Maria Nikoghosyan

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About Maria Nikoghosyan:

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SEEK ID: https://armlifebank.am/people/22

Location: Armenia

ORCID: https://orcid.org/0000-0002-9598-6524

Joined: 25th Feb 2025

Expertise: Not specified

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Plant Genomics & Bioinformatics

The Plant Genomics Programme focuses on the comprehensive characterization of plant genetic resources, aiming to understand the factors that shape genetic variation across plant genomes and drive phenotypic diversity in traits of adaptive and economic importance. The research explores the genetic architecture of plant adaptation to changing climatic conditions and investigates the genomic determinants of genetic diversity. A key objective is the integration of multi-omics data into innovative ...

Projects: Functional Genomics of Vine, Armenian Wine Genome Program

Web page: Not specified

Bioinformatics Tools for Analysis of Big Genomic Data

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.

Projects: Biological pathway activity analysis, ML approaches for omic data analysis, Tools for Telomere Biology

Web page: Not specified

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Functional Genomics of Vine

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 ...

Programme: Plant Genomics & Bioinformatics

Public web page: https://www.fast.foundation/en/program/847/2022/new_tab/6586/6677

Organisms: Grapevine

ML approaches for omic data analysis

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

Advanced Institutions list for this Person with search and filtering

Armenian Bioinformatics Institute

Country: Armenia

City: Yerevan

Web page: https://www.abi.am

Institute of Molecular Biology NAS RA

Country: Armenia

City: Yerevan

Web page: https://molbiol.sci.am

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Assigning Transcriptomic Subtypes to Chronic Lymphocytic Leukemia Samples Using Nanopore RNA-Sequencing and Self-Organizing Maps.

Molecular characterization of cancers with long-read RNA sequencing

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Background/Objectives: Massively parallel sequencing technologies have advanced chronic lymphocytic leukemia (CLL) diagnostics and precision oncology. Illumina platforms, while offering robust performance, … require substantial infrastructure investment and a large number of samples for cost-efficiency. Conversely, third-generation long-read nanopore sequencing from Oxford Nanopore Technologies (ONT) can significantly reduce sequencing costs, making it a valuable tool in resource-limited settings. However, nanopore sequencing faces challenges with lower accuracy and throughput than Illumina platforms, necessitating additional computational strategies. In this paper, 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 relevant molecular subtypes, and the assignment of these subtypes to nanopore-sequenced samples. Methods: Public Illumina RNA sequencing data for 608 CLL samples were obtained from the CLL-Map Portal. CLL transcriptome analysis, gene module identification, and transcriptomic subtype classification were performed using the oposSOM R package for high-dimensional data visualization with self-organizing maps. Eight CLL patients were recruited from the Hematology Center After Prof. R. Yeolyan (Yerevan, Armenia). Sequencing libraries were prepared from blood total RNA using the PCR-cDNA sequencing-barcoding kit (SQK-PCB109) following the manufacturer's protocol and sequenced on an R9.4.1 flow cell for 24-48 h. Raw reads were converted to TPM values. These data were projected into the SOMs space using the supervised SOMs portrayal (supSOM) approach to predict the SOMs portrait of new samples using support vector machine regression. Results: The CLL transcriptomic landscape reveals disruptions in gene modules (spots) associated with T cell cytotoxicity, B and T cell activation, inflammation, cell cycle, DNA repair, proliferation, and splicing. A specific gene module contained genes associated with poor prognosis in CLL. Accordingly, CLL samples were classified into T-cell cytotoxic, immune, proliferative, splicing, and three mixed types: proliferative-immune, proliferative-splicing, and proliferative-immune-splicing. These transcriptomic subtypes were associated with survival orthogonal to gender and mutation status. Using supervised machine learning approaches, transcriptomic subtypes were assigned to patient samples sequenced with nanopore sequencing. Conclusions: This study demonstrates that the CLL transcriptome landscape can be parsed into functional modules, revealing distinct molecular subtypes based on proliferative and immune activity, with important implications for prognosis and treatment that are orthogonal to other molecular classifications. Additionally, the integration of nanopore sequencing with public datasets and machine learning offers a cost-effective approach to molecular subtyping and prognostic prediction, facilitating more accessible and personalized CLL care.

Authors: A. Arakelyan, T. Sirunyan, G. Khachatryan, S. Hakobyan, A. Minasyan, M. Nikoghosyan, M. Hakobyan, A. Chavushyan, G. Martirosyan, Y. Hakobyan, H. Binder

Date Published: 13th Mar 2025

Publication Type: Journal

PubMed ID: 40149301

Citation: Cancers (Basel). 2025 Mar 13;17(6):964. doi: 10.3390/cancers17060964.

Created: 9th May 2025 at 06:24, Last updated: 9th May 2025 at 06:27

Unveiling Iso- and Aniso-Hydric Disparities in Grapevine-A Reanalysis by Transcriptome Portrayal Machine Learning.

Functional Genomics of Vine

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Abstract (Expand)

Mechanisms underlying grapevine responses to water(-deficient) stress (WS) are crucial for viticulture amid escalating climate change challenges. Reanalysis of previous transcriptome data uncovered …

Authors: T. Konecny, A. Asatryan, M. Nikoghosyan, H. Binder

Date Published: 6th Sep 2024

Publication Type: Journal

PubMed ID: 39273985

Citation: Plants (Basel). 2024 Sep 6;13(17):2501. doi: 10.3390/plants13172501.

Created: 16th May 2025 at 12:42

Projection of High-Dimensional Genome-Wide Expression on SOM Transcriptome Landscapes

ML approaches for omic data analysis, Molecular characterization of cancers with long-read RNA sequencing

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Abstract (Expand)

organizing maps portraying has been proven to be a powerful approach for analysis of transcriptomic, genomic, epigenetic, single-cell, and pathway-level data as well as for “multi-omic” integrative …

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

Created: 7th Mar 2024 at 11:00

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