How can i use NCBI for design a primer? how can i do recognize the best designed primer by NCBI?

In relavtive gene expression analysis, we have to average the DCT values of the control gene and then subtact the DCT values of test genes with this.

But my control gene DCT values among replicates are both positive and negative. So when I average them, do they actually hinder the result.

I am investigating whether two independent factors with only two levels each affect my dependent variable memory performance. Thus, I performed a two way analysis where my IV1 was significant and the interaction between both IVs.

I am confused regarding post hoc tests as some state no post hoc tests are needed in case of only two levels. Others state that Tukey test should be performed. However, the Tukey does not seem to make sense in case of only two levels in each IV? I thought about doing an independent samples t test? But in this case only further investigate the main effects. Thus, my question is if my analysis requires a post hoc test and if so, which post hoc test would you suggest for the main effects and interaction effect?

Thanks!!

As stated above is there a way to assign statistical significance (ie get a p-value) from the individual VIPs of RCs of a PLS model?

There is the obvious issue that the VIP is a single point and so there is no mean or variation. However some papers report they used jackknifing to generate a SEM for each VIP and could then run simple analysis (T tests). Unfortunately, I can't find any additional information and haven't gotten it to work yet.

Has anyone tried this? Or have an alternative method?

How to analyze LEfSe and interpret the result of soil microbes obtained through metagenomic high throughput sequencing

If it were a regular lm, we could just run anova and look at the p-value. If we run glm through anova, there is no p-value.

I have an abundance matrix with samples and OTU's present in those samples. I want to know if the samples are different between each other, so I ran a PERMANOVA in R with default options, using the function adonis within the vegan package.

Every time I run it this warning pops up:

'nperm' >= set of all permutations: complete enumeration.

Set of permutations < 'minperm'. Generating entire set.

Someone know what does this mean?

From Geneious support "Thank you for your email, I'm afraid we no longer offer perpetual license for Geneious."

20 years ago, we all switched from VectorNTI to Geneious.

At that time, it was the beginning of whole genomes sequencing, VNTI was the leader of the market but had the really bad idea to move from cheap academic perpetual licenses to expensive annual licenses. Geneious was new, user friendly and free.

We had the impression to fight against the system and to build a new world, with also the birth of open access movement.

Few years ago, Geneious became important on the market and moved from free licenses to cheap academic licenses (annual or perpetual). VNTI lost almost all its customers and was bought by ThermoScientific.

2020, VNTI is dead, Geneious is leader of the market and no longer offers perpetual licenses but only expensive annual licenses.

Why does Geneious did not learn from the past and the history of its concurrents?

Now, all academics will begin to search for better alternatives (even free), especially at that time of big data.

In 5-8 years, Biomatters will sell Geneious.

In 20 years, Geneious will be dead.

Too bad. It was a tremendous software but greed is definitely a cardinal sin.

Without academics, there is no market for these kind of softwares.

A now former angry and so disappointed customer.

Hi, I just have general question about passaging cells.

I have two t75 flask of Tramp C1 culture, and when I am subculturing them, after all the necessary steps, I get 5 mL of media+trypsin from each flask into a 15mL conical tube. Then, I centrifuge that 10 mL cell solution, and re-suspend the cell pellet with fresh 5mL media.

Then, I count the cell from 5mL cell solution with automated cell counter, and get live concentration of 5.2e6 cells/mL.

If I am trying to calculate the total live cell concentration, do I multiply 5.2e6 by 10mL(volume before centrifuging) or 5mL(media volume used to re-suspend the cell pellet after centrifuging)?

Also, if I want to aim to have 5e6 cells/mL per t75 flask, how much cell volume from 5mL cell suspension should be put into new t75 flasks with 10mL fresh media? Also, how many new t75 flasks should be prepared for this passage?

I am fairly new to this cell passage and very confused in figuring out how many new flasks I should prepare if I am subculturing from two old cell culture flasks. Please help me out!

I would like to graphically represent the R-squared values of the linear regression models I have created, however, I have a mixture of both linear (LM) and generalized least square (GLS) models. The output of the former produces an adjusted R-squared value, whereas, the latter does not. Therefore, for the GLS models, I have calculated pseudo R-squared values using the nagelkerke function in R. This function produces 3 pseudo R-squared values, namely:

1. McFadden,

2. Cox and Snell (ML), and

3. Nagelkerke (Cragg and Uhler).

Each value is quite different from the others, and I am not sure as to which value is the correct one to use, specifically in the context of a GLS model. Furthermore, once I have selected an adequate pseudo R-squared value, will it be comparable to the adjusted R-squared values produced by the LM model output?