Learning resources for bioinformatics, genomics and computational biology

Glittr - a large searchable community database of git repositories with bioinformatics training material

DReSA - Digital Research Skills Australasia - an active database of training material and national workshop events

Other Monash groups and pages

• Monash eResearch training page

• Monash NUMBATs R workshops

General programming and command line

In the wet lab, you might need to learn to pipette, use a centrifuge, or maybe run some gel electrophoresis before you can get useful results. In the dry lab, you need to learn to use a computer to automate tasks and analyse data before you can get useful results - often this means telling the computer what to do using plain text.

Linux, the "shell", command line

• https://linuxjourney.com/
• https://sandbox.bio/ - interactive commandline tutorials for bioinformatics
• https://www.edx.org/course/introduction-linux-linuxfoundationx-lfs101x-1
• http://andrewjrobinson.github.io/training_docs/tutorials/unix/
• http://andrewjrobinson.github.io/training_docs/tutorials/hpc/

Git

• https://learngitbranching.js.org/ - a visual, interactive tutorial to help you understand Git

R

MGBP developed R workshops

• Introduction to R is our recommended starting point.

• Programming and Tidy Data analysis in R covers automating tasks such as loading a large set of files, as well as data wrangling and how "tidy" data streamlines visualization and data analysis.

• Linear models in R covers many common statistical tasks in a unified way using "linear models". This will also be useful background knowledge for RNA-Seq analysis, especially with complex experimental designs.

• Introduction to R Shiny covers presentation of your data interactively.

• Working with DNA sequences and features in R with Bioconductor

Introductory R

• Introduction to R - Tidyverse, a workshop developed at WEHI.
• The R for Data Science book is a popular book covering the tidy approach.
• Posit Cloud to try R and RStudio online.
• R programming - coursera: https://www.coursera.org/course/rprog
• Data Carpentry R lessons: https://datacarpentry.org/R-ecology-lesson/ and https://datacarpentry.org/genomics-r-intro/
• It's often useful to generate reports including code and outputs, either using RMarkdown or the newer Quarto system.
• StatsTest (Wayback Machine archive) - which statistical test should you use? (Paul's comment: Don't be overwhelmed, learn to use linear models. Linear models provide a systematic way of thinking about the factors and variables in your experiment, and how they translate into a statistical model and tests. Then if the assumptions aren't quite met for using a linear model, consider one of the specialized methods listed in this page.)

Advanced R

• End-to-end visualisation using ggplot2; https://rviews.rstudio.com/2017/08/14/end-to-end-visualization-using-ggplot2/
• HarvardX biomedical data science MOOC: http://genomicsclass.github.io/book/ (Chapter 5 has good examples of linear model design and contrasts - with diagrams)

Python

Introductory Python

• Monash Data Fluency Introduction to Python Workshop material
  • ... uses some material from: Data Analysis and Visualization in Python for Ecologists
• The 'official' Python tutorial: https://docs.python.org/3/tutorial/
• http://rosalind.info/problems/list-view/?location=python-village
• http://andrewjrobinson.github.io/training_docs/tutorials/python_overview/python_overview/ - more of a quickstart for those comfortable with programming
• Intro to Python: http://introtopython.org/
• Introduction to Data Processing with Python: http://opentechschool.github.io/python-data-intro/
• Python for Everyone (Basic introductory material, through to object oriented programming, interaction with web services, databases, plotting) https://www.py4e.com/lessons
• BE/Bi 103 a: Introduction to Data Analysis in the Biological Sciences (Caltech) - Data Science for Biology, with statistics and visualisation in Python
• Programming in the Biological Sciences Bootcamp notes (Caltech) - a comprehensive introduction to Python programming, with some Pandas, Numpy, Scipy and git thrown in for good measure.

Advanced Python

• Magic methods, context managers (enter, exit for 'with' and more !): https://web.archive.org/web/20161024123835/http://www.rafekettler.com/magicmethods.html
• @property decorators, Descriptors: https://web.archive.org/web/20150407105027/http://intermediatepythonista.com/classes-and-objects-ii-descriptors
• @staticmethod, @classmethod and @abc.abstractmethod
• https://julien.danjou.info/blog/2013/guide-python-static-class-abstract-methods
• Metaclasses: http://eli.thegreenplace.net/2011/08/14/python-metaclasses-by-example and https://stackoverflow.com/questions/100003/what-is-a-metaclass-in-python
• Understanding scope, closures: https://www.farside.org.uk/201307/understanding_python_scope

