Sam’s Notebook: DNA Isolation and Quantification – C.bairdi Hemocyte Pellets in RNAlater

Isolated DNA from 22 samples (see Qubit spreadsheet in “Results” below for sample IDs) using the Quick DNA/RNA Microprep Kit (ZymoResearch; PDF) according to the manufacturer’s protocol for liquids/cells in RNAlater.

These samples were from an RNA isolation on the following date:

Brief rundown of method:

  • Used 35uL from each RNAlater/hemocyte slurry.
  • Mixed with equal volume of H2O (35uL).
  • Retained DNA on the Zymo-Spin IC-XM columns at 4oC for isolation after RNA isolation.
  • DNA was eluted in 15uL H2O

DNA was quantified on the Roberts Lab Qubit 3.0 using the 1x DNA High Sensitivity Assay (Invitrogen), using 1uL of each sample.

Sam’s Notebook: qPCR – C.bairdi RNA Check for Residual gDNA

Previuosly checked existing crab RNA for residual gDNA on 20200226 and identified samples with yields that were likely too low, as well as samples with residual gDNA. For those samples, was faster/easier to just isolate more RNA and perform the in-column DNase treatment in the ZymoResearch Quick DNA/RNA Microprep Plus Kit; this keeps samples concentrated. So, I isolated more RNA on 20200306 and now need to check for residual gDNA.

Used 2ng of RNA (1uL) in each reaction. A 5uL dilution of each sample was made to a concentration of 2ng/uL. The decision for this quantity was based on the amount of RNA we might use in a reverse transcription reaction (50ng/25uL) and the volume of the resulting cDNA we’d run in a qPCR reaction (1uL). Calculations and the qPCR results (Cq values) are below (Google Sheet).

All reactions were run with 2x SsoFast EVA Green qPCR Master Mix (BioRad) on the Roberts Lab CFX Connect qPCR machine.

All samples were run in duplicate. See the qPCR Report (in Results section below) for plate layout, cycling params, etc.

Master mix calcs are here (Google Sheet):

Shelly’s Notebook: Mon. Mar. 9, 2020 Geoduck Sig. DMR heatmaps with group means

Plotting % methylation of DMRs ID’d with ANOVA

I added lines 312-365 to this R markdown script MCmax30_asinT_groupStats.Rmd to plot group means of % methylation for each of the 4 comparisons (all ambient samples, all day 10 samples, all day 135 samples, and all day 145 samples).

I changed the heat colors to match more closely with the heatmaps Hollie has been generating. We determined that row scaling in the heatmap is the best way to visualize group differences in methylation for each comparison and that pheatmap defaults to no scaling

Significant DMRs from all ambient samples: amb_MCmax30DMR_Taov0.1_GROUPmean_heatmap2.jpg

Significant DMRs from all Day 10 samples: d10_MCmax30DMR_Taov0.1_GROUPmean_heatmap2.jpg

Significant DMRs from all Day 135 samples: d135_MCmax30DMR_Taov0.1_GROUPmean_heatmap2.jpg

Significant DMRs from all Day 145 samples: d145_MCmax30DMR_Taov0.1_GROUPmean_heatmap2.jpg

Finally I created a figure for the manuscript with these heatmaps: asinT_groupStats_heatmaps.jpg

Next steps are to:

  • determine from GO enrichment which DMRs in which genes are mainly contributing to enriched terms
  • compare DMRs with Hollie’s DMGs and determine overlap (will do this at meeting on Friday)
  • compare GO enrichment results for DMRs with Hollie’s results for DMGs
  • There is defintely a difference in the number of DMGs identified by the binomial glm that Hollie ran (>1000 gene significant at FDR adj. p value < 0.05) and the number of DMRs identified by the ANOVA I ran (38 DMRs signficant at uncorrected ANOVA p value < 0.1). Determine if a different method (e.g. binomial glm) should be used for identifying significant DMRs.

from shellytrigg

Shelly’s Notebook: Sat. Mar. 7, 2020 Geoduck DMR GO analysis

GO Analysis of DMRs from 11/02/2019 analysis

DMRs were identified as described here:

Created Rmarkdown script (20200306_anno.Rmd) to perform GOseq on DMRs based on Hollie’s script (GM.Rmd).

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Laura’s Notebook: Oly OA RNA isolation – juvenile ctenidia

I’m done with my adult ctenidia & larvae libraries, and have enough kit leftover for ~17 more samples. I’ve decided to prep a few of my juvenile samples, which were collected at the end of the summer deployment. It could be very interesting to assess differences in juveniles and whether they are similar to those observed in the parents that were directly exposed.

