| Info |
|---|
The new Zymo Mock Community samples (cleaned via the max tip protocol) did not show up in the demultiplexed reads. They were either not added, or somehow the demux key is wrong for them. There is a relationship between reads and qPCR quantification. We will take absorbance readings of these same samples. If there is a relationship between qPCR, absorbance, and reads of these same samples, we will use absorbance from all samples to normalize prior to pooling. If not, we will consider further qPCR. There is still a relationship to absorbance. We will proceed with 1 column of qPCR as a check on sequence-able state, and use absorbance to adjust for less read disparity. We will perform absorbance checks, compile the data, and then sort out the best program for normalization. |
Alex Buerkle Linda van Diepen
...
There is still a relationship here. We will proceed with 1 column of qPCR as a check on sequence-able state, and use absorbance to adjust for less read disparity.
We will perform absorbance checks, compile the data, and then sort out the best program for normalization. Probably, we will normalize to around 10 ng/ul. 1 nM is the minimum library concentration for a NovaSeq run. From the data here, 1.44 nM ~ 4 ng/ul. We would like to operate under a larger margin of error.
Is there a relationship between reads and qPCR?
| Code Block |
|---|
|
ggplot(LRIIIReads, aes(NgPerUl, mean, color=plate, shape=plate))+
geom_point(show.legend = TRUE)+
geom_smooth(method='lm', formula= y~x)+
stat_regline_equation(label.y = 125, aes(label = ..eq.label..)) +
stat_regline_equation(label.y = 100, aes(label = ..rr.label..))+
facet_wrap(~plate) |
Image AddedExcluding the Mock Community samples, there is a strong relationship between absorbance and qPCR. The odd MC results may be a result of the small Data Set, their larger than average contribution to the data set, and their very low relative complexity.
Files:
| View file |
|---|
| name | LRIIIfiltermergestats.csv |
|---|
|
...