Grace’s Notebook: Trying out RNA protocol with 6x original protocol’s volumes of reagents

Today Steven, Sam, and I met to talk about the issue with poor results on the QUbit and Nanodrop1000 on the pooled hemolymph samples I prepared. Short-term plan is to try out the RNA protocol, but multiply all reagents times 6 (so, use 6ml of RNAzol, etc.) to see if that ratio would help; run the Bioanalyzer on the samples that Sam processed as well as some random samples that I isolated. Today, I did the 6x RNA protocol on 4 of the extra samples from day 26 (samples from day 26 were taken in triplicate).

RNA Isolation Protocol x 6

Link to the protocol I did today: HERE
*Note: I forgot to do step 6

It was a bit challenging today because in order to support those larger volumes of reagents, I had to use 13ml round-bottomed tubes. It was difficult to see any pellets or genetic material, but I did my best. I oriented the tubes in the same way every time I put them in the centrifuge and I marked the orienation on the tubes so that I knew where the genetic material should be if it was going well.

Per Sam’s suggestion, I resuspended the samples in 25ul of 01% DEPC-treated H20, instead of the typical 50ul. I also pipetted to mix/dissolve.

I ran the Qubit, and they were ALL “out of range/too low”.

I did not run the Nanodrop, because I can’t remember how to use it. I will do it first thing tomorrow when Sam is in.

Goals for tomorrow:

  • Run Nanodrop on the samples I isolated yesterday
  • Run Bioanalyzer on the samples Sam isolated RNA from as well as some random ones selected from what I’ve already done
  • Try out/read about using Tri-reagent for isoalting RNA

from Grace’s Lab Notebook

Yaamini’s Notebook: Mixed Effects Models

Assumptions and p-values

Last week, I tackled reviwer comments and used mixed effect models for my analyses. I was right to be concerned about using a glmer with tank as a random effect without any way to assign tanks to some tissues. I reverted back to my binomial GLM. Although I obtained a p-value for my lmer analysis, I wasn’t sure if I was going about it correctly. After some Googling, I found a tutorial from Bodo Winter at UC Merced for linear mixed effect models! It has very clear instructions for constructing mixed effects models with lme4 and for using likelihood ratio tests for p-values.

Likelihood Ratio Tests

In this R script, I modified my code to use likelihood ratio tests instead of an ANOVA workaround. To do this, I needed to build a null model without the fixed effect of interest (either Parental.Treatment or Female.Treatment), and a full model with the effect of interest. I then used an ANOVA to compare both models and get a p-value. A significant p-value means that the effect is part of the most parsimonious model.

When I examined all treatments, I found that parental treatment affected D-hinge counts (Chi-squared = 9.1878, df = 3, p = 0.0269). D-hinge counts were -0.15795 ± 0.10468 lower for low pH female-ambient male and -0.15795 ± 0.10509 lower for low pH female-low pH male. While I have a significant p-value for this model, there is still evidence of a maternal effect. I investigated this further with my second lme using Female.Treatment instead of Parental.Treatment. Female treatment affected D-hinge counts (Chi-squared = 8.1781, df = 1, p = 0.00424). D-hinge counts were -0.21090 ± 0.07033 lower for families with low pH females.


I needed to check the following assumptions for these models:

  1. Normality of residuals
  2. Linearity (because linear mixed effect models)
  3. Homoskedasticity

To check residual normality, I used qqnorm and qqline. Visual inspection did not reveal any obvious violations of the normality assumption, since the data fell on a straight line. I plotted residuals vs. fitted values to check for linearity and homoskedasticity. There was no heteroskedasticity in my residual plots, but I did see striped patterns.


Figure 1. Residual plot for Female.Treatment model.

I’m not sure what this means, and neither Google nor my QSCI 483 notes were of any help. This guide suggests that such a pattern in my residual plot is acceptable, but this Bodo Winter tutorial does not. I might bring it up at lab meeting or ask Tim or Julian for some help. My gut feeling is that there’s some caveat to residual analysis with mixed effects models that I’m missing.

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from the responsible grad student

Sam’s Notebook: Mox – Over quota: Olympia oyster genome annotation progress (using Maker 2.31.10)


Well, this is an issue. Checked in on job progress and noticed that we’ve exceeded our storage quota on Mox: