Grace’s Notebook: Sampling for D-hinge development

Today I sampled the buckets for D-hinge development. Overall numbers were low, considering that we put 300,000 eggs in each bucket. The best group was treatment group 2: no KCl exposure, but fertilized. Details in post:

Procedure:

Started by checking a few buckets by screening on 20um, and sampling out 1ml from the suspended larvae in ~200ml. Used lugols to stop the larvae from moving around.

Numbers were very low, but I continued looking anyway and sampled all buckets.

I screened all buckets on 20um screen, then suspended the eggs using a suirt bottle filled with filtered seawater into a tripour. Then I added enough FSW to reach certain total volumes (detailed in results), then sampled out 1ml after mixing the larvae around. Used a few drops of lugols to stop movement, then counted the number of D-hinge larvae in that 1ml.

Results:

Spreadsheet: here

img

The result summary table is showing the treatment groups (KCl dose in mM, duration of dosing, and whether or not the group was hydrated and/or fertilized), and the average percent of development to D-hinge (averaged between the two replicates).

Note: 19 did not have a replicate.

Thoughts on experiment:

Regarding the low D-hinge counts relative to stocking amount:

  • Maybe there were triploid eggs? Maybe that’s why numbers were so low?
  • Maybe eggs got burst during the treatment process? When Benoit and I first picked up the screened silos out of the FSW in order to dose the eggs, it took a very long time for the water to drain out through the screen. When we put the screened silos back into the FSW after their respsective dosing durations were finished, it was really fast to drain. Maybe the pressure from the draining water burst the eggs, and that’s why numbers were so low?

Regarding D-hinge in negative control (1):

  • hermaphrodite?
  • parthenogensis?
  • Could I have possibly mixed up group 1 with group 3? I honestly extremely doubt that because I stocked group 1 (negative control) while all the others were being fertilized on a separate table.
  • Contamination of sperm? possible. I was very cautious and even wore gloves, but there still could have been contamination.

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Sam’s Notebook: Metagenomics – Taxonomic Diversity and Sequencing Coverage with MEGAHIT BLASTx and Krona Plots

After a meeting on this project around the middle of May, we decided to try various approaches to assessing the metagenome. One aspect was to add coverage sequencing coverage information to our BLASTx taxonomy visualizations. I used the MEGAHIT coverage info from 20190327 and the subsequent BLASTx data from 20190516.

Briefly, I parsed out and joined the data to generate the appropriate input file needed for visualizations using Krona Tools and then ran the ktImportTaxonomy Krona Tools program. This is all detailed in the Jupyter Notebook below.

Jupyter Notebook (GitHub):

NBViewer for viewing notebook:

Yaamini’s Notebook: DML Analysis Part 36

Reworking DMR

Changing methylKit parameters

One thing Mac mentioned to me at FROGER was the use of the cov.bases in tileMethylCounts. The argument cov.bases allows me to set the minimum number of bases to cover in a window. Looking at Mac’s salmon paper, I saw that she set cov.bases to 1, which is different than the default 0. In my R Markdown file, I also set cov.bases to 1 and created 100 bp, 500 bp, and 1000 bp DMR. All of the data and figures I generated are tagged with the date “2019-06-05” and can be found here.

Table 1. Number of DMR identified using different window sizes. Step size and window size were equal.

Window Size (bp) Number of DMR
100 71
500 12
1000 5

Visualizing DMR in IGV

My gut feeling was to go with the 100 bp DMR, just because it gives me a larger dataset to work with. Obviously gut feelings aren’t enough, so I visualized the different DMR sizes in IGV.

Screen Shot 2019-06-11 at 3 51 56 PM

Screen Shot 2019-06-11 at 3 52 13 PM

Screen Shot 2019-06-11 at 3 52 49 PM

Figures 1-3. 100 bp, 500 bp, and 1000 bp DMR tracks in IGV.

I found that the 100 bp DMR more consistently matched with the location of DML on various chromosomes (Figures 1-3). For example, there would be a genomic region with no DML, but a 500 bp DMR. When I looked closely at these DMR, I found that these were regions with one or two CpG loci with data for only a few samples. Some chromosomes did not have any DMR when looking at the 500 bp or 1000 bp tracks even though they had DML. After looking at the data in IGV, I trust the 100 bp DNMR more, so I’ll continue to use that for analyses. I quickly generated separate BEDfiles for hypermethylated and hypomethylated DMR so I could compare that to the breakdowns I had for hyper- and hypomethylated DML. Out of 71 total DMR, 37 are hypermethylated and 34 are hypomethylated.

Characterizing overlaps with DMR

I returned to this Jupyter notebook to characterize DMR overlaps with various genome feature tracks. I looked at overlaps for all DMR, as well as hyper- and hypomethylated DMR separately.

Table 2. Overlaps between DMR and various genome feature tracks.

