05.01-useful-links.Rmd

# (PART) RESOURCES {-}
# Useful links {#useful-links}

### Data access {- #links-data}

#### Host reference genomes {-}

* **[NCBI Genome](https://www.ncbi.nlm.nih.gov/genome/) (website):**
* **[Ensembl](https://www.ensembl.org/index.html) (website):**
* **[Vertebrates Genome Project](https://vertebrategenomesproject.org/) (website):**

#### Metagenomic data {-}

* **[HoloFood Data Portal](https://www.holofooddata.org/) (website):**
* **[MGnify](https://www.ebi.ac.uk/metagenomics/) (website):**
* **[Earth Hologenome Initiative](http://www.earthhologenome.org/database.html) (website):**

### Documentation {- #links-documentation}

#### Genomics {-}

* **[Data Wrangling and Processing for Genomics](https://datacarpentry.org/wrangling-genomics/) (website):**
* **[Vertebrate Genomes Project assembly pipeline tutorial](https://training.galaxyproject.org/training-material/topics/assembly/tutorials/vgp_genome_assembly/tutorial.html) (website):**


#### Shell command line usage {-}

* **[Introduction to the Command Line for Genomics](https://datacarpentry.org/shell-genomics/) (website):** general overview of basic command line usage.

#### R usage (General usage and programming) {-}

* **[Intro to R and RStudio for Genomics](https://datacarpentry.org/genomics-r-intro/) (website):**
* **[Efficient R programming](https://csgillespie.github.io/efficientR/index.html) (website):** best practices for programming in R.

#### R usage (Graphics and visualisation) {-}

* **[Fundamentals of Data Visualization](https://clauswilke.com/dataviz/) (website):** guide to making visualisations that accurately reflect the data, tell a story, and look professional.
* **[R Graphics Cookbook](https://r-graphics.org/index.html) (website):** a practical guide that provides more than 150 recipes to generate high-quality graphs using ggplot2.

#### Statistics {-}

* **[An Introduction to Statistical Learning](https://www.statlearning.com/) (book):** freely available book about general statistical learning covering regression and classification problems through linear modelling and machine learning.
* **[High dimensional statistics with R](https://carpentries-incubator.github.io/high-dimensional-stats-r/) (website):** virtual lesson specialised in dealing with high dimensional data.