Blog Series: WoG, Cesky Krumlov: Day 11: Functional metagenomic modeling with some Drosophila sperm thrown in for good measure...

Joseph Bielawski
Dalhousie University
Halifax, Nova Scotia, Canada

Topic: Searching for functional divergence in genomes and metagenomes

We are going to start off with the metagenomic portion of the talk first then at the bottom we'll hash out the genomic portion of his talk...

So in conducting research in metagenomics, as we learned from Rob Beiko's talk it's about who is there and what are they doing. Today we focused on how to infer function from metagenomic data. How to tack on phenotype to a metagenome 'genotype' if you will.

So you have two approaches in metagenomics: targeted analysis (ala PCR amplification from the environment using universal primers to catch all the organisms with your gene of interest) and random analysis which is a catch-all for everything you got within your sample. Now it's always great to have apriori knowledge and you are highly encouraged to collect as much metadata as possible about your sample...but inferring function from metagenomics is quite daunting especially if you have little to go on so it helps to have a model.

Now models are by no means going to fully explain exactly what's going on in the actual environment but they allow you to make inferences based on your data that you can explore in further detail and corroborate.

The model we will discuss actually doesn't have a name that I could find within his slides! So I will call it MetaG-MetaP-Modeling (MMM)...metagenomic metabolic pathway modeling. Bear in mind when the publication comes out it'll have most likely a cooler name.