I’m doing the whole body samples collected from the Hood Canal and Fidalgo Bay populations after they were deployed in Port Gamble Bay. I have n=4 per population and parental pH treatment (high or ambient). I had wanted to do ctenidia tissues, but then I checked out the frozen samples and noticed that they definitely are not just gill – lots of mantle tissue in there too. Therefore, I decided to do whole-body samples to try to standardize the tissue type.

Step 1: Homogenize tissue (March 6th, 2020)

Need: LN, dry ice, bleach, DI water, mortar + pestle, metal spatulas

  • Added 1mL RNAzol to 1.5 mL microcentrifuge tubes.
  • Cleaned mortars, pestles, and metal spatulas. Did this by cleaning under hot water, soaking in 10% bleach/DI solution for a minimum of 10 minutes, rinsing thoroughly with DI water, then rinsing with 190 proof ethanol and letting dry.
  • Put tubes with RNAzol on scale. Ground tissues to powder, scraped with metal spatula and carefully transferred powder to tube. Added approximately 50 mg.
  • I did 8 samples at a time (the # of mortar+pestle kits I have), then cleaned and repeated with another 8.

Step 2: RNA isolation (March 7th, 2020)

Need: RNAzol, DEPC-treated water, isopropanol, 200-proof ethanol, 1.7 mL tubes

Followed the RNAzol® RT RNA Isolation Reagent protocol for Total RNA isolation, using half of my homogenate, so 500 uL.

from The Shell Game

Sam’s Notebook: TrimmingMultiQC – Methcompare Bisulfite FastQs with fastp on Mox

Steven asked me to trim a set of FastQ files, provided by Hollie Putnam, in preparation for methylation analysis using Bismark. The analysis is part of a coral project comparing DNA methylation profiles of different species, as well as comparing different sample prep protocols. There’s a dedicated GitHub repo here:

I roughly followed the trimming pipeline that Hollie had already put together, but opted to use the program fastp as it is generally faster than other trimmers and comes with the bonus ability of generating pre/post-trimming graphs/tables; similar to FastQC. Additionally, [MultiQC(] can also interpret the output of fastp to generate summary statistics/graphs like it can with FastQC.

The data consisted of two different types of libraries: reduced representation bisfultie (RRBS) and whole genome bisulfite (WGBS). Knowing this, I followed the Bismark trimming guidelines for each library type. The fastp trimming and MultiQC were run with the following SBATCH script (GitHub):

#!/bin/bash ## Job Name #SBATCH --job-name=pgen_fastp_trimming_EPI ## Allocation Definition #SBATCH --account=coenv #SBATCH --partition=coenv ## Resources ## Nodes #SBATCH --nodes=1 ## Walltime (days-hours:minutes:seconds format) #SBATCH --time=1-00:00:00 ## Memory per node #SBATCH --mem=120G ##turn on e-mail notification #SBATCH --mail-type=ALL #SBATCH ## Specify the working directory for this job #SBATCH --chdir=/gscratch/scrubbed/samwhite/outputs/20200305_methcompare_fastp_trimming ### WGBS and RRBS trimming using fastp. ### FastQ files were provide by Hollie Putnam. ### See this GitHub repo for more info: ### # Exit script if any command fails # set -e # Load Python Mox module for Python module availability module load intel-python3_2017 # Document programs in PATH (primarily for program version ID) { date echo "" echo "System PATH for $SLURM_JOB_ID" echo "" printf "%0.s-" {1..10} echo "${PATH}" | tr : \\n } >> system_path.log # Set number of CPUs to use threads=27 # Paths to programs fastp=/gscratch/srlab/programs/fastp-0.20.0/fastp multiqc=/gscratch/srlab/programs/anaconda3/bin/multiqc # Programs array programs_array=("${fastp}" "${multiqc}") # Capture program options for program in "${!programs_array[@]}" do echo "Program options for ${programs_array[program]}: " echo "" ${programs_array[program]} -h echo "" echo "" echo "

Sam’s Notebook: RNA Isolation and Quantification – C.bairdi RNA from Hemolymph Pellets in RNAlater

Based on qPCR results testing for residual gDNA from 20200225, a set of 24 samples were identified that required DNase treatment and/or additional RNA. I opted to just isolate more RNA from all samples, since the kit includes a DNase step and avoids diluting the existing RNA using the Turbo DNA-free Kit that we usully use. Isolated RNA using the Quick DNA/RNA Microprep Kit (ZymoResearch; PDF) according to the manufacturer’s protocol for liquids/cells in RNAlater.

  • Used 35uL from each RNAlater/hemocyte slurry.
  • Mixed with equal volume of H2O (35uL).
  • Retained DNA on the Zymo-Spin IC-XM columns for isolation after RNA isolation.
  • Performed on-column DNase step.
  • RNA was eluted in 15uL H2O

RNA was quantified on the Roberts Lab Qubit 3.0 using the RNA High Sensitivity Assay (Invitrogen), using 2uL of each sample.