This Programme focuses on the development, implementation, and application of bioinformatics tools for the analysis of large-scale genomic datasets. It aims to facilitate the processing, integration, and interpretation of high-throughput sequencing data, including whole-genome, transcriptome, and epigenome analyses. The Programme encompasses computational approaches for variant discovery, gene expression profiling, network biology, and multi-omics data integration.
Web page: Not specified
Funding details:Related items
Projects: Sex-specific differences in long-term gamma and simGCRsim-associated alterations in deferential gene expression in the heart tissue, Functional Genomics of Vine, Molecular Profiling of Cancer Metastases, Study of the molecular mechanisms of familial Mediterranean fever using genetic engineering and functional genomics, Mental disorders and aging brain, Omics-Based Insights into Human Long-Term Exposure to Environmental Metals, Biological pathway activity analysis, ML approaches for omic data analysis, Tools for Telomere Biology, Armenian Wine Genome Program, Molecular characterization of cancers with long-read RNA sequencing, Low dose radiation risks: present research and future perspectives
Institutions: Institute of Molecular Biology NAS RA, Armenian Bioinformatics Institute

Projects: Functional Genomics of Vine, Molecular Profiling of Cancer Metastases, Biological pathway activity analysis, Mental disorders and aging brain, ML approaches for omic data analysis
Institutions: Armenian Bioinformatics Institute, Interdisciplinary Center for Bioinformatics (IZBI)

Projects: Tools for Telomere Biology
Institutions: Institute of Molecular Biology NAS RA

Projects: Biological pathway activity analysis, Sex-specific differences in long-term gamma and simGCRsim-associated alterations in deferential gene expression in the heart tissue
Institutions: Institute of Molecular Biology NAS RA

Expertise: Bioinformatics
Tools: Cytoscape, Genomics, Microarray analysis, Python, R, Single Cell analysis, Transcriptomics
Siras Hakobyan is currently a Junior Researcher and PhD student in the Research Group of Bioinformatics at the Institute of Molecular Biology NAS RA in Yerevan, Armenia. He received his Master's and Bachelor's degrees in Bioinformatics from Yerevan State University, completing his studies in 2018 and 2016. His PhD project focuses on pathway-centered analysis of high-throughput omics data, particularly examining pathway activity states in cancers based on pathway topology, gene expression, and ...
Programme: Bioinformatics Tools for Analysis of Big Genomic Data
Public web page: Not specified
Organisms: Not specified
This project focuses on developing and applying machine learning (ML) methodologies to analyze complex omic datasets, including genomics, transcriptomics, proteomics, and metabolomics. By leveraging advanced ML techniques, the project aims to uncover novel biological insights, identify biomarkers, and enhance our understanding of molecular mechanisms underlying various diseases. The initiative is a collaborative effort maintained by the Institute of Molecular Biology NAS RA (IMB), ...
Programme: Bioinformatics Tools for Analysis of Big Genomic Data
Public web page: Not specified
Organisms: Not specified
Programme: Bioinformatics Tools for Analysis of Big Genomic Data
Public web page: Not specified
Organisms: Not specified
The Pathway Signal Flow (PSF) toolkit is an R package designed for pathway editing and signal flow analysis. It enables users to perform PSF analysis on functional -omics data, facilitating the study of signaling pathways in various biological contexts.
Key Features:
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Pathway Editing: Allows customization and editing of biological pathways to suit specific research needs.
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Signal Flow Calculation: Computes signal flow within pathways based on gene expression data, aiding in the ...
Creators: Arsen Arakelyan, Siras Hakobyan
Submitter: Arsen Arakelyan
Model type: Not specified
Model format: Not specified
Environment: Not specified
Organism: Not specified
Investigations: No Investigations
Studies: No Studies
Assays: No Assays
KEGG pathway database is a collection of manually drawn pathway maps representing current knowledge on molecular interaction and reaction networks, accompanied with KGML (KEGG pathway xml format) files for automatic computational analyses and modeling of metabolic and signaling networks. In a KGML file the pathway is represented as a graph object with entry elements (gene products, compounds, pathways) as its nodes, and relations between elements as edges. However, in most cases there is a lack ...
Creator: Arsen Arakelyan
Submitter: Arsen Arakelyan
Model type: Not specified
Model format: Not specified
Environment: Matlab
Organism: Not specified
Investigations: No Investigations
Studies: No Studies
Assays: No Assays
Abstract (Expand)
Authors: A. Arakelyan, S. Avagyan, A. Kurnosov, T. Mkrtchyan, G. Mkrtchyan, R. Zakharyan, K. R. Mayilyan, H. Binder
Date Published: 17th Feb 2024
Publication Type: Journal
PubMed ID: 38368435
Citation: Schizophrenia (Heidelb). 2024 Feb 17;10(1):19. doi: 10.1038/s41537-024-00443-7.
Abstract (Expand)
Authors: S. Hakobyan, A. Stepanyan, L. Nersisyan, H. Binder, A. Arakelyan
Date Published: 8th Sep 2023
Publication Type: Journal
PubMed ID: 37680201
Citation: Front Genet. 2023 Aug 23;14:1264656. doi: 10.3389/fgene.2023.1264656. eCollection 2023.
Abstract (Expand)
Authors: Maria Nikoghosyan, Henry Loeffler-Wirth, Suren Davidavyan, Hans Binder, Arsen Arakelyan
Date Published: 27th Dec 2021
Publication Type: Journal
DOI: 10.3390/biomedinformatics2010004
Citation: BioMedInformatics,2(1):62-76
Abstract (Expand)
Authors: L. Nersisyan, A. Simonyan, H. Binder, A. Arakelyan
Date Published: 26th Apr 2021
Publication Type: Journal
PubMed ID: 33897770
Citation: Front Genet. 2021 Apr 7;12:662464. doi: 10.3389/fgene.2021.662464. eCollection 2021.
Abstract (Expand)
Authors: M. Nikoghosyan, M. Schmidt, K. Margaryan, H. Loeffler-Wirth, A. Arakelyan, H. Binder
Date Published: 17th Jul 2020
Publication Type: Journal
PubMed ID: 32709105
Citation: Genes (Basel). 2020 Jul 17;11(7):817. doi: 10.3390/genes11070817.