Grace’s Notebook: Crab Project – What I did for PCSGA 2019

In this post I will detail what I did for PCSGA 2019. I did some new analyses and found some new results from our first assembled transcriptome.

New BLAST

Steven made a BLAST database from the Dinophyceae proteins (Dinophyceae is class under which dinoflagellates are), and did a BLASTx against the first assembled transcriptome (Day 26, infected and uninfected, cold and ambient).

I used this script to separate the transcriptome into putative crab and putative Hematodiniium genes: sep_crab-hemat-genes.Rmd

That script resulted in the following files:

Annotating BLAST outputs

In jupyter notebooks, I annotated the two blastx outputs (crab and Hematodinium) with GOslim terms.

I then used the following R script to create files for creating pie charts for biological process GO slim terms:
091519-crab-hemat-GOslim.Rmd

The script resulted in the following files:

Creating pie charts

I used the count.csv files to create pie charts in google sheets:

For the talk, I went more in-depth into the “stress response” slice in the crab GOslim pie. I made a table of some crab genes with notes on their names and functions, and links to the uniprot database on those genes:

The ones highlighted are the ones I chose to talk about in the talk.

The talk and slides

Link to final google slides: Crandall_Wed_1645

Link to slidedeck on figshare: Effects_of_Bitter_Crab_Disease_on_the_gene_expression_of_Alaskan_Tanner_Crabs

Thoughts on talk

It was supposed to be 12 minutes, with 3 minutes for questions. I have no idea how much time I took, but I did get 2 clarifying questions at the end of the talk.

It was my first time presenting at a conference, and my slot was on day 2 (Wednesday) at 4:45pm. It was a struggle to stay energized and it was also a struggle to stay calm – I was really nervous!

I think I had good background information and that I explained things fairly well, but I wish I had gone a little more in depth into the genes that we found. I also think that for next time, I need to find ways to combat the nerves because they got a bit in the way of my ability to speak at the beginning – I collected myself well-enough after the third slide or so… but I want to improve!

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Shelly’s Notebook: Tues. Sept. 24, Geoduck Broodstock Histology

This post is in reference to the histology done on the Fall-Winter 2018-2019 Broodstock conditioned in constant low pH. This is data analysis for the manuscript on pH effect on reproductive development

Past histology analysis

Scoring females with imageJ

Analysis of scores

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Shelly’s Notebook: Mon. Sept. 23, Geoduck Broodstock Histology and Juv. low pH DMRs

Broodstock histology

Met with Kaitlyn in the am and came up with the following plan for scoring gonad histology:

  1. Females:
    • quantify follicle area
    • quantify egg area
    • calculate egg/follicle ratio
    • calculate follicle/tissue ratio
  2. Males:
    • quantify acini/tissue ratio
    • quantify spermatagonia/spermatid ratio (dark purple to light purple)

Comparing allc DMRs and 5x cov files

  • Steven’s 5x cov files:
    • still unsure about what happened in the strandedness code
    • include bases where MAPQ < 30
  • Allc files generated by methylpy (here):
    • filtered for bases with MAPQ >= 30 (–min-mapq 30 default)
  • Methylkit processBismarkAln default includes a ‘minqual’ filter for MAPQ >= 20
  • Not sure if [DMG pipeline] includes a MAPQ cutoff

CONCLUSION:

  • whatever files we use to validate our DMRs, DMLs, or DMGs, they must be filtered the same way.
    • Otherwise differences may not be apparent when all reads are included…

Some lit on whether to use a MapQ score threshold or not:

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