Feature Hypermethylated DMR Hypomethylated DML All DMR
Genes 33 33 66
Unique Genes 33 33 65
Exons 19 19 38
Introns 27 24 51
Transposable Elements (All) 3 8 11
Transposable Elements (C. gigas only) 3 6 9
Putative promoters 1 7 8
Other 2 0 2

Correcting DML chi-squared tests

Before creating DMR figures, I decided to take a quick DML detour and address a comment Steven gave me. When I initially conducted chi-squared tests with DML, I set the methylated CpGs as the background. While this is an interesting comparison, the methylated CpGs are not the appropriate background, since methylKit pulls DML from MBD-enriched loci. In this R Markdown file, I conducted chi-squared tests for MBD-enriched vs. DML and found significantly different distributions (chi-squared statistic = 342.69, df = 4, p-value < 2.2e-16). I also created a figure for this comparison.

Screen Shot 2019-06-11 at 6 12 26 PM

Figure 4. Comparing overlap proportions between MBD-enriched loci and DML.

DMR overlap figures

Since DMR are 100 bp and loci are well…1 bp, I decided that comparing distribution of loci with distribution of DMR did not make sense. If I were to do a chi-squared tests, I’d need to use the appropriate background: all the tiles generated by methylKit in the sliding window analysis. These 100 bp windows are all possible DMR. I exported all the tiles from methylkit in this R Markdown file. I then returned to this Jupyter notebook to characterize the locations of the DMR background.

Table 3. Overlaps between DMR background and various genome feature tracks. There were 152,226 possible tiles.

Feature DMR Background
Genes 142153
Unique Genes 11578
Exons 92552
Introns 93707
Transposable Elements (All) 25117
Transposable Elements (C. gigas only) 20228
Putative promoters 8238
Other 4649

I added the background overlap and DMR overlap counts to this table. I found that the distribution of the DMR background and DMR themselves were not significantly different (chi-squared statistic = 5.8078, df = 4, p-value = 0.214). I did, however, get a warning that the chi-squared approximation may be incorrect.

While Mac didn’t do a chi-squared test with her salmon DMR, she did create plots that compared the proportion DMR in various genomic features with the DMR background. I decided to follow her precedent and do the same in this R Markdown file.

Screen Shot 2019-06-12 at 11 10 54 AM

Figure 5. Comparing overlap proportions between the DMR background and DMR. There were no significant differences in the distribution.

Going forward

  1. Create an annotated table of DML and DMR
  2. Conduct a gene enrichment for DML and DMR
  3. Work through gene-level analysis
  4. Update methods and results
  5. Update paper repository
  6. Outline the discussion
  7. Share draft paper at the next Eastern Oyster Project Meeting
  8. Write the discussion
  9. Write the introduction
  10. Revise my abstract
  11. Share the draft with collaborators and get feedback
  12. Post the paper on bioRXiv
  13. Prepare the manuscript for publication

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Grace’s Notebook: Geoduck Egg Development – 3 days later

Today we took a look under the scope of the eggs in the silos from Friday’s egg development trial (Notebook post).

Looking at the eggs

I didn’t really look at the ones that were fertilized.

Benoit and I looked at eggs from the three silos that were not fertilized (although I did see polar bodies, cleavage).

Treatment group (all not fertilized) D-hinge? Notes
H50 yes don’t look great; lots of ciliates; some trochophores
C maybe lots of ciliates; remnants of trochophores; potential D-hinges, but hard to know because a lot of things could have been eaten
50 no some trochophores; lots of ciliates

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Grace’s Notebook: Day 1 of Trial 2 of Geoduck Stripspawn experiment – KCl dose + duration

Today was Day 1 of the second trial of geoduck stripspawn. We created an experimental design that takes into account both KCl dosage, and duration of the dose exposure. Overall things went pretty well (details in post), and on Wednesday, I’ll check for D-hinge development.

Google doc with plan

(written in prep for the day, things changed and will be noted in this post)

8:30am – Pt Whitney

Started at Pt Whitney, biopsy punching gonad tissue and checking for sex and ripeness.

I started out getting two or three males, and used a new biopsy punch with each geoduck. I placed the biopsied males in the right tank and grabbed new geoduck to biopsy from the left tank.

I then found two females, and then I found one that looked like a hermaphrodite, which was pretty cool! I’ve never seen that before:

img

Then I kept looking and finally found another female, though it may not have been as ripe as the other two. I left a little after 9:45am.

10:15am – Taylor Hatchery

I started out with cleaning the work area, cleaning the 20 20um screened mini-silos, gathering materials. Benoit, Michelle, and Molly helped by getting a filtered saltwater line available for me to use in the work area.

Once I had the filtered saltwater, I started setting up the tripours with the different mM KCl dosages, as well as some next to them full of filtered saltwater. While I was doing this, Benoit was setting up buckets in the bucket room with airstones and water in which the fertilized eggs will grow out in at the end of the dosing and fertilizing.

Setting up the dosages:

I filled the tripours up to 800ml (any more than that and the water will spill over once the 20um silo is placed in). When making the dosed saltwater, I made them as though they’d be filled to 1000ml so that the math would be easier.

I made them in a 3L pitcher, so I did them in sets of two. Example: to make the 20mM, I placed 20mL 2M KCl and 1960ml filtered saltwater. I then dispersed it into two tripours, up to 800ml. I did that again, and then moved on to the next dose.

