GeNOSA: Inferring and Experimentally Supporting Quantitative Gene Regulatory Networks in Prokaryotes
This package included several tools to perform reconstruction of gene regulatory networks from microarray data and connectivity information.
- gene2CS.sh: this script used to extract the conectivity matrix [A] from a gene list and a regulation list.
- raw2E.sh: this script used to extract the matrix [E] from a gene list and the raw microarray data.
- raw2CS.sh: this script used to extract the connectivity matrix [A] from the domain knowledge in the csv format.
- raw2TFA.sh: this script generated the initial matrix [TFA]
- OptNCA: this program used to optimize the input to the quantitative results.
- OutputCys.m: this script was written in MATLAB and used to output essential data to generate graphical presentations.
Usage of parameters
Optimal NCA for GRNs v1.2.13
Usage: OptNCA [options]
-h: Display this help.
[Input data parameters]
-P: Pass symbols.
-a: Maximum value of each gene. (Default: 10)
-b: Minimum value of each gene. (Default: -10)
-c: Input file of control strenth. (Default: CS.txt)
-C: Compare to input file of control strenth. (Default: CS0.txt)
-e: Input file of expression profiles. (Default: E.txt)
-t: Input file of transcription factors activities. (Default: TFA.txt)
-T: Compare to input file of transcription factors activities. (Default: TFA0.txt)
-F: Fitness function for optimization. (Default: 0)
0 : Least Square Error
1 : Root Mean Square Error
2 : Model Error
-s: The standard deviation of perturbation copies. (Default: 0.5)
-m: Perturbation handler. (Default: 0)
0 : None
1 : Read
2 : Export
-n: The number of perturbation copies. (Default: 20, Maximum: 50)
-N: Using pre-defined perurbation set. (Default: E%02d.txt)
-x: Optimization stretegy. (Default: 1)
1 : optimize TFA only
2 : optimize TFA and CS (no constraints)
3 : optimize TFA and CS (with loose constraints)
4 : optimzie TFA and CS (with tight constraints)
-v: Message verbose level from simple to verbose: 0->9 (Default: 0)
-M: Post process for analysis. (Default: 0)
0 : None
1 : LSE
2 : CS difference
3 : TFA standard deviation
4 : Overall mean from data
-I: Input pattern. (Default: IN%02d.txt)
-i: OSA initial temperature. (Default: 1000)
-r: OSA cooling rate. (Default: 0.999)
-u: OSA radius. (Default: 0.05)
-S: Steps to run. (Default: 1000)
- Escherichia coli
- Saccharomyces cerevisiae
- Arabidopsis thaliana
- Homo sapien
- Dose-responses of CRP-related regulatory genes
- Time-course profiles of isoleucine starvation (Accession: GSE11087)
- Y.-H. Chen, C.-D. Yang, C.-P. Tseng, H.-D. Huang, and S.-Y. Ho*, "GeNOSA: inferring and experimentally supporting quantitative gene regulatory in prokaryotes," Bioinformatics, 31 (13), 2151-2158
- Y.-H. Chen and S.-Y. Ho, "GRNet: an efficient and robust evolutionary method for reconstructing gene regulatory networks," 18th International Conference on Genome Informatics, Biopolis, Singapore, Dec 3-5, 2007.
EMail: Yi-Hsiung Chen