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.

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.

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.

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.

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.

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.

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.

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.

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.

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.