Rijksinstituut voor Volksgezondheid en Milieu (RIVM)

Sept 10-11, 2018

9:00 - 17:30

Instructors: Sam Nooij, Anita Schürch, Marieke Dirksen, Dennis Schmitz

Helpers: TBD

General Information

Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.

For more information on what we teach and why, please see our paper "Good Enough Practices for Scientific Computing".

Who: The course is aimed at graduate students and other researchers. You don't need to have any previous knowledge of the tools that will be presented at the workshop.

Where: Room T.007, T-Building (Enter the V-building near the main entrance, go straight ahead past the seal statue, it's behind the staircase), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven. Get directions with OpenStreetMap or Google Maps.

When: Sept 10-11, 2018. Add to your Google Calendar.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed here). They are also required to abide by Data Carpentry's Code of Conduct.

Data

During the course we will use data from a long term evolution experiment published in 2012: Genomic analysis of a key innovation in an experimental Escherichia coli population by Blount ZD, Barrick JE, Davidson CJ, and Lenski RE. (doi: 10.1038/nature11514).

We are using this collaborative document for chatting, taking notes, and sharing URLs and bits of code.

Useful resources and manuals:
FASTA Format Definition
FASTQ Format Definition
SAM/BAM Format Definition
VCF/BCF Format Definition
Trimmomatic Website
FastQC Website

Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Contact: Please email dennis.schmitz (at) rivm.nl for more information.


Surveys

Please be sure to complete these surveys before and after the workshop.

Pre-workshop Survey

Post-workshop Survey


Schedule

All courses, code and examples will be shared after the course using this PLACEHOLDER link.

N.B. The actual schedule may vary slightly depending on the topics and exercises chosen by the intructors.

We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.

Day 1

08:30 Optional: software installation troubleshooting
09:00 Introduction
09:10 Logging onto Cloud
09:20 Introducing the Shell
09:50 Navigating Files and Directories
~10:00 Coffee
10:40 Working with Files and Directories
11:25 Redirection
~12:00 Lunch break
13:00 Writing Scripts
13:40 Project Organisation
14:10 Assessing Read Quality
~15:00 Coffee
15:15 Trimming and Filtering
16:05 Variant Calling Workflow
17:00 Wrap-up
17:30 Finish

Day 2

08:30 Optional: software installation troubleshooting
09:00 Recap of day 1
09:20 Automating a Variant Calling Workflow
~10:00 Coffee
11:00 R for Microbial Genomics, introduction
~12:00 Lunch Break
13:00 R for Microbial Genomics, starting with data
14:00 R for Microbial Genomics, Data Frames
~15:00 Coffee
15:15 R for Microbial Genomics, the dplyr package
16:00 R for Microbial Genomics, data visualization
17:00 Wrap-up
17:30 Finish

Syllabus

The Unix Shell

  • Files and directories
  • History and tab completion
  • Pipes and redirection
  • Looping over files
  • Creating and running shell scripts
  • Finding things
  • Reference...

Programming in R

  • Working with vectors and data frames
  • Reading and plotting data
  • Creating and using functions
  • Loops and conditionals
  • Using R from the command line
  • Reference...