Genomics, Transcriptomics, and metagenomics

Genomics

• next-gen sequencing (NGS)

  • massively parallel sequencing

• SNPs and whole genomes

• Much more power and can more easily identify regions under natural selection

• Bioinformatic analyses with HPCs

• an assembled and annotated genome is helpful for examining regions under selection: you know the function of the genes

• not always available for non-model organisms

  • know theres a difference, but dont know what it signifies

• Datasets require:

  • careful processing

  • extensive data filtering

  • knowledgeable use of analytical programs

  • Attention to assumptions

  • Consideration of alternative explanations for results

Transcriptomics: RNA sequencing

• can extract mRNA (from one or more tissue types, individuals, infection status, development stage, etc)

• mRNA degrades very quickly, so liquid nitrogen or special reagents often involved in sample storage

• Once RNA is extracted, reverse transcribe to cDNA (complement DNA) i.e., you’re only looking at transcribed regions like exons

• cDNA is sequenced and aligned to a reference transcriptome

• e.g., marsh rice rats and hanta virus

Seaside Sparrows on oiled and unoiled sites following the Deepwater Horizon oil spill

• ~270 genes are differentially expressed

• Pathway analysis: genes associated with liver damage, liver proliferation, and cell death

  

Fig. 1. Differentially expressed genes in liver samples. (A) A volcano plot showing the differentially expressed genes (DEG; red circles) in birds exposed to oil compared to control birds (fold change N ˂ 1.5, false discovery rate (FDR) corrected p value (or q value) ˂ 0.1).

Metagenomics

• all DNA (sort of) present in a sample

  • e.g., DNA in soil, water samples to identify the species present (eDNA)

  • e.g., the microbiome present in the gut, on skin, etc.

  • e.g., all insect sequences in a bird fecal sample to examine diet