IRIS Toolbox

Category Cross-Omics>Pathway Analysis/Gene Regulatory Networks/Tools

Abstract IRIS (Inference of Regulatory Interaction Schema) Toolbox is a rapid and efficient tool for the inference of regulatory relations in ‘gene networks’.

The IRIS algorithm uses an iterative approach to map ‘gene expression profile’ values (both steady-state and time-course) into discrete states and a simple probabilistic method to infer the ‘regulatory functions’ of the network.

These ‘interaction rules’ are integrated into a factor graph model.

A topological description of the network and a matrix of gene expression profiles are required as input to the algorithm.

IRIS maps gene expression data onto discrete values and then computes ‘regulatory functions’ as conditional probability tables.

IRIS uses a set of MATLAB functions to analyze GRNs --

1) IRIS allows you to learn the interaction rules within a gene regulatory network (GRN) with a well-defined topology.

2) IRIS uses a ‘factor graph framework’ to infer the gene regulatory networks.

3) IRIS implements a function to compute the ‘steady states’ of the network.

IRIS Toolbox Functions --

The IRIS toolbox provides three (3) different types of functionalities to the end-user:

1) The ‘Model Functions’ which allow you to build both gene regulatory networks and experimental conditions;

2) The ‘Engine Functions’ which allow you to run:

3) The ‘Utility Functions’ which provide a set of tools to load gene expression profile datasets and also allows you to draw a ‘gene regulatory network’ as a direct graph.

Note: The draw gene regulatory network function requires Graphviz (an additional software product) to be installed on the user’s PC.

IRIS Testing files --

In order to perform the experiments validating IRIS, additional files are needed:

1) compute_perm_DKs: this file contains the statements to evaluate IRIS performance (in terms of Kullback-Leibler divergence) when the data matrix is changed into the random way.

2) disc_equal_freq: this function allows you to discretize the dataset passed as an argument into a number of bins, user-defined by using the ‘equal frequency’ approach.

3) disc_equal_width: this function allows to discretize the dataset passed as an argument into a number of bins, user-defined by using the ‘equal width’ approach.

4) disc_global_width: this function allows to discretize the dataset passed as an argument into a number of bins, user-defined by using the ‘global width’ approach.

5) em_map_coli_synth_net: this file contains the set of statements needed to compare the different discretization approaches on the E. coli synthetic network.

6) em_map_yeast_synth_net: this file contains the set of statements needed to compare the different discretization approaches on the S. cerevisiae synthetic network.

7) evalute_emmap_coli: this function computes the Kullback-Leibler divergence between the true distribution of the E. coli synthetic network and the computed one passed as arguments.

8) evalute_emmap_yeast: this function computes the Kullback-Leibler divergence between the true distribution of the S. cerevisiae synthetic network and the computed one passed as an argument.

IRIS documentation --

The manufacturers provide a downloadable ‘User Manual’ and a Reference Paper:

Ceccarelli, M, Morganella, S, and Zoppoli, P (2009); IRIS: a method for reverse engineering of regulatory relations in gene networks; BMC Bioinformatics 2009, 10:444.

System Requirements

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Manufacturer

Manufacturer Web Site IRIS Toolbox

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G6G Abstract Number 20567

G6G Manufacturer Number 104174