Arsen Arakelyan

Principal Investigator: Prof. Hans Binder

University: University of Leipzig

Research Group: Kristina Margaryan, Maria Nikoghosyan, Tomas Konecny, Anush Baloyan, Hripsime Gasoyan, Emma Hovhannisyan, Levon Galstyan, Duan Shengchang

Duration: 2023-2027

Co-implementing and hosting partner: Armenian Bioinformatics Institute (ABI) and Institute of Molecular Biology

Project Importance The cultivated grape (Vitis vinifera) has become the world’s leading fruit crop. Grape is unique not only because it is ...

This project focuses on studies around brain disorders and ageing.

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), ...

Together with the scientists at Agenus, a US-based biotech company specializing in innovative forms of cancer treatment, including the discovery and development of immuno-oncology therapies, ABI has formed a team of students and researchers to study cancer metastases and identify potential biomarkers and targets for cancer immunotherapies. This collaboration is also part of our long-term vision of supporting biotech developments in Armenia. The team discovers transcriptomics heterogeneity of Liver ...

DigitalLife aims at bringing together complementary Armenian and German competence of the project partners in three major areas: omics bioinformatics for health with single-cell omics resolution, the establishment of infrastructure for genomic data collection, analysis, and sharing, and the Research School offering a qualification program for young scientists. The research tasks include the development of pathway analysis and machine learning methods and software for single-cell omic data analysis, ...

The dataset contains raw and intermediated files, and scripts required to reproduce the results associated with the manuscript "Assigning transcriptomic subtypes to CLL samples using nanopore RNA-sequencing and self-organizing maps". Here, we demonstrate that integrating publicly available short-read data with in-house generated ONT data, along with the application of machine learning approaches, enables the characterization of the CLL transcriptome landscape, the identification of clinically ...

This is a supplementary dataset with raw data, scripts, and complete analysis results for the paper "Supervised projection of high-dimensional genome-wide expression on SOM transcriptome landscapes".

The archive contains three folders:

1. "Simdata" folder contains data, scripts, and results of performance evaluation of extension SOM and supervised SOM with simulated data.

2. "IBD" - folder contains data, scripts, and results of analysis of Inflammatory bowel disease datasets.

3. "BC" - folder ...

This is the submission accompanying the "Telomere Maintenance Pathways in Lower-Grade Gliomas: Insights from Genetic Subtypes and Telomere Length Dynamics" paper.

The dataset contains the TCGA and CGGA LGG

The dataset is organized as follows:

Folder "CGGA_LGG_IDH_subtype_mean_vis" - The CGGAL GG data IDH subtype pathway visualization and CSV data. Folder "Long_Short_telomeres_path_vis" - The TCGA LGG data ALT and TEL pathway visualization for Long and Short telomeres mean values. Folder ...

Sequencing raw data.

This dataset provides data necessary to replicate the results of the time-perturbation analysis of gene expression in health and mental disorders. The corresponding code is available in the GitHub repository under the https://github.com/susieavagyan/BrainExp-TemporalDynamics link.

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:

• Pathway Editing: Allows customization and editing of biological pathways to suit specific research needs.

• Signal Flow Calculation: Computes signal flow within pathways based on gene expression data, aiding in the ...

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 ...

The “Low dose radiation risks: present research and future perspectives” Advanced Research Workshop is a NATO (North Atlantic Treaty Organization)-sponsored workshop organized by the McMaster University (Canada) and Institute of Molecular Biology NAS RA (National Academy of Sciences of Republic of Armenia) which offers a unique opportunity to learn about new discoveries and exchange experience with professionals in the field of radioecology, radiobiology, country-specific regulatory agencies, ...

Patient-specific induced pluripotent stem cells (iPSCs) are reprogrammed somatic cells derived from individual patients through the expression of key transcription factors (e.g., OCT4, SOX2, KLF4, and c-MYC). These cells exhibit pluripotency, allowing them to differentiate into various cell types and serve as in vitro models for studying disease mechanisms, drug screening, and regenerative medicine.

Archive contains presentation slides and recorded talks of the “Low dose radiation risks: present research and future perspectives” Advanced Research Workshop.