Genome Spot

The molecular biology lab is becoming increasingly data driven and as a researcher or manager we need to make sure we're recording our work properly. Good documentation is critical to the future usability of the code, and especially so when it comes to handing a project over between project leads. It is also good practice to enable other researchers to run and use the code after publication.  So here are my recommendations as a researcher and manager of a small bioinformatics team: Use GitHub, GitLab, CodeBerg or another central repository to record changes made to code daily. Team members should invite manager and colleagues as collaborators on the Git repositories. R scripts should be written as R Markdown files, as this enables a few benefits like better documentation, outputs are arranged in sequence and high level transparency. R Markdown scripts are output as HTML files for sharing/archiving. For python based workflows, Jupyter notebooks achieve more or less the same thing. Q...

INTRO DRAM prices skyrocketed 171% in 2025 [ 1 ], and this trend looks like it will continue into 2026 unless there is a crash in demand for hardware for GenAI applications. This leaves bioinformaticians and other data analysts in a pickle, as most applications we use require a lot of RAM. To keep costs low, we might consider using a single stick (aka Dual In-line Memory Module: DIMM) of RAM for a new workstation build, which is something that has been tried with reasonable success for low budget gaming setups [ 2 ]. So in this post we will look at whether using a single stick of DDR5 DRAM will cause a dramatic reduction in computational throughput as compared to the normal two-stick setup. We will also examine whether stock memory configuration (4800MT/s) is any slower as compared to the tweaked settings (EXPO 6000MT/s with low latency and high bandwidth support). SETUP The tests I will use include: A synthetic CPU test using stress-ng Single end RNA-seq human (STAR) Single end RNA-se...