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8 Publications visible to you, out of a total of 8
Assigning Transcriptomic Subtypes to Chronic Lymphocytic Leukemia Samples Using Nanopore RNA-Sequencing and Self-Organizing Maps.

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

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

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 … disparities among isohydric and anisohydric grapevine cultivars in managing water scarcity. By using a self-organizing map (SOM) transcriptome portrayal, we elucidate specific gene expression trajectories, shedding light on the dynamic interplay of transcriptional programs as stress duration progresses. Functional annotation reveals key pathways involved in drought response, pinpointing potential targets for enhancing drought resilience in grapevine cultivation. Our results indicate distinct gene expression responses, with the isohydric cultivar favoring plant growth and possibly stilbenoid synthesis, while the anisohydric cultivar engages more in stress response and water management mechanisms. Notably, prolonged WS leads to converging stress responses in both cultivars, particularly through the activation of chaperones for stress mitigation. These findings underscore the importance of understanding cultivar-specific WS responses to develop sustainable viticultural strategies in the face of changing climate.

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

Date Published: 6th Sep 2024

Publication Type: Journal

Machine learned-based visualization of the diversity of grapevine genomes worldwide and in Armenia using SOMmelier

Abstract (Expand)

In the proposed study three major issues have been addressed: Firstly, the diversity of grapevine accessions worldwide and particularly in Armenia, a small country located in the largely volcanic … Armenian Highlands, is incredibly rich in cultivated and especially wild grapes; secondly, the information hidden in their (whole) genomes, e.g., about the domestication history of grapevine over the last 11,000 years and phenotypic traits such as cultivar utilization and a putative resistance against powdery mildew, and, thirdly machine learning methods to extract and to visualize this information in an easy to percept way. We shortly describe the Self Origanizing Maps (SOM) portrayal method called “SOMmelier” (as the vine-genome “waiter”) and illustrate its power by applying it to whole genome data of hundreds of grapevine accessions. We also give a short outlook on possible future directions of machine learning in grapevine transcriptomics and ampelogaphy.

Authors: Kristina Magaryan, Maria Nikogհosyan, Anush Baloyan, Hripsime Gasoyan, Emma Hovhannisyan, Levon Galstyan, Tomas Konecny, Arsen Arakelyan, Hans Binder

Date Published: 6th Dec 2023

Publication Type: Journal

Molecular Analysis of SARS-CoV-2 Lineages in Armenia

Abstract (Expand)

The sequencing of SARS-CoV-2 provides essential information on viral evolution, transmission, and epidemiology. In this paper, we performed the whole-genome sequencing of SARS-CoV-2 using nanopore and … Illumina sequencing to describe the circulation of the virus lineages in Armenia. The analysis of 145 full genomes identified six clades (19A, 20A, 20B, 20I, 21J, and 21K) and considerable intra-clade PANGO lineage diversity. Phylodynamic and transmission analysis allowed to attribute specific clades as well as infer their importation routes. Thus, the first two waves of positive case increase were caused by the 20B clade, the third peak caused by the 20I (Alpha), while the last two peaks were caused by the 21J (Delta) and 21K (Omicron) variants. The functional analyses of mutations in sequences largely affected epitopes associated with protective HLA loci and did not cause the loss of the signal in PCR tests targeting ORF1ab and N genes as confirmed by RT-PCR. We also compared the performance of nanopore and Illumina short-read sequencing and showed the utility of nanopore sequencing as an efficient and affordable alternative for large-scale molecular epidemiology research. Thus, our paper describes new data on the genomic diversity of SARS-CoV-2 variants in Armenia in the global context of the virus molecular genomic surveillance.

Authors: Diana Avetyan, Siras Hakobyan, Maria Nikoghosyan, Lilit Ghukasyan, Gisane Khachatryan, Tamara Sirunyan, Nelli Muradyan, Roksana Zakharyan, Andranik Chavushyan, Varduhi Hayrapetyan, Anahit Hovhannisyan, Shah A. Mohamed Bakhash, Keith R. Jerome, Pavitra Roychoudhury, Alexander L. Greninger, Lyudmila Niazyan, Mher Davidyants, Gayane Melik-Andreasyan, Shushan Sargsyan, Lilit Nersisyan, Arsen Arakelyan

Date Published: 17th May 2022

Publication Type: Journal

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

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 … analyses. However, the SOM method has a major disadvantage: it requires the retraining of the entire dataset once a new sample is added, which can be resource- and time-demanding. It also shifts the gene landscape, thus complicating the interpretation and comparison of results. To overcome this issue, we have developed two approaches of transfer learning that allow for extending SOM space with new samples, meanwhile preserving its intrinsic structure. The extension SOM (exSOM) approach is based on adding secondary data to the existing SOM space by “meta-gene adaptation”, while supervised SOM portrayal (supSOM) adds support vector machine regression model on top of the original SOM algorithm to “predict” the portrait of a new sample. Both methods have been shown to accurately combine existing and new data. With simulated data, exSOM outperforms supSOM for accuracy, while supSOM significantly reduces the computing time and outperforms exSOM for this parameter. Analysis of real datasets demonstrated the validity of the projection methods with independent datasets mapped on existing SOM space. Moreover, both methods well handle the projection of samples with new characteristics that were not present in training datasets.

