RADCamp NYC 2019 Part II (Bioinformatics)

Day 1 (PM)

Overview of the afternoon activities:

• ipyrad CLI assembly of simulated data Part II
• Coffee break
• Look at the real data
• Assemble real data

Participant intros Part II

1 minute/1 slide participant intros

ipyrad CLI simulated data assembly Part II

Lead: Isaac

ipyrad CLI Part II

Coffee break

Look at the real data we generated

Lead: Deren

• Now we will move to the real data
• Covering i7 demultiplexing (Mostly a lecture based thing, w/ an introduction to API mode).
• Show some results from the real demux process.

Form groups and assemble real data

Last hour at the end of the 1st day. Form the groups at the end of the day organized around the new datasets. The following file indicates the group membership as well as an ip address of a VM instance running on the google cloud infrastructure which you will use to perform the assembly and analysis.

RADCamp groups for assembling and analysing the real data

New: follow this link to open instructions in slide form

Slide instructions to start empirical assemblies

older instructions

• Form your groups and brief introductions
• Open a browser tab and go the ip address indicated in the file above. The password for your cloud instance will be written on the whiteboard.
• Open a new terminal window and cd ipyrad-workshop.
• Make a new directory for your 3RAD assembly: mkdir <assembly_name>.
• Make a new directory for fastq results: mkdir fastq_out
• cd fastq_out
• Run fastqc on on the real data, passing in the directory of the 3RAD data for your group, which will be of the form /media/RADCamp/<username>/raws/*R1* and /media/RADCamp/<username>/raws/*R2* replacing the username with the last name of the participant in your group who generated 3RAD data (should take 5-10 minutes per file).
• Examine the results of fastqc by opening the ~/ipyrad-workshop/<assembly-name>/fastqc_out/\*.html files in the jupyter notebook browser.
• Go back to the terminal and cd ~/ipyrad-workshop/<assembly_name>.
• Create a params file for the real data (ipyrad -n <assembly_name>).
• Update your params file as necessary including the correct overhang sequences and read trimming and adapter filtering settings based on the results from fastqc.
• Also, based on preliminary anaylsis, set max_barcodes_mismatch to 2.
• Launch ipyrad steps 1-7
• Go to the mixer and eat pizza and socialize! The results will be done tomorrow.

If you are very curious you can check out how the google cloud infrastructure was configured for this workshop: Google cloud setup