Category Archives: Software & Services

Dealing with multiple compilers

I don’t know you, but when I am compiling a complicated program and everything goes straightforward I feel a mixture of joy and surprise. Let’s face it, compiling can be quite frustrating, and if you need to compile something relatively old, chances are that you will spend hours and hours trying to understand the compiler error messages.

Several such compiler errors, that in many cases can be quite convoluted, tell you that your program requires an older version, so you first need to install it. I am going to assume that you have sudo rights, otherwise, we will be playing the game of compiling a compiler, something that I recommend you to do at least and at most once in your life.

In common Linux distributions like Ubuntu, installing an older compiler is as easy as using apt or yum:

#Ubuntu
$ sudo apt install build-essential
$ sudo apt install gcc-7 g++-7
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Make your code do more, with less

When you wrangle data for a living, you start to wonder why everything takes so darn long. Through five years of introspection, I have come to conclude that two simple factors limit every computational project. One is, of course, your personal productivity. Your time of focused work, minus distractions (and yes, meetings figure here), times your energy and mental acuity. All those things you have little control over, unfortunately. But the second is the productivity of your code and tools. And this, in principle, is a variable that you have full control over.

Even quick calculations, when applied to tens of millions of sequences, can take quite some time!

This is a post about how to increase your productivity, by helping you navigate all those instances when the progress bar does not seem to go fast enough. I want to discuss actionable tools to make your code run faster, and generate more results, with less effort, in less time. Instructions to tinker less and think more, so you can do the science that you truly want to be doing. And, above all, I want to give out advice that is so counter-intuitive that you should absolutely consider following it.

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Packaging with Conda

If you are as happy for the big snake as I am, you have probably wondered how you can create a Conda package with your amazing code. Fear not, in the following text you will learn how to make others go; 

conda install -c coolperson amazingcode

Roughly, the only thing needed to create a Conda package, is a ‘meta.yaml’ file specific for your code. This file contains all the metadata needed to create your package and is highly customizable. While this means the meta.yaml can be written to allow your Conda package to work on any operating system and with any dependencies (doesn’t have to be python) it can be annoying to write from scratch (here is a guide for manually writing this file). Since we just want to create a simple Conda package, we will in this guide avoid fiddling around with the meta.yaml file and instead create the file based on a PyPI package. This will also give you a nice template, if you later need to adapt your meta.yaml file.
Note: Conda packages can also be made from GitHub repositories, which is likely favorable in most cases, but it also requires some manual work on the meta.yaml.

1. Create a PyPI package of your code

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Former OPIGlets – where are they now?

Since OPIG began in 2003, 53 students* have managed to escape. But where are these glorious people now? I decided to find out, using my best detective skills (aka LinkedIn, Google and Twitter).

* I’m only including full members who have left the group, as per the former members list on the OPIG website

Where are they?

Firstly, the countries. OPIGlets are mostly still residing in the UK, primarily in the ‘golden triangle’ of London, Oxford and Cambridge. The US comes in second, followed closely by Germany (Note: one former OPIGlet is in Malta, which is too small to be recognised in Geopandas so just imagine it is shown on the world map below)

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2021 likely to be a bumper year for therapeutic antibodies entering clinical trials; massive increase in new targets

Earlier this month the World Health Organisation (WHO) released Proposed International Nonproprietary Name List 125 (PL125), comprising the therapeutics entering clinical trials during the first half of 2021. We have just added this data to our Therapeutic Structural Antibody Database (Thera-SAbDab), bringing the total number of therapeutic antibodies recognised by the WHO to 711.

This is up from 651 at the end of 2020, a year which saw 89 new therapeutic antibodies introduced to the clinic. This rise of 60 in just the first half of 2021 bodes well for a record-breaking year of therapeutics entering trials.

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A handful of lesser known python libraries

There are more python libraries than you can shake a stick at, but here are a handful that don’t get much love and may save you some brain power, compute time or both.

Fire is a library which turns your normal python functions into command-line utilities without requiring more than a couple of additional lines of copy-and-paste code. Being able to immediately access your functions from the command line is amazingly helpful when you’re making quick and dirty utilities and saves needing to reach for the nuclear approach of using getopt.

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AlphaFold 2 is here: what’s behind the structure prediction miracle

Nature has now released that AlphaFold 2 paper, after eight long months of waiting. The main text reports more or less what we have known for nearly a year, with some added tidbits, although it is accompanied by a painstaking description of the architecture in the supplementary information. Perhaps more importantly, the authors have released the entirety of the code, including all details to run the pipeline, on Github. And there is no small print this time: you can run inference on any protein (I’ve checked!).

Have you not heard the news? Let me refresh your memory. In November 2020, a team of AI scientists from Google DeepMind  indisputably won the 14th Critical Assessment of Structural Prediction competition, a biennial blind test where computational biologists try to predict the structure of several proteins whose structure has been determined experimentally but not publicly released. Their results were so astounding, and the problem so central to biology, that it took the entire world by surprise and left an entire discipline, computational biology, wondering what had just happened.

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Singularity: a guide for the bewildered bioinformatician

Have you ever worked with a piece of software that is awfully difficult to set up? That legacy code written on FORTRAN 77, that other one that requires significant modifications to compile, or any of those that require a long-winded bash script with a thousand dependencies (which you also have to install!). Would it not be helpful if, when that red-eyed PhD student, that one that just spent three months writing up their thesis, says that they absolutely must use that server where you have installed all your stuff, you could just relocate to another one without trouble? Well, you may be able to do that now. You just need to use containerization.

The idea behind containerization is rather simple. The best way to ensure anyone can reproduce your work is to, well, ship your entire system to whomever needs to use it. You could, for example, pack up your desktop in a box, and ship it to your collaborators anywhere in the world. Unfortunately, this idea is quite unpractical, not only because of tedious logistics (ever had to deal with customs?), but also because suddenly you won’t be able to run your own pipeline. However, it is a good enough thought that at some point made a clever engineer wonder whether there was a way to ship an entire system without physically delivering the computer. And that’s exactly what they designed.

40ft x 8ft (9ft 6") One trip high cube shipping container bl
Best way to make sure your collaborators on the other side of the world can run your pipeline — just pack your desktop in one of these, and ship it away!
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The Coronavirus Antibody Database: 10 months on, 10x the data!

Back in May 2020, we released the Coronavirus Antibody Database (‘CoV-AbDab’) to capture molecular information on existing coronavirus-binding antibodies, and to track what we anticipated would be a boon of data on antibodies able to bind SARS-CoV-2. At the time, we had found around 300 relevant antibody sequences and a handful of solved crystal structures, most of which were characterised shortly after the SARS-CoV epidemic of 2003. We had no idea just how many SARS-CoV-2 binding antibody sequences would come to be released into the public domain…

10 months later (2nd March 2021), we now have tracked 2,673 coronavirus-binding antibodies, ~95% with full Fv sequence information and ~5% with solved structures. These datapoints originate from 100s of independent studies reported in either the academic literature or patent filings.

The entire contents CoV-AbDab database as of 2nd March 2021.
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BioDataScience101: a fantastic initiative to learn bioinformatics and data science

Last Wednesday, I was fortunate enough to be invited as a guest lecturer to the 3rd BioDataScience101 workshop, an initiative spearheaded by Paolo Marcatili, Professor of Bioinformatics at the Technical University of Denmark (DTU). This session, on amino acid sequence analysis applied to both proteomics and antibody drug discovery, was designed and organised by OPIG’s very own Tobias Olsen.

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