Further learning resources
WarningThis page will continue to be updated as the course progresses.
1 Introduction
This page lists further resources for learning about the topics covered in this course, and for learning more about related topics. Required readings and optional self-study resources from the pages for each week are also repeated here, so that you can find them all in one place.
2 Reproducibility
General
- Good enough practices in scientific computing (Wilson et al. 2017, PLoS Computational Biology)
- Reproducible Research: A Retrospective (Peng & Hicks 2021, Annu Rev Public Health)
- Ten Simple Rules for Reproducible Computational Research (Sandve et al. 2013, PLoS Computational Biology)
- The Turing Way – A community handbook on reproducible, collaborative data science.
- Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward (Sparks et al. 2023, Phytopathology)
- Open access and reproducibility in plant pathology research: Guidelines and best practices (Grünwald et al. 2024, Phytopathology)
- The five pillars of computational reproducibility: bioinformatics and beyond (Ziemann et al. 2023, Briefings in Bioinformatics)
- Five selfish reasons to work reproducibly (Markowetz 2015, Genome Biology)
- A quick guide to organizing computational biology projects (Noble 2009, PLoS Computational Biology)
Data management and documentation
- The FAIR Guiding Principles for scientific data management and stewardship (Wilkinson et al. 2016, Scientific Data)
- What’s in a README? (Dryad blog), and a README template for datasets (Cornell University)
- Project structure (slides by Danielle Navarro)
3 Markdown
4 Shell
Online guides and tutorials
- sandbox.bio – Interactive bioinformatics tutorials, including on shell basics, that run in your browser.
- Command Challenge – shell puzzles to solve in your browser.
- Nice collections of one-liners, mostly for bioinformatics
Books
- The Linux Command Line: A Complete Introduction (William Shotts, 2019)
- Linux Command Line and Shell Scripting Bible (Christine Bresnahan, 2015)
- Command Line Kung Fu (Jason Cannon, 2014)
Further reading
- Ten simple rules for getting started with command-line bioinformatics (Brandies & Hogg 2021, PLoS Computational Biology)
- Five reasons why researchers should learn to love the command line (Perkel 2021, Nature)
5 Git
Online guides and tutorials – general
- An overview of Git tutorials
- Happy Git and GitHub for the useR Somewhat R-centric, but a very accessible introduction to Git.
- Git best practices by Seth Robertson
- Atlassian Git tutorials
- A quick GitHub overview of some Git and GitHub functionality including branching and Pull Requests
Online guides and tutorials – interactive practice
- Git-it – a small application to learn and practice Git and GitHub basics.
- Learn Git branching
- GitHub Skills – short interactive courses on using GitHub, which take place in a GitHub repository.
Further reading
- A Quick Introduction to Version Control with Git and GitHub (Blischak et al. 2016, PLoS Computational Biology)
- Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution (Braga et al. 2023, Methods in Ecology and Evolution)
- Excuse me, do you have a moment to talk about version control? (Bryan 2017, PeerJ)
- Ten Simple Rules for Taking Advantage of Git and GitHub (Perez-Riverol et al. 2016, PLoS Comutational Biology)
6 Workflows and pipelines
- Workflow systems turn raw data into scientific knowledge (Perkel 2019, Nature)
- The nf-core framework for community-curated bioinformatics pipelines (Ewels et al. 2020, Nature Biotechnology)
- Nextflow enables reproducible computational workflows (Di Tommaso et al. 2017, Nature Biotechnology)
7 R
7.1 Books
- R for Data Science (Wickham et al.) – The standard introduction to the tidyverse.
- R for Plant Disease Epidemiology (Emerson Del Ponte et al.)
- Fundamentals of Data Visualization (Claus Wilke) – About principles of good figures rather than code.
- R Graphics Cookbook (Winston Chang)
7.2 Courses and tutorials
- R for Bio Data Science (Technical University of Denmark)
- STAT 545 (Jenny Bryan)
- R Crash Course for Biologists
8 AI
Ohio State resources
Courses and tutorials
- Claude Academy — Multiple free short “courses” (series of videos, mostly) by Anthropic on using AI.
- AI Fluency for Biologists (Gladstone Institutes)
- Large language models explained briefly — 8-minute video.
Further reading – papers
- Ten simple rules for effective use of generative AI for code development in environmental science (King 2026, PLoS Computational Biology)
- Twelve quick tips for AI-assisted coding in science (Bridgeford et al. 2026, PLoS Computational Biology)
- Ten simple rules for optimal and careful use of generative AI in science (Helmy et al. 2025, PLoS Computational Biology)
- Large Language Models for Science and Research: A Practical Guide (Dewar 2026, Zenodo)
- Rethinking bioinformatics expertise in the era of artificial intelligence (Goh et al. 2026, npj Digital Medicine)
Further reading – opinion and news
- PhD training needs a reboot in an AI world (Sen Gupta 2025, Nature)
- Is AI ruining our skills? Early results are in — and they’re not good (Lenharo 2026, Nature)
- Universities are embracing AI: will students get smarter or stop thinking? (Pearson 2025, Nature)
9 General/similar courses and books
Courses
- Practical Computing and Bioinformatics for Conservation and Evolutionary Genomics (Eric Anderson) – Covers the shell, HPC with Slurm, Git, and R.
- Tools for Reproducible Research (Karl Broman, University of Wisconsin–Madison)
- Computing Tools for Data Analytics (Cécile Ané, University of Wisconsin–Madison)
- The Missing Semester of Your CS Education – An MIT course on the shell, scripting, editors, Git, and more.
- Omics Data Analysis (Laurent Gatto, course)
- Introduction to Bioinformatics and Computational Biology by Shirley Liu
- Applied Genomics course by Michael Schatz
- Applied Computational Genomics course by Aaron Quinlan
- Modern Plain Text Computing (Kieran Healy)
Books
- Bioinformatics Data Skills (Vince Buffalo, 2015)
- Computing Skills for Biologists (Stefano Allesina & Madlen Wilmes, 2019)
- A Primer for Computational Biology
- Practical Computing for Biologists (Haddock & Dunn, 2011)
- Computational Genomics with R (Altuna Akalin)
10 Miscellaneous bioinformatics
Websites with workshop materials
Other
- Rosalind – A website with bioinformatics exercises
- Collections of training materials:
- Glittr.org – A searchable index of bioinformatics training materials on GitHub
- Getting started with genomics tools and resources (Tommy Tang)
11 Omics data
Introductory papers
- Working with omics data: An interdisciplinary challenge at the crossroads of biology and computer science (Poinsignon et al. 2023, in Machine Learning for Brain Disorders)
- The principles and applications of high-throughput sequencing technologies (Lee 2023, Development & Reproduction)
- High-throughput sequencing in plant disease management: a comprehensive review of benefits, challenges, and future perspectives (Nizamani et al. 2023, Phytopathology Research)
- New-generation sequencing technology in diagnosis of fungal plant pathogens: A dream comes true? (Aragona et al. 2022, Journal of Fungi)
- Multi-omics revolution to promote plant breeding efficiency (Mahmood et al. 2022, Frontiers in Plant Science)
- Sequencing-based analysis of microbiomes (Pinto & Bhatt 2024, Nature Reviews Genetics)
- Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences (Manzoni et al. 2018, Briefings in Bioinformatics)
- Advancing species conservation and management through omics tools (von der Heyden et al. 2025, Molecular Ecology Resources)
Example dataset
- Two avian Plasmodium species trigger different transcriptional responses on their vector Culex pipiens (Garrigós et al. 2025, Molecular Ecology) — the paper behind the course’s example dataset.