I wrote a little bit recently on why I am so interested in making CAGs from metagenomic data, and I wanted to provide a little update on that topic.
CAGs are "Co-Abundant Genes," which is to say the groups of genes which are found at similar abundances in metagenomic data across many different samples. The inference from the "co-abundance" observation is that those genes are likely present on the same bits of DNA (i.e. chromosomes) across those samples. I am particularly interested in these groups of genes because of the highly mosaic nature of microbial genomic evolution.
Having spent a bit of time on maximizing the computational efficiency of finding CAGs, I have been struck by just how hard it is. Naively, the core computational task requires calculating a similarity (or co-abundance) metric for every pair of genes, which scales exponentially with the number of genes. For 100 genes there are ~5,000 comparisons, for 1,000 genes there are ~500,000, for 10,000 genes there are ~50,000,000 comparisons, etc. etc.
Speaking practically, this means that it takes a long time on a very large computer to get this work done. There are all sorts of tricks to make it faster, including parallelization, code optimization, etc., but at the core it is just a hard task.
However, I happened to ask the Twitter hive mind for help, and Andrew Carroll happened to give me some great advice:
A solution of this nature is similar to Approximate Nearest Neighbor, which is worth a read just for general background alone - e.g. https://t.co/umqqFcaOAH— Andrew Carroll (@acarroll_ATG) July 6, 2018
I see this problem come up a lot as scale increases. Law of large numbers is a friend here. Using approx soln usually good
This lead me down the road of exploring the Approximate Nearest Neighbor algorithms. It turns out that there a number of different algorithms out there which approximate this task of finding the closest set of points in highly dimensional space, which is exactly what I needed to make CAGs. There's even a website showing you which of these pieces of code work the best.
Interestingly, one of these algorithms (called "annoy") was produced by Spotify, and can be used to rapidly find the nearest neighbor from a large index that can be mmapped for rapid access across multiple threads.
The point I want to reinforce is that Good Software matters. Implementing an exact solution was taking me 10 days with an expensive 72 core machine, which gave me little opportunity to iterate over different variables or analyze new datasets quickly. The advantage of using a better piece of software is not just that I can make pretty figures, it's that I can actually do science more quickly, spending less money, and getting more accurate answers. I didn't know that this software was out there, and so it wasn't until I asked some people on Twitter for me to find out about it. But now that I know I'm happy to tell the world to give it a try, and hopefully that will save you all some time, some money, and help you find out the answer to whatever questions are important.