Authors: Maria Nikoghosyan, Henry Loeffler-Wirth, Suren Davidavyan, Hans Binder, Arsen Arakelyan

Date Published: 27th Dec 2021

Publication Type: Journal

Transcriptome Patterns of BRCA1- and BRCA2- Mutated Breast and Ovarian Cancers

Abstract (Expand)

Mutations in the BRCA1 and BRCA2 genes are known risk factors and drivers of breast and ovarian cancers. So far, few studies have been focused on understanding the differences in transcriptome and …d functional landscapes associated with the disease (breast vs. ovarian cancers), gene (BRCA1 vs. BRCA2), and mutation type (germline vs. somatic). In this study, we were aimed at systemic evaluation of the association of BRCA1 and BRCA2 germline and somatic mutations with gene expression, disease clinical features, outcome, and treatment. We performed BRCA1/2 mutation centered RNA-seq data analysis of breast and ovarian cancers from the TCGA repository using transcriptome and phenotype “portrayal” with multi-layer self-organizing maps and functional annotation. The results revealed considerable differences in BRCA1- and BRCA2-dependent transcriptome landscapes in the studied cancers. Furthermore, our data indicated that somatic and germline mutations for both genes are characterized by deregulation of different biological functions and differential associations with phenotype characteristics and poly(ADP-ribose) polymerase (PARP)-inhibitor gene signatures. Overall, this study demonstrates considerable variation in transcriptomic landscapes of breast and ovarian cancers associated with the affected gene (BRCA1 vs. BRCA2), as well as the mutation type (somatic vs. germline). These results warrant further investigations with larger groups of mutation carriers aimed at refining the understanding of molecular mechanisms of breast and ovarian cancers.

Authors: Arsen Arakelyan, Ani Melkonyan, Siras Hakobyan, Uljana Boyarskih, Arman Simonyan, Lilit Nersisyan, Maria Nikoghosyan, Maxim Filipenko, Hans Binder

Date Published: 28th Jan 2021

Publication Type: Journal

WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene

Abstract (Expand)

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, … however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother's, and, to a lesser extent, with father's TL having the strongest influence on the offspring. In this cohort, mother's, but not father's age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait.

Authors: Lilit Nersisyan, Maria Nikoghosyan, Laurent C. Francioli, Androniki Menelaou, Sara L. Pulit, Clara C. Elbers, Wigard P. Kloosterman, Jessica van Setten, Isaäc J. Nijman, Ivo Renkens, Paul I. W. de Bakker, Freerk van Dijk, Pieter B. T. Neerincx, Patrick Deelen, Alexandros Kanterakis, Martijn Dijkstra, Heorhiy Byelas, K. Joeri van der Velde, Mathieu Platteel, Morris A. Swertz, Cisca Wijmenga, Pier Francesco Palamara, Itsik Pe’er, Kai Ye, Eric-Wubbo Lameijer, Matthijs H. Moed, Marian Beekman, Anton J. M. de Craen, H. Eka D. Suchiman, P. Eline Slagboom, Victor Guryev, Abdel Abdellaoui, Jouke Jan Hottenga, Mathijs Kattenberg, Gonneke Willemsen, Dorret I. Boomsma, Elisabeth M. van Leeuwen, Lennart C. Karssen, Najaf Amin, Fernando Rivadeneira, Aaron Isaacs, Albert Hofman, André G. Uitterlinden, Cornelia M. van Duijn, Mannis van Oven, Manfred Kayser, Martijn Vermaat, Jeroen F. J. Laros, Johan T. den Dunnen, David van Enckevort, Hailiang Mei, Mingkun Li, Mark Stoneking, Barbera D. C. van Schaik, Jan Bot, Tobias Marschall, Alexander Schönhuth, Jayne Y. Hehir-Kwa, Robert E. Handsaker, Paz Polak, Mashaal Sohail, Dana Vuzman, Karol Estrada, Steven A. McCarroll, Shamil R. Sunyaev, Fereydoun Hormozdiari, Vyacheslav Koval, Carolina Medina-Gomez, Ben Oostra, Jan H. Veldink, Leonard H. van den Berg, Steven J. Pitts, Shobha Potluri, Purnima Sundar, David R. Cox, Peter de Knijff, Qibin Li, Yingrui Li, Yuanping Du, Ruoyan Chen, Hongzhi Cao, Jun Wang, Ning Li, Sujie Cao, Jasper A. Bovenberg, Gert-Jan B. van Ommen, Arsen Arakelyan

Date Published: 10th Dec 2019

Publication Type: Journal

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