Bioinformatic; Learn Bulk RNA-Seq Data Analysis From Scratch

Describe DNA as a double-stranded, helical polymer of nucleotides with deoxyribose and phosphate; bases adenine, thymine, cytosine, and guanine pair via hydrogen and phosphodiester bonds to form the double helix.

DNA resides in the nucleus of a typical eukaryotic cell, organized as chromosomes. Long DNA threads wrap around a scaffold in a chromosome, and a portion unwraps to express itself.

Compare prokaryotic and eukaryotic genes, contrasting tandem arrangement and absence of introns with splicing in eukaryotes; contrast promoters, ribosome binding sites (Shine-Dalgarno vs Kozak sequences), RNA polymerases, and transcription factors.

Explore the coding DNA structure, including open reading frames, codons and degeneracy, exons and introns, regulatory elements, and features like start and stop codons and transcription start sites.

Discover how nascent mRNA gains stability through 5' capping, poly-A tail addition, and intron splicing to produce mature mRNA ready for translation.

Explore RNA-seq analysis to uncover differential expression of genes between normal and cancer cells, and compare gene expression across liver, kidney, and plant stress conditions.

Learn the basic workflow of rna-seq analysis, comparing normal and cancer cells, by extracting rna, converting to cdna, sequencing, and aligning to a reference genome to assess differential gene expression.

Explore fastq, sam, and bam formats and how they feed the feature count matrix derived from reads for differential gene expression analysis in the RNA-seq workflow.

Learn how to install the Linux operating system for NGS data analysis, with guidance for Windows and Mac users, including using the Mac terminal and Unix compatibility.

Install and update a Linux environment in Windows, then upgrade and verify the system; install FastQC, HISAT2, and Subgrade for RNA-seq data analysis.

Install samtools on your Linux system with sudo apt install samtools, enter your password, maintain an active internet connection, then verify with samtools --version (e.g., 1.10).

Download and install Trimmomatic 0.39 in a Linux environment. Trim low-quality reads and adapters, and run the executable jar with Java to prepare fastq data for a feature count matrix.

Perform a quality check of a fastq file using fastqc in Ubuntu, navigate directories, run fastqc on test.fastq, and review the resulting html report.

Learn how to read a FASTQC report to assess bulk RNA-seq read quality, including per-base quality, sequence length distribution, GC content, adapters, and trimming with Trimmomatic.

Trim low-quality reads from single-end sequencing data using Trimmomatic, specify phred 33 and four threads, and compare trimmed fastq and fastqc results.

Build a Hisat2 compatible reference genome from a fasta file downloaded from the Ensembl genome browser, using Solomon fish as an example, and generate indices for the NGS pipeline.

Build a gene-level count matrix from aligned RNA-seq data using the Subread featureCounts tool, select single- or paired-end reads, and output test_feature_counts.txt with the Homo sapiens GRCh38 106 GTF annotation.

Install and verify that R and RStudio are correctly integrated by downloading from CRAN, selecting the appropriate OS installer, and completing the setup.

Set the working directory in RStudio, understand its meaning, and navigate to the Udemy folder on your desktop to establish inputs and outputs in that location.

Explore the four core data types in r: vectors, matrices, data frames, and lists, and learn how data frames enable mixed data types for bulk rna-seq analysis.

Learn how functions act as reusable blocks of code in R, and how packages bundle related functions you can install with install.packages in R Studio, e.g., ggplot.

Learn Bioconductor, an open source software for rigorous, reproducible analysis of biological data, and install it in R using BiocManager to access packages such as genomic features.

Learn how to install the DESeq2 package from Bioconductor in RStudio, including using BiocManager to install DESeq2, handling updates, and adding helpful code comments.

Learn how to perform a pre-analysis quality check for bulk RNA-seq data by validating that column names match metadata rows and their order using a short R script.

Learn to perform differential expression analysis with DESeq2 by building a dataset from counts and metadata and comparing treated versus untreated cell lines using a dexamethasone design.

Explore leaky expression and differential expression in RNA-Seq data, understanding how low read counts arise and why we remove leaky genes during analysis.

Learn to remove low-count genes in bulk RNA-Seq data with DESeq2 by filtering counts >= 10 via row sums and subsetting from 64,100 to 22,369 rows with eight columns.

Demonstrates performing differential expression analysis with DESeq2, setting untreated as reference, using relevel and the seek function to compare treated vs untreated samples, and exporting results to CSV.

Explore size factor estimation in DESEq2, and how gene length, sequencing depth, and library composition influence normalization of raw counts. Learn how this normalization enables accurate differential expression analysis.

Explore dispersion estimation in DESeq2: interpret dispersion, the red fitted curve, dispersion shrinkage to reduce false positives, and subsequent GLM fitting with log values and log two fold change.

Explain p-values and p-adjusted values in bulk RNA-seq analysis, showing how multiple test corrections like Bonferroni and Benjamini-Hochberg control false positives and identify differentially expressed genes.

Perform quality checks on rna-seq data before analyses, using principal component analysis, size factor estimation, and dispersion plots to assess overall data quality and guide subsequent analysis.

Explore the basic idea of principal component analysis and how PCA plots reveal clustering and outliers in RNA-seq data, with interpretation of PC1 and PC2.

Learn how size factors reveal systematic bias in RNA-seq by comparing sequencing depth across samples, identify under-sequenced samples like 0.72 or 0.67, and compute them in R.

Build dispersion plots in R Studio to visualize dispersion estimates and the fitted red line, interpret a downward trend as a sign of strong differential expression, and perform quality checks.

Learn the basics of tidyverse and ggplot2, using filtering, sorting, and data editing to create scatter plots, histograms, density plots, and box plots, while managing large RNA-seq datasets.

Explore the Tidyverse and ggplot2 in R using the Gapminder dataset, inspect a 1704-row dataframe with six columns, and install and load dplyr and ggplot2 to analyze and visualize data.

Install tidyverse and ggplot2, then apply filter, arrange, and mutate with pipes to filter, sort, and transform data in a bioinformatics workflow.

Learn to use ggplot2 basics from the tidyverse to create scatter plots and other visualizations, mapping x and y aesthetics like GDP per capita and life expectancy.

Identify the best genes as the top differentially expressed by adjusted value, using R studio with dplyr; arrange by adjusted, take the top ten, and save as best_genes.csv.

Explore how volcano plots display RNA-seq results by plotting log2 fold change against p-values to reveal upregulated genes on the right, downregulated on the left, and the most significant genes.

Create a volcano plot for RNA-seq data in R using ggplot2, filter out missing p-adjusted values, color significant genes (p<0.05, abs(log two fold change) > 1), and label genes with symbols.

Learn to build a heat map of RNA-Seq differentially expressed genes (DEGs) using ComplexHeatMap in R, including data normalization, z-scores, and hierarchical clustering of samples and genes.

Perform basic gene ontology and KEGG pathway analysis of gene lists using an online tool, selecting Homo sapiens gene symbols, and interpreting GO BP, CC, MF terms and KEGG pathways.