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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.
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.
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.
Comprehensive tutorial on analyzing single-cell RNA-Seq data using Seurat in R, covering data processing, quality control, normalization, dimensionality reduction, and clustering for genomics research.
Learn to visualize gene expression data using R and ggplot2. Create barplots, density plots, boxplots, scatterplots, and heatmaps from RNA-Seq data. Hands-on tutorial for beginners in bioinformatics.
Learn to manipulate gene expression data from NCBI GEO using R and dplyr. Covers data reading, metadata retrieval, and essential dplyr functions for effective bioinformatics analysis.
Explore Elucidata's Polly platform for effortless RNA-Seq dataset discovery, featuring intuitive search, metadata exploration, visual analysis, and easy downloads. Overcome common challenges in accessing public genomic data.
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