Your bioinformatics
cheatsheets.
Command-line tricks, R one-liners, pipeline snippets - everything you forget at 2am. Curated for genomics, data science, and computational biology.
Core language references
The foundational languages and tools every bioinformatician reaches for - version control, scripting, and markup.
The R visualisation toolkit
From tidyverse manipulation to publication-quality figures - the packages that power modern R analyses.
Terminal survival kit
Shell commands, scripting patterns, and bioinformatics-aware text processing.
Bioawk
Awk extensions for processing biological data - FASTQ, FASTA, SAM, BED, VCF aware.
Your analysis, end-to-end
Tools and formats specific to genomics - workflow managers, file standards, and analysis frameworks.
Understanding FASTQ structure, quality scores, and how to interpret FastQC output reports.
Quick reference for Nextflow syntax, channels, processes and DSL2 workflow patterns.
Reproducible pipelines - rules, wildcards, conda integration, and cluster submission.
Germline and somatic variant calling, BQSR, and VQSR command reference.
The Python data science stack
The everyday Python toolkit for wrangling, summarising, and visualising experimental data.
Statistical & machine learning methods
Algorithm references and statistical method guides for predictive modelling and inference in genomics.
# pick an estimator from sklearn import svm clf = svm.SVC() clf.fit(X, y) y_hat = clf.predict(X_t) "classification"
ML Algorithm Map
Choose the right algorithm - classification, regression, clustering, and dimensionality reduction.
Scikit-learn algorithm map
Classification, regression, clustering on tabular data.
Interactive flowchart guiding estimator choice by data shape.
Python · NumPy · basic ML vocabulary.
Pandas for prep · Matplotlib / Seaborn for viz.
import tensorflow as tf m = tf.keras.Sequential([ layers.Dense(64), layers.Dense(10), ]) m.compile("adam"...)
API Quick Guide
Key TensorFlow APIs, Keras layers, model compilation, and training-loop patterns.
TensorFlow / Keras essentials
Deep learning · neural nets · large-scale model training.
Curated docs hub linking quickstarts, tutorials, API reference.
Python · NumPy · core ML concepts.
JAX · PyTorch · scikit-learn for traditional baselines.
# t-test in R t.test(x, y, alternative = "two.sided", var.equal = FALSE) aov(y ~ group, data)
Stats with R
Statistical methods and R implementations - hypothesis testing, regression, ANOVA, and more.
Statistics with R - methods & code
Inferential stats · linear models · experimental design.
Bookdown chapters with worked examples and R code blocks.
R fundamentals · basic probability theory.
tidyverse for prep · ggplot2 for diagnostics.
Tools for every workflow
Cross-cutting tools that appear in nearly every analysis pipeline, regardless of domain.
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Bioinformatics, statistical genomics & computational biology - practical tools, learning resources, and expert guidance based in South Australia. We are a community that believes in accessible knowledge and learning together.