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TRNInfer specifications


Unique identifier OMICS_24049
Name TRNInfer
Alternative name Transcriptional Regulatory Networks Infer
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Stability Stable
Maintained Yes




No version available


  • person_outline Luonan Chen
  • person_outline Xiang-Sun Zhang

Publication for Transcriptional Regulatory Networks Infer

TRNInfer citations


Reverse Engineering of Genome wide Gene Regulatory Networks from Gene Expression Data

Curr Genomics
PMCID: 4412962
PMID: 25937810
DOI: 10.2174/1389202915666141110210634

[…] izedthem into one of them. For instance, REVEAL also employs mutual information technique beyond Boolean network, so it can also belong to correlation-based methods. In (Table ), some methods such as TRNInfer [] and Inferelator [] reconstruct the four levels of transcriptional regulatory relationships, while others such as PCA-CMI [] and WGCNA [] generally identify gene regulations without directi […]


Pathway mapping and development of disease specific biomarkers: protein based network biomarkers

J Cell Mol Med
PMCID: 4407592
PMID: 25560835
DOI: 10.1111/jcmm.12447

[…] ational programs were developed to integrate selected genes or proteins into the knowledge-based networks via the combination of genomics, proteomics and bioinformatics, such as GRNInfer , MDCinfer , TRNInfer , Samo , MNAligner , PTG , PRNA , NOA , differential dependency network (DDN) , WGCNA , SurvNet or DiME , each of them has its own advantages and strength on basis of scientific needs and in […]


Refining transcriptional regulatory networks using network evolutionary models and gene histories

PMCID: 2823753
PMID: 20047657
DOI: 10.1186/1748-7188-5-1

[…] ince larger networks generally need more samples to gain inference accuracy comparable to smaller ones. Second, we apply DEI to datasets generated by GeneSim to infer the networks. Since the DEI tool TRNinfer does not accept large datasets (with many time points), here we use smaller datasets than the previous group of experiments with at most 75 time points. For each setup, experiments with diffe […]


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TRNInfer institution(s)
School of Information, Renmin University of China, Beijing, China; Academy of Mathematics and Systems Science, CAS, Beijing, China; Institute of Systems Biology, Shanghai University, Shanghai, China; Osaka Sangyo University, Osaka, Japan; ERATO Aihara Complexity Modelling Project, JST, Tokyo, Japan; Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
TRNInfer funding source(s)
Supported by JSPS under JSPS-NSFC collaboration project, the National Nature Science Foundation of China (NSFC) under grant No. 10701080 and the Ministry of Science and Technology, China, under grant No. 2006CB503905.

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