Probabilistic and causal modelling of genome-scale data

This is a set of lecture notes on machine learning methods for analyzing genome-scale data, taught as part of the BINF301 course.

The notes are written in markdown and converted to a static site using Docsy, a Hugo theme module for technical documentation sites.

The public URL for the site is: https://tmichoel.github.io/genome-scale-modelling/

Running the website locally

You can find detailed theme instructions in the Docsy user guide.

Building and running the site locally requires a recent extended version of Hugo. You can find out more about how to install Hugo for your environment in our Getting started guide.

Once you've made your working copy of the site repo, from the repo root folder, run:

hugo server

Troubleshooting

As you run the website locally, you may run into the following error:

➜ hugo server

INFO 2021/01/21 21:07:55 Using config file: 
Building sites … INFO 2021/01/21 21:07:55 syncing static files to /
Built in 288 ms
Error: Error building site: TOCSS: failed to transform "scss/main.scss" (text/x-scss): resource "scss/scss/main.scss_9fadf33d895a46083cdd64396b57ef68" not found in file cache

This error occurs if you have not installed the extended version of Hugo. See this section of the user guide for instructions on how to install Hugo.

Or you may encounter the following error:

➜ hugo server

Error: failed to download modules: binary with name "go" not found

This error occurs if you have not installed the go programming language on your system. See this section of the user guide for instructions on how to install go.

Code

The "code" subfolder contains example notebooks to illustrate the course material.

This code base is using the Julia Language and DrWatson to make a reproducible scientific project named

Genome-scale ML code

To (locally) reproduce this project, do the following:

1. Download this code base. Notice that raw data are typically not included in the git-history and may need to be downloaded independently.
2. Open a Julia console and do:

   julia> using Pkg
   julia> Pkg.add("DrWatson") # install globally, for using `quickactivate`
   julia> Pkg.activate("path/to/this/project")
   julia> Pkg.instantiate()
   

This will install all necessary packages for you to be able to run the scripts and everything should work out of the box, including correctly finding local paths.

You may notice that most scripts start with the commands:

using DrWatson
@quickactivate "Genome-scale ML code"

which auto-activate the project and enable local path handling from DrWatson.

The main code files are Pluto notebooks and are located in the notebooks folder. To run the reactive notebooks, follow the instruction at the bottom of the Pluto homepage.