IgRENE - Suite

Integrating drug ontologies and conditional regulatory networks to optimize the modulation of a target gene's expression

we use Systems Bioinformatics approaches to develop next generation drug discovery tools

About IGRENE

IGRENE is a web-based platform that integrates Gene Regulatory Networks (GRNs) and drug-based regulatory information using a criteria-based computational framework. It employs a three-tier systemic approach to identifying drugs and their appropriate regulatory network paths towards suppressing or activating the expression of specific genes of interest, enabling the prioritization of drugs-compounds for experimental validation and therapeutic application. The tools are listed below.

complex networks is the key for perfection

we use Systems Bioinformatics approaches to develop next generation tools

The Pipeline

The platform leverages the SIGNOR 3.0 database as its primary source for gene regulatory information, a well curated repository of causal relationships between biological entities, including proteins, genes, and small molecules. Its rich dataset offers mechanistic regulatory information, critical for network-based drug prioritization, enabling precise mapping of the upstream and downstream interactions that influence target gene expression. Drug-related data derive from three diverse repositories, encompassing five drug ontologies, each contributing unique strengths, as follows.

The DRUGBANK repository, a comprehensive resource of chemical, pharmacological, and molecular data, that detailed information about drug structures, mechanisms of action, and target proteins, allowing the platform to incorporate precise drug-target interactions. The Drug Gene Budger (DGB) repository which provides gene-to-drug associations from L1000, CREEDS and CMAP ontologies, through data-driven models, offering valuable insights into potential regulatory effects even for drugs with limited experimental evidence. The platform further incorporates the SIGNOR drug information to integrate drug-regulatory effects directly into the GRN framework.

Bioinformatics Tools

we use state of the art approaches for developing

Department of Bioinformatics

The Cyprus Institute of Neurology & Genetics (CING)

The mission of the Department is to function as a hub of excellence in the areas of applied bioinformatics to early diagnosis, effective prognosis and drug discovery contributing to the concepts of Preventive, Personalized and Precise Medicine. This is accomplished through state-of-the-art bioinformatics research, advanced education in postgraduate level and a bundle of bioinformatics pipelines that are offered either as services or as resources for collaboration. A suite of public bioinformatics tools can be found at bioinformatics.cing.ac.cy

Drug Repurposing-Discovery Tools

We develop drug repurposing tools against known or unknown pathogens, especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine.

We also provide pipelines that involve: the selection of proper gene expression datasets, data preprocessing and statistical analysis, selection of the most important genes related to the disease, analysis of biological pathways, investigation of related molecular mechanisms, identification of fibrosis-related microRNAs, drug repurposing, structural and literature-based analysis of the repurposed drugs.

AI-Driven Applications

The Department is dedicated to advancing the application of artificial intelligence across scientific and medical domains. We focus on developing and integrating AI-driven methodologies to accelerate discovery, improve data interpretation, and enable precision in research and healthcare. From machine learning models that uncover patterns in genomic and clinical data, to generative AI systems used in drug design, diagnostics, and biomedical imaging, our interdisciplinary teams harness the power of AI to address complex biological questions and transform the future of medicine. Through collaboration and innovation, we aim to push the boundaries of what AI can achieve in science and society.

Pathway Network Analysis

We develop tools that facilitate the construction of complementary pathway-to-pathway networks and subnetworks of them, based on the rich information available in KEGG or Reactome and Wiki Pathways repositories. We further extend the results of differential expression analysis of proteins and genes, to the construction of protein-to-protein and gene-to-gene co-expression networks and to identification of significant pathways related to a specific experiment under study. These tools are simple yet informative tools towards identifying connected groups of pathways that are significantly related to specific diseases.

Protein-Gene Expression Statistics

We develop post-processing workflows for the analysis of protein and gene expression datasets, using network-based bioinformatics approaches, that facilitate differential expression analysis and co-expression network analysis as well as pathway and post-pathway analysis. These tools are useful towards the understanding and exploitation of protein and gene expression datasets, especially for those that do not have the expertise and local resources to replicate specific analyses in the context of collaborative and scientific data exchanging.

Next Generation Sequencing (NGS)

We perform state of the art Next Generation Sequencing (NGS) analysis using cutting edge tools and methods, even at a single-cell level and cell–cell interactions.Our NGS pipelines support: read quality assessment, mapping reads, preprocessing duplicate reads, variant calling for SNP and indel variants using the GATK Haplotype Caller. Annotation is performed by: (1) Variant Effect Predictor (VEP) from the Ensemble tools and (2) GEMINI (GEnome MINIng). We further develop tools for selecting variants of unknown significance through network-based gene-association significantly, improving risk prediction for disease-control cohorts.

Multi-Omics Data Integration

The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. In the CING Bioinformatics Department we have developed methodologies for the integration of such multisource data. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.

High Processing Units

we use parallel processing environments to optimize data handling

Available Data Sources

Click a button below to download one of the data files.

SIGNOR 3.0 Gene-Gene Regulatory Network

Tab Separated format (TSV)

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SIGNOR 3.0 Drug-Gene Regulatory Network

Tab Separated format (TSV)

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Promoting Networking

focusing on new cross collaborations and new research ideas

Integrating drug ontologies and conditional regulatory networks to optimize the modulation of a target gene's expression

About IGRENE

complex networks is the key for perfection

The Pipeline

Bioinformatics Tools

Department of Bioinformatics

The Cyprus Institute of Neurology & Genetics (CING)

Drug Repurposing-Discovery Tools

AI-Driven Applications

Pathway Network Analysis

Protein-Gene Expression Statistics

Next Generation Sequencing (NGS)

Multi-Omics Data Integration

High Processing Units

Available Data Sources

SIGNOR 3.0 Gene-Gene Regulatory Network

SIGNOR 3.0 Drug-Gene Regulatory Network

Promoting Networking

Department of Bioinformatics

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