oBIG - omics Bioinformatics for Health

The aim of oBIG is to establish modern genome bioinformatics in Armenia to investigate genesis and progression of complex diseases. Therefore we will extend the collaboration between the Interdisciplinary Center for Bioinformatics (IZBI) of Leipzig University and the Institute of Molecular Biology (IMB) in Yerevan, Armenia, to accomplish three main aspects of the project:Firstly, current research topics in systems biology of cancer, lung diseases and infections will be addressed by a joint team of young scientists and Armenian students.Secondly, methods for complex genomic and phenotypic data will be developed and implemented, creating prerequisites for an epidemiologic cohort study of the Armenian population.Thirdly, the project supports students and young scientists in the frame of practical courses, lectures and joint supervision of graduation theses.

Programme: Default Programme

LHA ID: 8AC22MUVW9-4

Public web page: https://www.izbi.uni-leipzig.de/current-projects/obig/

Human Diseases: No Human Disease specified

Health Atlas - Local Data Hub/Leipzig PALs: No PALs for this Project

Project Coordinators: No Project coordinators for this Project

Project created: 20th Jan 2022

Project Hierarchy

Country: Germany

City: Leipzig

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

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy.

Leipzig Melanoma Studies, oBIG - omics Bioinformatics for Health

Abstract (Expand)

Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a …

Authors: S. Hakobyan, H. Loeffler-Wirth, A. Arakelyan, H. Binder, M. Kunz

Date Published: 2nd Jul 2021

Publication Type: Journal article

PubMed ID: 34281234

Citation: Int J Mol Sci. 2021 Jul 2;22(13). pii: ijms22137165. doi: 10.3390/ijms22137165.

Created: 20th Jan 2022 at 13:20

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health.

LIFE Adult, oBIG - omics Bioinformatics for Health

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Background: The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and …

Authors: M. Schmidt, L. Hopp, A. Arakelyan, H. Kirsten, C. Engel, K. Wirkner, K. Krohn, R. Burkhardt, J. Thiery, M. Loeffler, H. Loeffler-Wirth, H. Binder

Date Published: 11th Mar 2021

Publication Type: Journal article

PubMed ID: 33693414

Citation: Front Big Data. 2020 Oct 30;3:548873. doi: 10.3389/fdata.2020.548873. eCollection 2020.

Created: 23rd Apr 2021 at 12:04, Last updated: 7th Dec 2021 at 17:58

oposSOM-Browser: an interactive tool to explore omics data landscapes in health science

LHA - Leipzig Health Atlas, oBIG - omics Bioinformatics for Health

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Background: oposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and …

Authors: Henry Loeffler-Wirth, Jasmin Reikowski, Siras Hakobyan, Jonas Wagner, Hans Binder

Date Published: 1st Dec 2020

Publication Type: Journal article

DOI: 10.1186/s12859-020-03806-w

Citation: BMC Bioinformatics 21(1),465

Created: 3rd Feb 2021 at 16:51, Last updated: 7th Dec 2021 at 17:58

SOMmelier-Intuitive Visualization of the Topology of Grapevine Genome Landscapes Using Artificial Neural Networks.

oBIG - omics Bioinformatics for Health

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BACKGROUND: Whole-genome studies of vine cultivars have brought novel knowledge about the diversity, geographical relatedness, historical origin and dissemination, phenotype associations and genetic …

Authors: M. Nikoghosyan, M. Schmidt, K. Margaryan, H. Loeffler-Wirth, A. Arakelyan, H. Binder

Date Published: 17th Jul 2020

Publication Type: Journal article

PubMed ID: 32709105

Citation: Genes (Basel). 2020 Jul 17;11(7). pii: genes11070817. doi: 10.3390/genes11070817.

