TRNinfer

Inferring Transcriptional Regulatory Networks from

High-throughput Data

[Version 1.0]

June 25, 2007

AUTHORS
　

Rui-Sheng Wang (wangrsh@amss.ac.cn)

Yong Wang (ywang@ctex.org)

Xiang-Sun Zhang (zxs@amt.ac.cn)

Luonan Chen (chen@elec.osaka-sandai.ac.jp)

METHOD
　

TRNinfer aims to infer the direct relations between transcription factors (TF) and target genes, i.e. transcriptional regulatory network (TRN). The data is from high-throughput data such as gene expression data and protein-protein interaction from which transcription factor activities can be estimated. The inferring process is formulated as a general linear programming framework which is fast and has globally optimal solutions.

PROCEDURE
　

• Step 1: Download TRNinfer.rar from the web site.

• Step 2: Unzip it and copy all files into the same file folder.

• Step 3: Format input data into desired data file (See input.txt), which also includes two parameters:

  • Lambda: This parameter is used in the inferring algorithm to adjust the sparsity of the inferred TRN.

  • RealorSim: This parameter indicates the type of input data. 0 denote simulated data and 1 denotes real data.

• Step 4: Computing by click the executable file TRNinfer.exe when the data file and parameters are ready.

• Step 5: Checking the result. After Step 4, a file named TRN.dat will be generated which contains the inferred connection matrix between TFs and target genes.

REFERENCE
　

Rui-Sheng Wang, Yong Wang, Xiang-Sun Zhang, and Luonan Chen. Inferring transcriptional regulatory networks from high-throughput data. In submission.

SOFTWARE
　

This is a beta version of the program for preliminary testing. The program is still under development.

TRNinfer.rar