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EpiTracer specifications

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Unique identifier OMICS_17772
Name EpiTracer
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data For computing epicenters: a tab delimited file with 3 columns (source, target and weight of edge), “disease_network”, “compute_cshan?” and “percentile_threshold”. For extracting immediate influence zone and highlighting dysregulated genes: network from which to extract influence zone, nodes around which to extract influence zone, node weights, fold change, control-specific highest activity network, disease-specific highest activity network, upstream limit, downstream limit and fold change limit.
Output data For extracting immediate influence zone and highlighting dysregulated genes: immediate influence zone (edges) around each epicenter, node information of immediate influence zone for each epicenter, the table similar to table 1 in the paper and summary statistics of each influence zone.
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Nagasuma Chandra <>

Publication for EpiTracer

EpiTracer citation

library_books

Unbiased Identification of Blood based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks

2016
PMCID: 5233809
PMID: 28065665
DOI: 10.1016/j.ebiom.2016.12.009

[…] a change at that node can transmit across the network, leading to the identification of the most influential nodes, also known as epicenters. the ripple centrality measure was computed using the epitracer algorithm (), which measures the outward reachability of a given node, a property of its connections as well as its condition-specific node weight. of the 276 upregulated nodes, 153 nodes […]


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EpiTracer institution(s)
IISc Mathematics Initiative, Indian Institute of Science, Bangalore, India; Department of Biochemistry, Indian Institute of Science, Bangalore, India

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