Category Archives: How To

How to do things. doh.

Writing Papers in OPIG

I’m dedicating this blog post to something I spend a great deal of my time doing – reading the manuscripts that members of OPIG produce.

As every member of OPIG knows we often go through a very large number of drafts as I inexpertly attempt to pull the paper into a shape that I think is acceptable.

When I was a student I was not known for my ability to write, in fact I would say the opposite was probably true. Writing a paper is a skill that needs to be learnt and just like giving talks everyone needs to find their own style.

Before you write or type anything, remember that a good paper starts with researching how your work fits into existing literature. The next step is to craft a compelling story, whilst remembering to tailor your message to your intended audience.

There are many excellent websites/blogs/articles/books advising how to write a good paper so I am not going to attempt a full guide instead here are a few things to keep in mind.

  1. Have one story not more than one and not less – when you write the paper look at every word/image to see how it helps to deliver your main message.
  2. Once you know your key message it is often easiest to not write the paper in the order the sections appear! Creating the figures from the results first helps to structure the whole paper, then you can move on to methods, then write the results and discussion, then the conclusion, followed by the introduction, finishing up with the abstract and title.
  3. Always place your work in the context of what has already been done, what makes your work significant or original.
  4. Keep a consistent order – the order in which ideas come in the abstract should also be the same in the introduction, the methods, the results, the discussion etc.
  5. A paper should have a logical flow. In each paragraph, the first sentence defines context, the body is the new information, the last sentence is the take-home message/conclusion. The whole paper builds in the same way from the introduction setting the context, through the results which give the content, to the discussion’s conclusion. 
  6. Papers don’t need cliff hangers – main results/conclusions should be clear in the abstract.
  7. State your case with confidence.
  8. Papers don’t need to be written in a dry/technical style…
  9. …..but remove the hyperbole. Any claims should be backed up by the evidence in the paper.
  10. Get other people to read your work – their comments will help you (and unless it’s me you can always ignore their suggestions!)

Uniformly sampled 3D rotation matrices

It’s not as simple as you’d think.

If you want to skip the small talk, the code is at the bottom. Sampling 2D rotations uniformly is simple: rotate by an angle from the uniform distribution \theta \sim U(0, 2\pi). Extending this idea to 3D rotations, we could sample each of the three Euler angles from the same uniform distribution \phi, \theta, \psi \sim U(0, 2\pi). This, however, gives more probability density to transformations which are clustered towards the poles:

Sampling Euler angles uniformly does not give an even distribution across the sphere.

In Fast Random Rotation Matrices (James Avro, 1992), a method for uniform random 3D rotation matrices is outlined, the main steps being:

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Making your python tool as easy to install as possible

Have you ever tried to use someone else’s code and spent a whole day trying to install it? Have you ever decided not to use a tool because installing it was a massive pain? Both of those have happened to me and, to be honest, it is a massive shame. The authors may spend large amounts of time developing these tools and in the end, no one uses them because they can’t get them to work. So I have decided to try and make all code I develop as easy and painless as possible to install and use.

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Linux Horror Stories and Protection Spells (Volume I)

Don’t get me wrong. I love Linux. After many years of using it, I ended up appreciating how flexible, potent, and even beautiful it is. However, using Linux has never been a bed of roses and every single Linux user that I know has had to deal with many problems since the very beginning. Indeed, I still remember how frustrating installing my first Linux machine was, especially after realizing that my network card was not working. Had I given up, I would never have written this post.

Although many of the problems that I faced while using Linux are related to updates and drivers (how painful NVidia drivers updates can be, I will write another post about that in the future), I must recognize that on many other occasions I was the only one responsible for such problems. Consequently, I want to warn the reader against a couple of those mistakes I made in the past and provide some tips about how to deal with them.

My worst nightmare: rm –r * 

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Automated intermolecular interaction detection using the ODDT Python Module

Detecting intermolecular interactions is often one of the first steps when assessing the binding mode of a ligand. This usually involves the human researcher opening up a molecular viewer and checking the orientations of the ligand and protein functional groups, sometimes aided by the viewer’s own interaction detecting functionality. For looking at single digit numbers of structures, this approach works fairly well, especially as more experienced researchers can spot cases where the automated interaction detection has failed. When analysing tens or hundreds of binding sites, however, an automated way of detecting and recording interaction information for downstream processing is needed. When I had to do this recently, I used an open-source Python module called ODDT (Open Drug Discovery Toolkit, its full documentation can be found here).

My use case was fairly standard: starting with a list of holo protein structures as pdb files and their corresponding ligands in .sdf format, I wanted to detect any hydrogen bonds between a ligand and its native protein crystal structure. Specifically, I needed the number and name of the the interacting residue, its chain ID, and the name of the protein atom involved in the interaction. A general example on how to do this can be found in the ODDT documentation. Below, I show how I have used the code on PDB structure 1a9u.

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Hosting multiple Flask apps using Apache/mod_wsgi

A common way of deploying a Flask web application in a production environment is to use an Apache server with the mod_wsgi module, which allows Apache to host any application that supports Python’s Web Server Gateway Interface (WSGI), making it quick and easy to get an application up and running. In this post, we’ll go through configuring your Apache server to host multiple Python apps in a stable manner, including how to run apps in daemon mode and avoiding hanging processes due to Python C extensions not working well with Python sub-interpreters (I’m looking at you, numpy).

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Bioinformatics Hackathon Reflection

A week ago I participated in Copenhagen Bioinformatics Hackathon 2021, a hackathon focusing on machine learning and proteins, as a mentor for a challenge proposed by our group. The whole experience was fun, but I am also sitting here contemplating over a lot of things I wish I had done differently. For this blog text, I therefore want to highlight two changes which I believe would have greatly improved my challenge and which can hopefully also work as an inspiration for others presenting a hackathon challenge. 

Going into this event I had some experience from a few hackathons I had previously attended. Based on this, I wanted to create a challenge containing two parts. First, a simple task which everyone would be able to create a solution for, and second, a more challenging addition to the first task for more experienced participants. I decided to go with the challenge of predicting which heavy and light chains can form a pair, where the additional challenge was to try to visualize which residues were relevant for this interaction. Together with OAS containing a really nice positive dataset of paired chains, I thought this was going to be an amazing challenge, but as soon as the event began I started seeing the flaws of the challenge.

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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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Better understanding of correlation

Although correlation is often used as the linear relationship between two sets of points, I will in the following text use it more broadly to mean any relationship between two sets of points.

You have tasked yourself with finding the correlation between the different features in your dataset. Your purpose could be to remove highly correlated features or just improve your understanding of your data. Nonetheless, calculating and using the Pearson Correlation Coefficient (PCC) or the Spearman’s rank Correlation Coefficient (SCC) to get an overview of the correlations might be the first thing that comes to your mind.

Unfortunately, both of these are limited to linear (PCC) or monotonic (SCC) relationships. In datasets with many and complex features, many of them will be highly correlated, just not linearly (or monotonic). Instead these correlations can be non-linear which, as seen in the third row in the below figure, does not get detected with PCC.

Figure: PCC of different sets of x and y points. https://en.wikipedia.org/wiki/Correlation_and_dependence
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Peer Review: reviewing as an early career researcher

Peer review is an important component of academic research and publishing, but it can feel like an opaque process, especially for those not directly involved. I am very fortunate to have been able to participate in the peer review of multiple papers, despite being very early in my career, through support from my supervisors and a mentoring program run by Sense about Science with Nature Communications. Here are some of the things I have learned.

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