Data files

Genotype data produced by HaplotypeCaller and Illumina genotyping datа

Links to Individual Files in the Dataset

This study included 299 SARS-CoV-2 infected patients. During April-May 2020, blood and nasopharyngeal swab samples from patients were collected in six hospitals of the Health Ministry of the Republic of Armenia: National Center for Infection Diseases, Surb Grigor Lusavorich, Surb Astvatsamayr and Artashat Medical Centres, Maralik and Akhuryan Mother and Child Health Centers. The diagnosis was performed on nasopharyngeal specimens by detection of SARS-CoV-2 ...

Datasets for Publication

Sequencing of SARS-CoV-2 provides essential information on viral evolution, transmission, and epidemiology. In this study, we performed whole-genome sequencing of SARS-CoV-2 using nanopore and Illumina short-read sequencing to describe the circulation of the virus lineage in Armenia.

This dataset contains Nextstrain configuration files, the auspice JSON file, BEAST output logs, and trees files, and resulting log and tree files as well as R scripts and data files used in phylogenetic and functional ...

Aligned sequencing data is available in the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/) under accession SRP095082. Variants were called using GATK HaplotypeCaller (version 3.6). After joint performing joint genotyping multi-sample vcf file was generated. Next, SNPs and indels were extracted into two different vcf files and specific set of filters were applied for each case.

This is the submission accompanying raw result files for multiple-layer SOM (ml-SOM) analysis for the paper "Transcriptome patterns of BRCA1- and BRCA2- mutated breast and ovarian cancers".

The dataset contains the results of the ml-SOM analysis of RNA-sequencing data from TCGA-OV (ovarian cancer) and TCGA-BRCA (breast cancer) projects.

The dataset is organized as follows:

Folder "12.BC.40 - Results" - ml-SOM analysis of TCGA-BRCA (breast cancer) dataset Folder "12.OV.40 - Results" - ml-SOM ...

oposSOM is a comprehensive data analysis software combining diversity analyses, biomarker selection, functional information mining, and visualization to the machine learning based ‘high-dimensional data portraying’.