges of biology. Due to its complexity, however, only a handful of computational algorithms have been developed for this task. Here we present CINDy (Conditional Inference of Network Dynamics), a novel algorithm for the genome-wide, context specific inference of regulatory dependencies between signaling protein and transcription factor activity, from gene expression data. The algorithm uses a novel adaptive partitioning methodology to accurately estimate the full Condition Mutual Information (CMI) between a transcription factor and its targets, given the expression of a signaling protein. We show that CMI analysis is optimally suited to dissecting post-translational dependencies. Indeed, when tested against a gold standard dataset of experimentally validated protein-protein interactions in signal transduction networks, CINDy significantly outperforms previous methods, both in terms of sensitivity and precision.

PMID:
25314274
PMCID:
PMC4196905
DOI:
10.1371/journal.pone.0109569
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center