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---
title: "Home"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
My aim for this workshop is to introduce computational tools and demonstrate how they can be used to help promote reproducibility when performing bioinformatic analyses. Many of these tools help adhere to these [Ten Simple Rules for Reproducible Computational Research](https://doi.org/10.1371/journal.pcbi.1003285):
* Rule 1: For Every Result, Keep Track of How It Was Produced
* Rule 2: Avoid Manual Data Manipulation Steps
* Rule 3: Archive the Exact Version Versions of All External Programs Used
* Rule 4: Version Control All Custom Scripts
* Rule 5: Record All Intermediate Results, When Possible in Standardised Formats
* Rule 6: For Analyses That Include Randomness, Note Underlying Random Seeds
* Rule 7: Always Store Raw Data behind Plots
* Rule 8: Generate Hierarchical Analysis Output, Allowing Layers of Increasing Detail to Be Inspected
* Rule 9: Connect Textual Statements to Underlying Results
* Rule 10: Provide Public Access to Scripts, Runs, and Results
I will be talking about [Docker](docker.html), [Conda](conda.html), and [workflowr](https://jdblischak.github.io/workflowr/articles/wflow-09-workshop.html). I share some of my thoughts on reproducible bioinformatics in [this post](https://davetang.org/muse/2019/12/04/reproducible-bioinformatics/) on my blog.