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Detailed tutorial on variant calling from WGS data using GATK best practices, covering quality control, alignment, duplicate marking, base recalibration, and variant discovery with HaplotypeCaller.
Comprehensive guide to VCF and gVCF file formats in bioinformatics, covering structure, interpretation, and key concepts like genotypes, alleles, and variant recording for genomic analysis.
Comprehensive guide to DESeq2 for differential gene expression analysis in RNA-seq data, covering statistical models, normalization methods, and key steps in the analysis pipeline.
Learn metagenomics analysis using Kraken 2 in OmicsBox. Covers data preprocessing, taxonomic classification, visualization, and differential abundance analysis for shotgun sequencing data.
Comprehensive guide for downstream analysis of single-cell ATAC-Seq data using R and Signac. Covers integration with RNA-Seq, differential accessibility analysis, and visualization of genomic regions.
Explore somatic variant calling with Mutect2, covering key concepts, challenges, and hands-on demonstration of variant identification, filtering, and annotation using GATK best practices.
Understand why NGS samples generate multiple FASTQ files through library prep, flowcells, lane multiplexing, and learn proper data organization strategies for bioinformatics workflows.
Detailed tutorial on filtering and annotating genetic variants using GATK tools. Covers hard filtering, genotype refinement, and Funcotator for comprehensive variant analysis in bioinformatics workflows.
Learn step-by-step Weighted Gene Co-expression Network Analysis (WGCNA) for RNA-Seq data, covering data manipulation, outlier detection, normalization, module identification, and visualization techniques.
Step-by-step tutorial on single-cell trajectory analysis using Monocle3 and Seurat. Covers theory, workflow, and practical implementation in R, including data processing, visualization, and gene expression analysis along trajectories.
Step-by-step tutorial on processing RNA-Seq data: quality control, trimming, alignment, and quantification. Covers tools like FastQC, Trimmomatic, HISAT2, and featureCounts, with comparisons of aligners and best practices.
Comprehensive overview of bioinformatics and computational biology, covering research fields, career paths, applications, job prospects, and educational opportunities for aspiring professionals in this cutting-edge domain.
Comprehensive tutorial on pseudo-bulk analysis for single-cell RNA-Seq data, covering the what, why, and how. Includes data manipulation, aggregation, and differential expression analysis using R, Seurat, and DESeq2.
Comprehensive tutorial on identifying cell markers in single-cell RNA-Seq data using Seurat, covering various marker identification methods, data visualization, and cluster annotation techniques.
Comprehensive tutorial on integrating single-cell RNA-Seq datasets using Seurat in R, covering data merging, batch effect correction, and visualization techniques for improved analysis.
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