Articles by tag: comp-bio
Here is a brief rundown of file permissions on Unix systems and how to change them.
I use Anaconda managing my computational software environments. Here are some pragmatic tips for making conda environments easier to deal with.
Dependencies are complicated for computational biologists. Adapting a different development strategy can help your end users.
Hi-C data analysis is still a relatively new field in genomics. The data itself is quite large and expensive to make, which means datasets and exploration of the data is still immature, compared to other technologies like RNA-seq. Here, I discuss aggregate peak analysis, a commonly-used and poorly-documented analytical technique to verify identified features in Hi-C data.
Differential analysis using sequencing data is, at its heart, a very simple idea that involves a lot of complicated statistics. It makes explaining the simple idea to newcomers in bioinformatics very difficult. Here, I want to break down the motivation behind differential analysis and explain where the complicated statistics come from.
How incomprehensible machine learning models answer questions without providing the solutions we desire.
There are many reasons to be excited about scientific progress in the biological sciences, especially if you're a mathematician of almost any kind.
I offer 10 practical suggestions for designing robust, intuitive, and user-friendly software tools for bioinformatics.
A brief introduction to creating your own conda packages.
A brief description of how I try my best to keep a low-maintenance and reproducible software environment.