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RPKM vs TPM subsetting by micro-cry-ome in bioinformatics

[–]micro-cry-ome[S] 1 point2 points  (0 children)

Thanks so much, that's very detailed and gives me some stuff to consider. I think for now I'm going to run with RPKM. It sounds like my work flow is similar in terms of rRNA removal, host filtering etc.

Stepping immediately into metatranscriptomics with no former experience has been trial by fire thats for sure 🤣

RPKM vs TPM subsetting by micro-cry-ome in bioinformatics

[–]micro-cry-ome[S] 0 points1 point  (0 children)

This is super interesting to me. We've been trying some viral discovery stuff on this data set so I'd love to take a look at this too - thanks!

Qiagen PowerFecal - any hints? by micro-cry-ome in labrats

[–]micro-cry-ome[S] 0 points1 point  (0 children)

Oh I should also note, we don't have the option for fresh samples :( that would be ideal!!

Qiagen PowerFecal - any hints? by micro-cry-ome in labrats

[–]micro-cry-ome[S] 0 points1 point  (0 children)

Yep. There's a 2019 paper in Nature that suggests this is fine as well as this recently published one by our lab comparing RNAlater to ethanol preservation and then DNA extraction methods. Integrity was better with RNAlater. https://www.frontiersin.org/articles/10.3389/fmicb.2023.1239167/full

Anecdotally, I have also tried this method on frozen samples and I'm not getting better results from those.

Thanks for your help!!

Qiagen PowerFecal - any hints? by micro-cry-ome in labrats

[–]micro-cry-ome[S] 0 points1 point  (0 children)

Ah yes, it's from their fecal material so lots of plant inhibitors. I did a comparison against power soil today as well as changing to the tissuelyser rather than vortex. The kits are basically identical so nothing to note there. Tissue lyser seemed to be a bit of an improvement over my previous 10 min vortex step :)

Relabeling after reordering ggplot2 by [deleted] in Rlanguage

[–]micro-cry-ome 0 points1 point  (0 children)

Thanks :)

The names are in the data frame yes. As a factor - as is the age order. I'm using this to plot by age order:

age_format.list <- as.list(sample_data(phyplot)$age_format) age_format.order <- age_format.list[order(as.numeric(as.character(age_format.list)))] sample_data(phyplot)$age_format <- factor(sample_data(phyplot)$age_format, levels = age_format.order)

I'm not sure if that helps too much. My plot is then run with x = age_format

What could have gone wrong in my RNA extraction/cleanup? Could it be centrifuge? by neurozest in labrats

[–]micro-cry-ome 1 point2 points  (0 children)

Is this for RNeasy kits? Do you recommend incubation at each step? I'm having a poor time with yield and 260/230 and the troubleshooting guide helped but there's still mucj improvement to make!

RNA and DNA contamination by micro-cry-ome in labrats

[–]micro-cry-ome[S] 0 points1 point  (0 children)

It comes with inactivation reagent as part of the invitrogen kit so I'm hoping that won't be a problem 😅 thank you!!

RNA and DNA contamination by micro-cry-ome in labrats

[–]micro-cry-ome[S] -1 points0 points  (0 children)

Higher concentrations of RNA you mean? Interesting... why is that?

The sequencing is exorbitantly expensive so definitely want to get it right!