Notes worth keeping
A running record of things I've learned - genomics, bioinformatics, Linux, machine learning, and programming - written so future-me (or you) can learn from it too.
10 notes
Introduction to Git and GitHub for Reproducible Research
Why version control matters for research code, and the small set of commands that cover 95% of day-to-day use.
Python for Bioinformatics: Getting Started with Biopython
Reading sequence files, computing GC content, and querying NCBI - the Biopython basics that cover most day-to-day scripting.
Applying Machine Learning to Genomic and Clinical Data
The practical challenges that make health data different from a typical ML dataset, and how to handle them.
Machine Learning 101: Supervised vs Unsupervised Learning
The most fundamental split in machine learning, explained with examples from genomic data.
Shell Scripting Basics for Automating Bioinformatics Tasks
Variables, loops, and functions in Bash - enough to stop repeating yourself across sample files.
From Reads to Variants: An Introduction to NGS Data Analysis Pipelines
The standard stages of a next-generation sequencing pipeline, from raw reads to a clinically interpretable variant list.
Bioinformatics File Formats Explained: FASTA, FASTQ, SAM/BAM, VCF
A practical field guide to the four file formats you'll touch in almost every sequencing pipeline.
Understanding Genetic Variants: SNPs, Indels, and CNVs
The main categories of DNA variation, why they matter clinically, and how they show up in a VCF file.
Genomics 101: DNA, Genes, and the Central Dogma
The foundational biology behind every bioinformatics pipeline - what a genome actually is, and how DNA becomes protein.
Essential Linux Commands for Bioinformatics
A practical cheat sheet of the Linux commands you'll actually use day-to-day when working with sequencing data.