Hans Binder

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About Hans Binder:

No description specified

SEEK ID: https://armlifebank.am/people/4

Locations: Armenia , Germany

ORCID: https://orcid.org/0000-0002-2242-4678

Joined: 2nd Apr 2025

Expertise: Bioinformatics, Genomics, Precision Medicine, Machine Learning, Cancer Biology

Tools: Mathematical Modelling, Single Cell analysis, Computational and theoretical biology, Genomics, Transcriptomics

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Advanced Programmes list for this Person with search and filtering

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

Genes, Environment and Chronic Diseases

This program is designed to encapsulate and facilitate research projects focusing on the intricate dynamics between genetic predispositions and environmental factors in the progression of chronic diseases, excluding cancer. This initiative aims to unravel the molecular mechanisms and pathways that underlie these diseases, thereby paving the way for targeted interventions and therapies.

Projects: Mental disorders and aging brain, Omics-Based Insights into Human Long-Term Exposure to Environmental Metals, Deciphering the Fine-Scaled Temporal Changes in Microbiome Communities Following Antibiotic Exposure, Telomere-related biomarkers of disease in liquid biopsies

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

Advanced Projects list for this Person with search and filtering

Molecular Profiling of Cancer Metastases

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 ...

Programme: Cancer Omics

Public web page: https://abi.am/research/research-labs/agenus-lab/

Start date: 21st Feb 2024

Organisms: Human

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

Mental disorders and aging brain

This project focuses on studies around brain disorders and ageing.

Programme: Genes, Environment and Chronic Diseases

Public web page: Not specified

Organisms: Human

Biological pathway activity analysis

No description specified

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

Interdisciplinary Center for Bioinformatics (IZBI)

Country: Germany

City: Leipzig

Web page: https://www.izbi.uni-leipzig.de/

Advanced Investigations list for this Person with search and filtering

Digital life sciences for genome-based healthcare

ML approaches for omic data analysis

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, ...

Submitter: Arsen Arakelyan

Studies: No Studies

Assays: No Assays

Snapshots: No snapshots

Created: 14th Apr 2025 at 06:12

Showing 5 out of a possible 11 Publications Advanced Publications list for this Person with search and filtering

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

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

Pan-cancer analysis of telomere maintenance mechanisms.

Molecular characterization of cancers with long-read RNA sequencing

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Abstract (Expand)

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

Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder.

Mental disorders and aging brain, ML approaches for omic data analysis

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Abstract (Expand)

The molecular events underlying the development, manifestation, and course of schizophrenia, bipolar disorder, and major depressive disorder span from embryonic life to advanced age. However, little …

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.

Created: 28th Apr 2024 at 14:09

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

View all 11 Publications