Yaamini’s Notebook: October 2019 Goals


September Goals Recap

Virginica Gonad Methylation:

  • I HAVE A FULL DRAFT! Planning on submitting by the end of the week.

Gigas Gonad Methylation:


  • Registered for WSN and ASLO
  • Sent out a poll to committee members for to schedule my committee meeting

October Goals

Virginica Gonad Methylation:

  • Create new figures based on edits
  • Address all edits and send out for another review
  • Clean up repository and add relevant supplementary material
  • Rejoice.

Gigas Gonad Methylation:

  • Annotate genes with DML with GOslim terms
  • Attempt GO-MWU enrichment
  • Conduct DMG analysis
  • Figure out if more samples shouldbe sequenced
  • Prepare presentation for WSN


  • Finalize sample preparation flowchart for methylation analysis
  • Figure out next steps for C. virginica sperm analysis
  • Have a committee meeting
  • Think about timelines for qualifying exams and other deliverables

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Grace’s Notebook: October 2019 Goals


  • Work on paper
  • Pick samples accounting for temperature and infection status from master-qubit-sample.csv
  • Assemble transcriptomes, annotate etc. once new data arrives (should be any day now!)

Oysterseed project

  • Finish paper and have clean repo by October 18th!

MS duties

  • Finish my MS proposal (include oysterseed project as a chapter)
  • Prepare a timeline of project and presentation on current statuses and results
  • Have my first committee meeting by the end of October

OneHealth Certificate duties:

  • Do well in my current two classes: ENV H 586 and EPI 511
  • Meet with Dr. Peter Rabinowitz to learn about all requirements for certificate
  • Start coming up with a plan for a capstone project

Looking back on September:


  • I kept waiting for data – never arrived
  • I finished up preparation for PCSGA and presented (details on all I did: here)
  • Still haven’t interviewed Genevieve, but I WILL be interviewing Shanelle this Thursday before she heads back to the East Coast now that all of her labwork is done here

Oysterseed project

  • I did publish RAW and *.elib files to PRIDE (will find out to officially share that published data in paper soon)

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Shelly’s Notebook: Mon. Sept. 30, Geoduck Genome

Genome DMR analysis

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Shelly’s Notebook: Mon. Sept. 30, Juv. Geoduck Var.low v amb Respirometry

Ran respirometry analysis of the three trials I’ve done so far.

  • markdown report here: Resp.Calculations.ST.md
  • conclusions:
    • there was a significant parental effect from the last trial (which was 2 weeks of exposure) when the data were normalized by wet weight.
    • the var.low parental history animals seem to show less variation in their metabolic rates compared to amb parental history. They also show slightly reduced rates
    • I’m not sure how we determine if this is an advantage or disadvantage. Does reduced metabolic rate mean the animals can go into a ‘hibernation state’ or does it mean they can’t adequately supply their demands?


More details:

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Adding lines to barplots + 2 y-axes

Explanation as to why base plot should be saved as a separate object and referenced in subsequent plots: https://www.r-bloggers.com/adding-lines-or-points-to-an-existing-barplot/

Good code example: https://stackoverflow.com/questions/15277712/adding-lines-to-barplot-in-a-different-y-axis-in-r

Grace’s Notebook: Oysterseed DIA – trying to characterize list of detected proteins in relation to _C. gigas_ proteome

I’m still working on wrapping up the results and discussion of this paper. Steven recommended I look into characterizing the list of detected proteins (2,808) in relation to the whole C. gigas proteome. Detailed in post:

DAVID (from 9/27/19 post)

Gene list: Uniprot accession #s from 2,808 detected proteins –> Cgseed-protcomp-annotation-name.sorted
Background: Uniprot accession #s from C. gigas proteome –> 0327-cgseedblast-sprot.tab

Resulting file: david-diffex-proteome.txt

Excel to pare down DAVID results:

Steven recommended only looking at DAVID GO IDs that had <0.05 FDR.
New list –> 108 GO IDs


I put the 108 GO IDs and their respective fold enrichment values into REViGO (0.7 allowed similarity; numbers associated with GO terms are other than -pvalues, where larger is better) and got the following:


I put them in the paper in the results section and basically said that the most enriched GO processes were to do with general functioning and growth…

Paper writing:

I’ve been woring on clarifying things in the Methods, and working on the discussion. All still very much “work in progress”.

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