KCl mM 2 M KCl stock to add (ml) ml saltwater to add
20 10 980
50 25 975
60 30 970
80 40 960

Tripour treatments:
img

Check female geoduck ploidy

Before dissecting the females and stripping the eggs, I measured the shells:

female number length width ratio diploid? (yes if ratio < 1.62)
1 127 73 1.739 no
2 121 71 1.704 no
3 124 80 1.55 yes

Benoit ran the ctenidia samples from all three females on the flow (? is that what it’s called) to check for ploidy. All three had the same ploidy, but it isn’t known if they are all three diploid, or all three triploid. Benoit believes they are likely all three diploid.

Stripspawn females

I dry stripped some eggs from all three directly into the screened silo sitting in tripour 19 (50mM with no hydration), and timed for 20 mins.

During those 20 mins, I stripped eggs from the three females into some saltwater. Benoit helped get a lot of eggs out. One female was much more ripe than the other two. Of the remaining two, one female was not very ripe at all unfortunately.

Benoit helped with screening and cleaning the eggs, and counted them using the coulter counter. The first counts showed that there was about 4 million eggs in the 3L pitcher.

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Kaitlyn’s notebook: ANOVA on heath stack juveniles

I preformed an ANOVA on the lengths of the juveniles in the heath stacks at Pt.Whitney. I did a post-hoc test (Tukey HSD) to determine significance between he treatments.

length-treatment

length-treatment

homogenization

With blocking the anova gives a p=0.028 for treatment and p=0.245 for tray.

Here is the Tukey HSD p-values on the anova with blocking:

Tukey multiple comparisons of means
95% family-wise confidence level

AE-AA 0.54267725
EA-AA 0.25261761
EE-AA 0.72476397
EA-AE 0.91813748
EE-AE 0.10032636
EE-EA 0.03504769

H0_T-H0_B 0.9999666
H1_B-H0_B 0.9999930
H1_T-H0_B 0.9303406
H2_B-H0_B 0.9999448
H2_T-H0_B 0.9970459
H3_B-H0_B 0.9994060
H3_T-H0_B 0.9988241
H1_B-H0_T 0.9980676
H1_T-H0_T 0.8107845
H2_B-H0_T 0.9963141
H2_T-H0_T 0.9703042
H3_B-H0_T 0.9875827
H3_T-H0_T 0.9999998
H1_T-H1_B 0.9588824
H2_B-H1_B 1.0000000
H2_T-H1_B 0.9996848
H3_B-H1_B 0.9999842
H3_T-H1_B 0.9708530
H2_B-H1_T 0.9898375
H2_T-H1_T 0.9991863
H3_B-H1_T 0.9962770
H3_T-H1_T 0.4563048
H2_T-H2_B 0.9999849
H3_B-H2_B 0.9999999
H3_T-H2_B 0.9641553
H3_B-H2_T 0.9999998
H3_T-H2_T 0.8273796
H3_T-H3_B 0.9074722

Tank Treatment
H0_T EA
H0_B EE
H1_T AE
H1_B AA
H2_T EA
H2_B AA
H3_T AE
H3_B EE

Laura’s Notebook: June 2019 goals

Accomplished last month:

O. lurida 2017-2018 project (OA/Temp carryover)

  • Revised MS based on co-author feedback and for general improvements
  • Sent for 2nd round of comments.

O. lurida 2018 project (Temp/Food carryover)

  • Finished 1st draft discussion
  • Correlated CV for larval shell length/width with # larvae released that day (is CV higher with more larvae?) Answer: no.

Bypass & admin:

  • Compiled draft application, including draft dissertation
  • Scheduled committee meeting

NSF GRFP

  • Met with Krista N. re: NSF GRIP, interesting Dungeness crab project looking at larval epigenomes in various pCO2
  • Submitted NSF GRFP funding request for next year (final year)

Oly methylation data:

  • Pulled matrices for Katherine

Oly QuantSeq data & future steps:

  • Expanded plan of attack. Would like to include some juvenile Port Gamble whole-body samples to compare those with high pCO2 parental histories vs. no history, in the location where parental history correlated with survival

Goals / To Do List

Admin

  • Prepare committee meeting presentation, meeting agenda, list of items needed / questions
  • Revise bypass application based on committee input
  • Submit bypass
  • Schedule qualifying exam
  • NSA quarterly newsletter

O. lurida 2017-2018 project (OA/Temp carryover)

  • Write cover letter for journal submission
  • Revise MS based on 2nd round of co-author feedback
  • Submit!

O. lurida 2018 project (Temp/Food carryover)

  • Calculate degree-days until larval release onset for all spawning tanks (if possible).
  • Figure out how to do broodstock survival analysis, then do it
  • Write Intro
  • Finalize plots
  • Send to co-authors

Oly QuantSeq:

  • Start testing QuantSeq analysis pipeline with Oly genome, Salmon (removing the multiple read/transcript auto-correct), Trinity’s isogroup designation file.
  • Schedule benchwork, order supplies

Oly epigenetics project

  • Wrap my head around action plan, steps for analysis

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