Telomere Maintenance Pathways in Lower-Grade Gliomas: Insights from Genetic Subtypes and Telomere Length Dynamics Version 1
External LinkThis is the submission accompanying the "Telomere Maintenance Pathways in Lower-Grade Gliomas: Insights from Genetic Subtypes and Telomere Length Dynamics" paper.
The dataset contains the TCGA and CGGA LGG
The dataset is organized as follows:
Folder "CGGA_LGG_IDH_subtype_mean_vis" - The CGGAL GG data IDH subtype pathway visualization and CSV data. Folder "Long_Short_telomeres_path_vis" - The TCGA LGG data ALT and TEL pathway visualization for Long and Short telomeres mean values. Folder "TCGA_ATRX_IDH_WT_Mutanat_path_vis" - The TCGA BRCA, COAD, KIRC, PAAD, and UCEC data ALT and TEL pathway visualization for IDH WT and Mutant samples mean values. Folder "TCGA_LGG_IDH_subtype_mean_vis" - TCGA LGG ALT and TEL pathway visualization for IDH-A, IDH-O, and IDH-wt mean values and CSV data. File "COPD_LGG_GBM_PSF_data.csv" - CSV data file of COPD, LGG, and GBM PSF results for negative control. File "LGG_TCGA_IDH_ATRX_subtype.csv" - CSV data file of TCGA LGG IDH subtype information and ATRX mutation. File "TL_ratio_tel_type_ALT_TEL_PSF_tel_phenotype_Survival_IDH_type.csv" - CSV data file of TCGA LGG TL ratio, Telomere length, ALT and TEL pathway branches, Survalval data, IDH subtype and TMM phenotypes.
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Creators and SubmitterViews: 8
Created: 9th Jul 2025 at 08:47
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Version 1 (earliest) Created 9th Jul 2025 at 08:47 by Arsen Arakelyan
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Institutions: Institute of Molecular Biology NAS RA, Armenian Bioinformatics Institute
https://orcid.org/0000-0002-6851-1056
Projects: Functional Genomics of Vine, Molecular Profiling of Cancer Metastases, Biological pathway activity analysis, Mental disorders and aging brain, ML approaches for omic data analysis, Armenian Wine Genome Program
Institutions: Armenian Bioinformatics Institute, Interdisciplinary Center for Bioinformatics (IZBI)
https://orcid.org/0000-0002-2242-4678
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.
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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.
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Abstract (Expand)
Authors: Meline Hakobyan, Hans Binder, Arsen Arakelyan
Date Published: 28th Apr 2025
Publication Type: Journal
