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10 Publications visible to you, out of a total of 10
Integrated Multi-Omics Maps of Lower-Grade Gliomas.

Abstract (Expand)

Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative … analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It "portrays" the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers.

Authors: H. Binder, M. Schmidt, L. Hopp, S. Davitavyan, A. Arakelyan, H. Loeffler-Wirth

Date Published: 4th Jun 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

SARS-CoV-2 detection by extraction-free qRT-PCR for massive and rapid COVID-19 diagnosis during a pandemic in Armenia

Abstract (Expand)

COVID-19 pandemic severely impacted the healthcare and economy on a global scale. It is widely recognized that mass testing is an efficient way to contain the spread of SARS-CoV-2 infection as well as … aid in the development of informed policies for disease management. However, the current COVID-19 worldwide infection rates increased the demand for rapid and reliable screening of infection. We compared the performance of qRT-PCR in direct heat-inactivated (H), heat-inactivated and pelleted (HC) samples against RNA in a group of 74 subjects (44 positive and 30 negative). Then we compared the sensitivity of HC in a larger group of 196 COVID-19 positive samples. Our study suggests that HC samples show higher accuracy for SARS-CoV-2 detection PCR assay compared to direct H (89 % vs 83 % of the detection in RNA). The sensitivity of detection using direct samples varied depending on the sample transport and storage media as well as the viral loads (as measured by qRT-PCR Ct levels). Altogether, all the data suggest that purified RNA provides more accurate results, however, direct sample testing with qRT-PCR may help to significantly increase testing capacity. Switching to the direct sample testing is justified if the number of tests is doubled at least.

Authors: Diana Avetyan, Andranik Chavushyan, Hovsep Ghazaryan, Ani Melkonyan, Ani Stepanyan, Roksana Zakharyan, Varduhi Hayrapetyan, Sofi Atshemyan, Gisane Khachatryan, Tamara Sirunyan, Suren Davitavyan, Gevorg Martirosyan, Gayane Melik-Andreasyan, Shushan Sargsyan, Armine Ghazazyan, Naira Aleksanyan, Xiushan Yin, Arsen Arakelyan

Date Published: 4th Jun 2021

Publication Type: Journal

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