Bioinformatics
Explore a comprehensive bioinformatics course covering a wide range of topics and techniques essential for analyzing biological data. Learn to plot heatmaps in RStudio, identify differentially expressed genes from RNA-Seq samples using Galaxy, perform gene set enrichment analysis, and understand VCF file formats. Master RNA-Seq analysis, including differential gene expression and single-cell RNA-Seq data analysis using the Seurat package. Dive into SAM/BAM file specifications, GEO data analysis in R, and advanced statistical concepts like log2 fold-change and linear mixed models. Discover weighted correlation network analysis (WGCNA), survival analysis on cancer data, and hidden Markov models. Explore object-oriented programming in R, meta-analysis techniques, and protein folding with AlphaFold 2. Gain skills in text mining, TCGA cancer database analysis, and creating complex heatmaps. Investigate scRNA-Seq integration analysis, cancer somatic mutation analysis using MAFtools, and machine learning approaches for biomarker identification. Understand file formats like GFF3, learn best practices for data manipulation and project organization in R, and explore 16s rRNA sequencing analysis. Delve into topics such as semantic similarity between genes, organoid creation, and utilizing resources like cBioPortal for human cancer genomics. Read more