library(renv) # install.packages("renv")
library(dplyr)
deps <- renv::dependencies(path = ".", progress = F, quiet = T)
unique_packages <- unique(deps$Package)
writeLines(unique_packages, here::here("src/packages_use_in_this_quarto.txt"))
data.frame(
Package = unique_packages,
Version = sapply(unique_packages, function(pkg) {
tryCatch(as.character(packageVersion(pkg)), error = function(e) NA)
}),
stringsAsFactors = FALSE
) %>%
#knitr::kable(caption = "List of packages used in this document", row.names = FALSE)
DT::datatable(.,
filter = "top",
class = 'cell-border stripe',
rownames = FALSE
)Bioinformatics Tools
A hands-on course for MSc biology students
Abstract
Student website for the Bioinformatics Tools course: nine sessions introducing the essential command-line, R, and machine-learning tools used to inspect, analyse, and interpret biological data.
Preface
Contributors: Corentin Maslard, postdoctoral researcher in Joop E.M Vermeer lab team
Advisor: Thomas Badet
This website is the course support for Bioinformatics Tools, a practical course for MSc biology students at the University of Neuchâtel. It builds on the material developed by Thomas Badet and reworks it into a simpler, progressive path with prepared datasets and no long installations.
The course uses examples from genomics, microbial ecology, and plant biology, and introduces machine learning as a tool for answering biological questions rather than as a programming topic in isolation.
TipPlease feel free to contact me for:
- Internship
- Information
- Help with your future analyses
NoteMore informations about my work :
Getting started
This site collects the written supports for the nine sessions of the course. Each session has its own practical.qmd chapter with the exact steps to follow during the practical.
- Format: nine sessions, 14:15–17:00 — about 45 min of theory, 1 h 50 of practice, 10 min break.
- Level: deliberately simple and progressive; no prior programming experience is assumed.
- Language: all written supports are in English.
- Data: small teaching datasets are prepared in advance; no long installation or large download during class.
Course programme
- Initial diagnostic and essential commands
- Shell, biological formats, and quality control
- Sequence similarity and phylogeny
- Biological tables and visualisation with R
- PCA and unsupervised learning
- Supervised machine learning and evaluation
- Introduction to deep learning
- Guided project: question, script, and first result
- Guided project: correction, interpretation, and presentation
ImportantAssessment
A short individual Moodle quiz (about 15 minutes) at the end of each session, based on the results actually obtained during the practical. Nine assessments of 10 points; the final project is spread over the last two sessions.
Licence
This document is made available under the terms of the Licence Creative Commons Attribution - Non Commercial Use - Share Alike 4.0 International.