Created: 20th Jan 2022 at 13:24

Transcriptome-Guided Drug Repositioning.

oBIG - omics Bioinformatics for Health

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Abstract (Expand)

Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data …

Authors: A. Arakelyan, L. Nersisyan, M. Nikoghosyan, S. Hakobyan, A. Simonyan, L. Hopp, H. Loeffler-Wirth, H. Binder

Date Published: 12th Dec 2019

Publication Type: Journal article

PubMed ID: 31842375

Citation: Pharmaceutics. 2019 Dec 12;11(12). pii: pharmaceutics11120677. doi: 10.3390/pharmaceutics11120677.

Created: 20th Jan 2022 at 13:22

Telomere Length Maintenance and Its Transcriptional Regulation in Lynch Syndrome and Sporadic Colorectal Carcinoma.

HNPCC-Sys - Genomic and transcriptomic heterogeneity of colorectal tumours arising in Lynch syndrome, oBIG - omics Bioinformatics for Health

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Background: Activation of telomere maintenance mechanisms (TMMs) is a hallmark of most cancers, and is required to prevent genome instability and to establish cellular immortality through reconstitution … of capping of chromosome ends. TMM depends on the cancer type. Comparative studies linking tumor biology and TMM have potential impact for evaluating cancer onset and development. Methods: We have studied alterations of telomere length, their sequence composition and transcriptional regulation in mismatch repair deficient colorectal cancers arising in Lynch syndrome (LS-CRC) and microsatellite instable (MSI) sporadic CRC (MSI s-CRC), and for comparison, in microsatellite stable (MSS) s-CRC and in benign colon mucosa. Our study applied bioinformatics analysis of whole genome DNA and RNA sequencing data and a pathway model to study telomere length alterations and the potential effect of the "classical" telomerase (TEL-) and alternative (ALT-) TMM using transcriptomic signatures. Results: We have found progressive decrease of mean telomere length in all cancer subtypes compared with reference systems. Our results support the view that telomere attrition is an early event in tumorigenesis. TMM gets activated in all tumors studied due to concerted overexpression of a large fraction of genes with direct relation to telomere function, where only a very small fraction of them showed recurrent mutations. TEL-related transcriptional state was dominating in all CRC subtypes, showing, however, subtype-specific activation patterns; while contribution of the ALT-TMM was slightly more prominent in the hypermutated MSI s-CRC and LS-CRC. TEL-TMM is mainly activated by over-expression of DKC1 and/or TERT genes and their interaction partners, where DKC1 is more prominent in MSS than in MSI s-CRC and can serve as a transcriptomic marker of TMM activity. Conclusions: Our results suggest that transcriptional patterns are indicative for TMM pathway activation with subtle differences between TEL and ALT mechanisms in a CRC subtype-specific fashion. Sequencing data potentially provide a suited measure to study alterations of telomere length and of underlying transcriptional regulation. Further studies are needed to improve this method.

Authors: L. Nersisyan, L. Hopp, H. Loeffler-Wirth, J. Galle, M. Loeffler, A. Arakelyan, H. Binder

Date Published: 22nd Nov 2019

Publication Type: Not specified

Human Diseases: cancer

PubMed ID: 31750255

Citation: Front Oncol. 2019 Nov 5;9:1172. doi: 10.3389/fonc.2019.01172. eCollection 2019.

Created: 20th Apr 2020 at 10:12, Last updated: 7th Dec 2021 at 17:58

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians - A Machine Learning Approach.

LHA - Leipzig Health Atlas, oBIG - omics Bioinformatics for Health

Abstract (Expand)

