I am having some fun running AlphaPulldown on a computing cluster. A requirement is to have input sequences in FASTA format. I found that I needed to get ~600 sequences. I had a list of the relevant Uniprot IDs. Surely getting the sequences for these proteins should be straightforward? Solution The Uniprot IDs can be converted – using the ID Mapping Tool on the Uniprot website – into any number of other IDs.

I like to set up a standardised directory structure for RStudio projects. The idea came from here. In brief, the structure is: Data/ Output/Data/ Output/Plots/ Script/ My typical workflow is therefore to: select File > New Project in RStudio make a new directory and RProj file then use this R script or these shell commands to setup the directories. So far, so good. However, this process is a bit tedious.

There are a few one-liners that I use in the shell to do some really nifty stuff. I struggle to quickly find and reuse these and asked for a solution on Mastodon.

Another post looking at Twitter data in R. It follows this one and this one. I wanted to look again at my tweeting frequency over the 12 years on Twitter, but this time do it in a calendar view. Something like a GitHub commit calendar would be perfect. I have used a library for this in the past.

Please consider this a “supplementary analysis” to my previous post looking at the frequency of tweets from my personal account over the last 12 years. I was curious about what times I was active on Twitter (measured by when I tweeted). Others might be interested in a solution to look at this in R. The code As in the previous post, we need to get the data into R and then make sure we have a date object to work with.

At the time of writing, I have essentially left Twitter. It was a fun ride and without going into what’s happening there now, this is a good opportunity to look at my 12 years on the platform. Early in November, I downloaded my data and locked my Twitter account. This gave me all the data I needed. Using R, a few nifty libraries and the tweets.js file that was part of the download, I could gain quite a lot of insight.

I recently made my first R package and was asked how I did it. The answer of course was: I searched, read, and stumbled around until it was done. But having gone through the process I figured it was worthwhile summarising what I did and what I found tricky. First off, there are a ton of resources out there that describe how to go about building a package.

A comment from a referee led me to find a method to describe curvature of membranes. This is a quick write-up of our solution. I couldn’t find a solution readily available in Fiji, so I created one using a combination of Fiji for tracing the curvature and IgorPro to do the fitting. If there is any interest I can convert this code into an all-Fiji routine. The code is simple and a gist is available here.

I read about aesthetic uses of Latin Squares on John Cook’s site a few months ago. Since I maintain a resource to use colour tables in Igor Pro, I thought it would be fun to use Latin Squares to display colour tables for easy visualisation. Briefly, I wrote some code to generate a 9 x 9 latin square and assigned the values 1 – 9 to a colour table wave. The results were nice.

In a previous post, I described a coding challenge sent to me by my colleague. Here is the challenge again: My solution was… let’s say… over-engineered. He did tell me that the winner (the challenge was set in an international coding competition) solved it in a ridiculously short time. He didn’t tell me how short which I think would have led me to take a different approach.