Siras Hakobyan

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About Siras Hakobyan:

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 somatic mutation data. This project is conducted in collaboration with Leipzig University. Since 2017, Siras has been engaged in genome bioinformatics, accumulating diverse expertise in the field.

SEEK ID: https://armlifebank.am/people/8

Location: Armenia

ORCID: https://orcid.org/0000-0002-6875-2482

Joined: 14th Mar 2024

Expertise: Bioinformatics

Tools: Cytoscape, Genomics, Microarray analysis, Python, R, Single Cell analysis, Transcriptomics

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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

Space Radiobiology

Principal Investigator: Prof. David Goukassian

University: Icahn School of Medicine at Mount Sinai

Research team: Recruitment in progress

Duration:2023-2027

Hosting partner: Institute of Molecular Biology NAS RA (IMB)

Project Importance The high likelihood of developing genomic instability due to exposure to ionizing radiation suggests that exposure can cause DNA damage to hematopoietic stem cells (HSCs), promoting the development of somatic mutations that can lead to aberrant clonal events. ...

Projects: Sex-specific differences in long-term gamma and simGCRsim-associated alterations in deferential gene expression in the heart tissue

Web page: https://fast.foundation/en/program/847/2022/new_tab/6586/7014

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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

Sex-specific differences in long-term gamma and simGCRsim-associated alterations in deferential gene expression in the heart tissue

Space radiation (IR) from Solar Particle Events (SPE) and Galactic Cosmic Rays (GCR), also known as high charge and energy (HZE) IR, is a primary risk associated with deep-space missions. There are limited animal and human studies on the risk of cardiovascular disease (CVD) development due to space-IR. The cardiac effects induced by space-type IR, specifically simplified GCR simulated (simGCRsim)-IR, are yet to be discovered. We hypothesized that gamma (γ) and simGCRsim IR-induced biological ...

Programme: Space Radiobiology

Public web page: Not specified

Start date: 1st Jul 2023

End date: 30th Jun 2027

Organisms: Mouse

Biological pathway activity analysis

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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Life long effects of radiation and high fat dieat on the mouse left ventricular tissue

Sex-specific differences in long-term gamma and simGCRsim-associated alterations in deferential gene expression in the heart tissue

Sequencing raw data.

Creators: Arsen Arakelyan, Siras Hakobyan

Submitter: Arsen Arakelyan

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

Created: 11th Feb 2025 at 13:41

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PSF toolkit: an R package for pathway curation and topology-aware analysis

Biological pathway activity analysis

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 ...

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

Created: 5th Feb 2025 at 05:42, Last updated: 5th Feb 2025 at 05:46

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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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Abstract (Expand)

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

Evaluating sex-specific responses to western diet across the lifespan: impact on cardiac function and transcriptomic signatures in C57BL/6J mice at 530 and 640/750 days of age.

Sex-specific differences in long-term gamma and simGCRsim-associated alterations in deferential gene expression in the heart tissue

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BACKGROUND: Long-term consumption of Western Diet (WD) is a well-established risk factor for the development of cardiovascular disease (CVD); however, there is a paucity of studies on the long-term …

Authors: A. Stepanyan, A. Brojakowska, R. Zakharyan, S. Hakobyan, S. Davitavyan, T. Sirunyan, G. Khachatryan, M. K. Khlgatian, M. Bisserier, S. Zhang, S. Sahoo, L. Hadri, A. Rai, V. N. S. Garikipati, A. Arakelyan, D. A. Goukassian

Date Published: 28th Dec 2024

Publication Type: Journal

PubMed ID: 39732652

Citation: Cardiovasc Diabetol. 2024 Dec 28;23(1):454. doi: 10.1186/s12933-024-02565-9.

Created: 11th Feb 2025 at 13:26

Genewise detection of variants in MEFV gene using nanopore sequencing

Study of the molecular mechanisms of familial Mediterranean fever using genetic engineering and functional genomics

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Mediterranean Fever (FMF) is a genetic disorder with complex inheritance patterns and genotype-phenotype associations, and it is highly prevalent in Armenia. FMF typically follows an autosomal recessive …

Authors: Lilit Ghukasyan, Gisane Khachatryan, Tamara Sirunyan, Arpine Minasyan, Siras Hakobyan, Andranik Chavushyan, Varduhi Hayrapetyan, Hovsep Ghazaryan, Gevorg Martirosyan, Gohar Mkrtchyan, Valentina Vardanyan, Vahan Mukuchyan, Ashot Davidyants, Roksana Zakharyan, Arsen Arakelyan

Date Published: 29th Nov 2024

Publication Type: Journal

DOI: 10.3389/fgene.2024.1493295

Citation: Frontiers in Genetics,15

Created: 6th Dec 2024 at 09:33

PSF toolkit: an R package for pathway curation and topology-aware analysis.

Biological pathway activity analysis, Molecular characterization of cancers with long-read RNA sequencing

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Abstract (Expand)

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based … pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

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.

Created: 6th Mar 2024 at 19:17, Last updated: 3rd Apr 2025 at 08:38

Long-term environmental metal exposure is associated with hypomethylation of CpG sites in NFKB1 and other genes related to oncogenesis.

Omics-Based Insights into Human Long-Term Exposure to Environmental Metals

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Abstract (Expand)

BACKGROUND: Long-term environmental exposure to metals leads to epigenetic changes and may increase risks to human health. The relationship between the type and level of metal exposure and epigenetic …

Authors: A. Stepanyan, A. Petrackova, S. Hakobyan, J. Savara, S. Davitavyan, E. Kriegova, A. Arakelyan

Date Published: 7th Aug 2023

Publication Type: Journal

PubMed ID: 37550793

Citation: Clin Epigenetics. 2023 Aug 7;15(1):126. doi: 10.1186/s13148-023-01536-3.

Created: 6th Mar 2024 at 18:15