RNA-Seq

[jjjermiah]

Hackathon

• Basics of RNA-Seq #68
• Data Types

Future work:

• Get Familiar with Data Types and Outputs
  • Identify common data outputs: FASTQ, BAM/SAM, and count matrices.
  • Describe the purpose of normalized counts (e.g., TPM, FPKM, RPKM).

Resources To Be Described:

Software & Tool Documentation

• STAR - Spliced Transcripts Alignment to a Reference
  A popular RNA-Seq aligner.
• HISAT2 - Hierarchical Indexing for Spliced Alignment of Transcripts
  Fast and memory-efficient tool for RNA alignment.
• DESeq2 and EdgeR
  Widely used R packages for differential expression analysis, available on Bioconductor.

Practical Datasets To Be Described:

• The Cancer Genome Atlas (TCGA)
• GEO (Gene Expression Omnibus)
• EGA

some inspiration:

• https://koesterlab.github.io/data-science-for-bioinfo/data_analyses/differential_expression.html