Background: During the last decades a number of genome-wide association studies (GWASs) has identified numerous single nucleotide polymorphisms (SNPs) associated with different complex diseases. However, … associations reported in one population are often conflicting and did not replicate when studied in other populations. One of the reasons could be that most GWAS employ a case-control design in one or a limited number of populations, but little attention was paid to the global distribution of disease-associated alleles across different populations. Moreover, the majority of GWAS have been performed on selected European, African, and Chinese populations and the considerable number of populations remains understudied. Aim: We have investigated the global distribution of so far discovered disease-associated SNPs across worldwide populations of different ancestry and geographical regions with a special focus on the understudied population of Armenians. Data and Methods: We have used genotyping data from the Human Genome Diversity Project and of Armenian population and combined them with disease-associated SNP data taken from public repositories leading to a final dataset of 44,234 markers. Their frequency distribution across 1039 individuals from 53 populations was analyzed using self-organizing maps (SOM) machine learning. Our SOM portrayal approach reduces data dimensionality, clusters SNPs with similar frequency profiles and provides two-dimensional data images which enable visual evaluation of disease-associated SNPs landscapes among human populations. Results: We find that populations from Africa, Oceania, and America show specific patterns of minor allele frequencies of disease-associated SNPs, while populations from Europe, Middle East, Central South Asia, and Armenia mostly share similar patterns. Importantly, different sets of SNPs associated with common polygenic diseases, such as cancer, diabetes, neurodegeneration in populations from different geographic regions. Armenians are characterized by a set of SNPs that are distinct from other populations from the neighboring geographical regions. Conclusion: Genetic associations of diseases considerably vary across populations which necessitates health-related genotyping efforts especially for so far understudied populations. SOM portrayal represents novel promising methods in population genetic research with special strength in visualization-based comparison of SNP data.

Authors: M. Nikoghosyan, S. Hakobyan, A. Hovhannisyan, H. Loeffler-Wirth, H. Binder, A. Arakelyan

Date Published: 21st May 2019

Publication Type: Journal article

PubMed ID: 31105750

Citation: Front Genet. 2019 Apr 26;10:394. doi: 10.3389/fgene.2019.00394. eCollection 2019.

Created: 22nd Apr 2020 at 12:57, Last updated: 7th Dec 2021 at 17:58

A modular transcriptome map of mature B cell lymphomas.

GLA - German Lymphoma Alliance, MMML-MYC-SYS, oBIG - omics Bioinformatics for Health

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Abstract (Expand)

BACKGROUND: Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic … phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt's lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma. METHODS: We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics. RESULTS: We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt's lymphoma and particularly on 'double-hit' MYC and BCL2 transformed lymphomas. CONCLUSIONS: The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.

Authors: H. Loeffler-Wirth, M. Kreuz, L. Hopp, A. Arakelyan, A. Haake, S. B. Cogliatti, A. C. Feller, M. L. Hansmann, D. Lenze, P. Moller, H. K. Muller-Hermelink, E. Fortenbacher, E. Willscher, G. Ott, A. Rosenwald, C. Pott, C. Schwaenen, H. Trautmann, S. Wessendorf, H. Stein, M. Szczepanowski, L. Trumper, M. Hummel, W. Klapper, R. Siebert, M. Loeffler, H. Binder

Date Published: 30th Apr 2019

Publication Type: Not specified

Human Diseases: B-cell lymphoma, diffuse large B-cell lymphoma, follicular lymphoma, Burkitt lymphoma

PubMed ID: 31039827

Citation: Genome Med. 2019 Apr 30;11(1):27. doi: 10.1186/s13073-019-0637-7.

Created: 16th May 2019 at 09:53, Last updated: 7th Dec 2021 at 17:58

Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome.

CAPSys - Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome, oBIG - omics Bioinformatics for Health

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Abstract (Expand)

We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated …

Authors: L. Hopp, H. Loeffler-Wirth, L. Nersisyan, A. Arakelyan, H. Binder

Date Published: 2nd Aug 2018

Publication Type: Not specified

Human Diseases: disease by infectious agent, pneumonia

PubMed ID: 30065722

Citation: Front Immunol. 2018 Jul 17;9:1620. doi: 10.3389/fimmu.2018.01620. eCollection 2018.

Created: 13th May 2019 at 11:38, Last updated: 7th Dec 2021 at 17:58

Hans Binder