Visualization

• Introductory interactive visualization using Altair (University of Washington): https://uwdata.github.io/visualization-curriculum/intro.html
• PCA for Data Science: https://pca4ds.github.io/

General Bioinformatics and Computational Biology

• EMBL-ABR training videos: https://www.youtube.com/channel/UC5WlFNBSfmt3e8Js8o2fFqQ/videos
• Bioinformatics Workbook: https://bioinformaticsworkbook.org/#gsc.tab=0
• SequenceEng - resource of most seq applications and analysis pipeline: http://education.knoweng.org/sequenceng/

Theory

• Rosalind - problem solving exercises in computational biology to learn the fundamentals
• JHU Computational Genomics notebooks - in depth code examples to help understand how genome short read alignment and assembly works. Burrows-Wheeler Transforms and de Bruijn graphs.

Genomics on the commandline

• Data Carpentry Genomics Workshop - a great starting point for learning genomics on the commandline.
• Computational Genomics Tutorial - based on the Massey University Genome Science course taught by Sebastian Schmeier. A very clear tutorial series that covers installing and running tools for doing NGS read quality control, genome assembly and mapping, annotation, variant calling and interpretation. Light on theory, but a good starting point for working through the mechanics of genomics on the commandline.
• Sanger Pathogen Informatics Training - commandline tutorials covering various analysis on microbial pathogens. Structured as a series of notebooks to follow, starting here.

RNA-seq (bulk)

Some of these tutorials and guides start with raw FASTQ reads, through to differential expression analysis. Others begin with the counts matrix.

• MGBP RNA-Seq workshop (2024)

• Sydney informatics hub RNAseq tutorial 2023 - starting from raw reads with nf-core/rnaseq, through to differential expression analysis with R & DESeq2, and functional enrichment.

• Introduction to differential gene expression analysis using RNA-seq (Dündar, Skrabanek, Zumbo @ Cornell) - a very nice RNA-seq overview and tutorial, from RNA extraction and experimental design to differential gene expression analysis, with Unix commandline and R exercises.

• RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR - Law et al, 2016 - a good practical tutorial for edgeR and limma, starting from a counts matrix.

• From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline

• A guide to creating design matrices for gene expression experiments

• A big list of RNASeq links - nicely organized into sections like 'normalization' and 'batch effects': https://github.com/crazyhottommy/RNA-seq-analysis

• http://master.bioconductor.org/help/course-materials/2015/Uruguay2015/V6-RNASeq.html

• https://diytranscriptomics.com/ - largely video-based tutorial series for learning RNA-seq analysis using R.

• Harvard Chan core RNA-seq beginner and Salmon+DESeq2 courses

• Case study: using a Bioconductor R pipeline to analyze RNA-seq data

• https://www.ebi.ac.uk/training/online/course/ebi-next-generation-sequencing-practical-course/rna-sequencing/rna-seq-analysis-transcripto-0

• RNASeq tutorial from UOregon: https://github.com/griffithlab/rnaseq_tutorial/wiki

• http://www.ngscourse.org/Course_Materials/alignment/tutorial/example.html

• COMBINE- http://combine-australia.github.io/RNAseq-R/

• http://www.rnaseqforthenextgeneration.org/protocols/index.htm

• https://github.com/MaayanLab/intro-rnaseq-jupyter

• https://newonlinecourses.science.psu.edu/stat555/node/78/

• https://mikelove.github.io/counts-model/index.html

RNA-seq (single cell)

• MGBP single cell RNA-Seq workshop (2024)

• Orchestrating Single-Cell Analysis with Bioconductor covers the Bioconductor way of doing single cell. But many people prefer to use Seurat instead. Seurat also has many useful vignettes.

Metagenomics

• Data processing and visualization for metagenomics - a Carpentries workshop in incubation. May have some rough edges, but it's already looking quite good.

• Orchestrating Microbiome Analysis covers the Bioconductor way of doing microbiome analysis.

Functional Enrichment Analysis

• MGBP + Sydney Informatics Hub functional enrichment workshop (2024)

Papers that could be of interest for functional enrichment analysis.

• Null hypothesis in GSEA https://www.frontiersin.org/articles/10.3389/fgene.2020.00654/full
• Survey of ORA and FCS (including recommendations) http://ziemann-lab.net/public/kaumadi/manuscript.html
• Univariate and Mutivariate FCS - https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1790-4
• Multi contrast and multi-omics FCS- Mitch R package (https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-06856-9)