Meline Hakobyan

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About Meline Hakobyan:

No description specified

SEEK ID: https://armlifebank.am/people/7

Location: Armenia

ORCID: https://orcid.org/0000-0002-1089-0655

Joined: 14th Mar 2024

Expertise: Not specified

Tools: Not specified

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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

Cancer Omics

This Programme offers a comprehensive summary of various projects focused on deciphering the intricate molecular mechanisms of cancer development, progression, and response to treatments, utilizing bioinformatics, computational biology, and machine learning. These are multi-laboratory efforts carried out across several research institutions, supported by various grant programs.

Projects: Molecular Profiling of Cancer Metastases, Molecular characterization of cancers with long-read RNA sequencing

Web page: Not specified

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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

Molecular characterization of cancers with long-read RNA sequencing

The overarching objective of this project is to utilize nanopore sequencing technology and to develop machine learning–based pipelines for the integrative analysis of heterogeneous biological "big data," including multi-omic and phenotypic datasets. The goal is to investigate the molecular mechanisms underlying selected cancers and to identify novel omics-based markers for cancer classification, diagnostics, and prognostics.

Programme: Cancer Omics

Public web page: Not specified

Start date: 1st Sep 2021

End date: 30th Sep 2026

Organisms: Human

Tools for Telomere Biology

No description specified

Programme: Bioinformatics Tools for Analysis of Big Genomic Data

Public web page: Not specified

Organisms: Not specified

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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

Pan-cancer analysis of telomere maintenance mechanisms.

Molecular characterization of cancers with long-read RNA sequencing

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Telomeres, protective caps at chromosome ends, maintain genomic stability and control cell lifespan. Dysregulated telomere maintenance mechanisms (TMMs) are cancer hallmarks, enabling unchecked cell …

Authors: M. Hakobyan, H. Binder, A. Arakelyan

Date Published: 2nd Jul 2024

Publication Type: Journal

PubMed ID: 38763334

Citation: J Biol Chem. 2024 Jun;300(6):107392. doi: 10.1016/j.jbc.2024.107392. Epub 2024 May 18.

Created: 11th Feb 2025 at 13:12

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