Shelly’s Notebook: Fri. May 31, 2019 Methyl CpGoe analysis

This post is related to github issue #694

The main problem was not being able to combine the ID_CpG_labelled outputs together into one file as Sam was able to do in his original CpGoe analysis using this script.

Yaamini created new CpGoe files specific for CAP, CDS, GENE, Exons, and windows features of the genome mentioned in this post and following this markdown file. After having problems with the script adding ID_CpG file headers and joining the files together, I attempted to run this script

on Mox:

  1. First I copied the data and script over to Mox: – mkdir /gscratch/srlab/strigg/data/Cvirg/FROGER_CAP_CpGoe – cd /gscratch/srlab/strigg/data/Cvirg/FROGER_CAP_CpGoe
    • rsync –archive –progress –verbose yaamini@ .
  2. Next I turned this script (/gscratch/srlab/strigg/data/Cvirg/FROGER_CAP_CpGoe/CAP_CpGoe/ into a Mox job (/gscratch/srlab/strigg/jobs/ and ran it on May 23. It errored out in the join step after 3-4 hours of running and joining 26 of the 90 files.
    • error message: “Broken pipe join –nocheck-order ID_CpG_labelled_all ${file}ID_CpG_labelled “
    • slurm file is here: /gscratch/srlab/strigg/data/Cvirg/FROGER_CAP_CpGoe/CAP_CpGoe/slurm-863764.out
  3. I copied everything to my folder on Gannet
  4. I realized some of the sample names have more than one underscore and having a non-unique column name could have been problematic.
  5. We also learned that certain bash commands (sed and maybe even the join command) may not run the same way on a mac (how Yaamini and I have tried running the script) as they do on a PC (how Sam ran it)
  6. I made changes to the script to

On Ostrich:

  1. I used this jupyter notebook to
    • run the updated script on the CAP data
    • run the updated script on the GENE data to duplicate what Sam already did so I could compare the output of the new script to Sam’s.
  2. QC: In R, I compared output of new script with output of Sam’s orginial script that I reformatted in R.


  • updated script gives same output as original script and can now be used on remaining CpGoe analyses (CDS, Exons, windows).

from shellytrigg

Grace’s Notebook: June Goals

I leave for FHL on June 16th for a 5-week intensive course on the Ecology of Infectious Marine Disease!! Lots to do before I go. ## Taylor Hatchery Work – Continue strip spawn trials – Read more about mechanisms behind geoduck spawning, why KCl works, etc. ## Oysterseed DIA – Revisit and try to get paper finished – Clean up repo

from Grace’s Lab Notebook

Grace’s Notebook: Day 2 Geoduck Stripspawn Project at Taylor – not great results

Today we checked to see what development occurred since Wednesday’s strip spawn and KCl-treatment trials. There were so few eggs in the tripours to begin with, that there ended up being way too small of numbers of D-hinge larvae that I ended up dumping the trial and we’ll have to rethink this and re-do it. Details in post.

Counts using coulter counter

We started out using the coulter counter to see if it would work. We screened the tripours (used 31-33 because those are the extras) on 60micron screens. We realized quickly that there was a lot of larger junk that stayed in the sample, including pollen because the table wasn’t covered over the two days. We then swithed to screening with a 90micron screen into a 60 in order to catch the big stuff. I suspended them in 10ml seawater.

The first counts of the coulter counter were pretty high, but then the next two tripours were very low. Counts listed below:

Counts are #cells/ml in 10ml samples.

Tripour No. Trtmnt Count 1 Count 2 Count 3 Avg
1 0mM KCl 86 75 85 82
16 50mM KCl with hydration step 13 12 11 12
25 80mM KCl 29 43 34 36

The coulter counter was likely counting things that weren’t D-hinge.

Counts by hand

We then decided to do counts by hand. I screened them the same way and suspended them in 10ml seawter just like with the coulter counter.

I sampled out 1ml after mixing well by flipping contatiner upside-down a few times. I put the sample on a slide and put two drops of lugols in order to preserve the organisms so that they wouldn’t be moving around when I was trying to count them.

The counts were also very low. I didn’t count all tripours – I just started out by looking at a bunch of different treatments to see if it was worth me continuing and counting them all. Counts are below:

Counts are #D-hinge/1ml in 10ml samples

Tripour No. Trtmnt Count/ml Total No.
2 0mM KCl 0 0
4 10mM KCl 4 40
8 20mM KCl 6 60
17 50mM KCl + hydration step 7 70
18 50mM KCl + hydration step 10 100
19 60mM KCl 3 30
26 80mM KCl 0 0
27 80mM KCl 3 30
29 50mM KCl without hydration 3 30

The numbers are way too small to have too much meaning. Each tripour originally had ~10,000 eggs, so in terms of percentages, the number of eggs that made it to D-hinge range from 0%-0.01%.

Notes from this trial/things to change for next time

  • Didn’t have a true “negative control”. A true negative control would have been having a tripour of just eggs (no sperm added to fertilize)
  • 10,000 eggs/tripour is sticking with the 10eggs/ml rule that the hatchery has, but maybe it’s too small a scale for capturing any differences in this experiment. Maybe try putting more eggs in the tripours…. or using larger buckets?
  • Keep male and female geoduck separate after biopsy punching gonads to determine sex – may be cause of early polar body sightings (see this post).
  • Use less treatment groups, easier to manage and may be better to start on a much smaller scale, then get more complex as we learn what works

from Grace’s Lab Notebook