Intro to Bioinformatics

Where we overlap with the biology team.

Why?

We want to look both broadly and specifically, which means data past the size of Excel t-tests. We’re looking for expression signatures that clue us into mechanisms of sight degradation — then searching for medicines showing the anti-signature as potential treatments.

Central Dogma

DNA is transcribed into RNA and translated into proteins.

Pairing Desired Information with Data Type

  • Bulk RNA-seq: cheap, broad characterization of expression
  • Bulk ATAC-seq: broad characterization of the epigenome
  • CutNRun: (description TODO)
  • HiChIP-seq: broad characterization of chromatin configuration
  • Single Cell RNA-seq: precise characterization of expression, clustered by cell type
  • Single Cell ATAC-seq: precise characterization of the epigenome, clustered by cell type
  • Spatial Transcriptomics: precise, in-situ characterization of genes of interest; cell-cell communication

External Resources

  • Journal articles — read published methods to see what other labs are doing. The lab runs Journal Club instead of the usual lab meeting once a month.
  • Seurat (Satija lab) — R package for single-cell analysis, with vignettes for default workflows.
  • Single Cell Best Practices — free e-book, current as of 2023.1
  • Analyzing RNA-seq data with DESeq2 — vignette by DESeq2’s authors.
  • Biostars — bioinformatics Q&A forum (Michael Love, DESeq2’s author, is an active member).
  • YouTube channels (Jeffrey Maurer’s picks): Illumina (sequencing-machine presentations), StatQuest (stats/bioinformatics intros), Bioinformagician (specific pipeline walkthroughs), Sanbomics (detailed task walkthroughs).

  1. Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for single-cell analysis across modalities. Nat Rev Genet (2023). https://doi.org/10.1038/s41576-023-00586-w ↩