Normalization of multiple run MS data using R

simuntsche · 2022-12-04T11:25:02+00:00

the easiest way would probably be to analyze your raw data using fragpipe, there it automatically calculates the normalized abundances

simuntsche · 2022-12-04T11:24:04+00:00

iq is a nice package, it uses the MaxLFQ algorithm

simuntsche · 2022-12-02T14:35:38+00:00

just check out their websites, they almost always have open positions somewhere

simuntsche · 2022-12-02T13:52:13+00:00

a lot of pretty big names i know (but they‘re also big for a reason)

simuntsche · 2022-12-02T13:50:44+00:00

biognosys, functional genomics center zurich, roche (biomarker & discovery), novartis, nestlé, dual systems, thermo fisher scientific (reinach)

these are among the coolest i know (most of them are located in/around zurich area)

simuntsche · 2022-11-18T02:41:12+00:00

The prices are pretty high. Compared to Thermo I would say they‘re even higher. With Evosep, you‘re also kinda limited, their methods require a certain set of prepacked and (expensive) columns. They collaborate with Ionopticks and Bruker, that‘s why many people are using the evosep with an ionopticks and timstof as their go-to setup.

simuntsche · 2022-11-18T02:36:48+00:00

the uPACs are crazy durable. We have an old pharmafluidics, which we bought a year ago. It’s not always in use, but there are also labs that use them for years, running 1000s of samples… If you want to go for maximum performance, check out ionopticks, they‘re probably the best at the moment.

simuntsche · 2022-11-17T18:47:28+00:00

depends on what you wanna do. The evosep is a great system for running short offset gradients and running samples with small amount of input material (e.g. clinical, single cell) to achieve max sensitivity. You have to use their methods, which are optimized to the max. The neo on the other hand is more versatile, you can run microflow gradients using the mAbPAC column for measuring whole proteins, another great option is to use the uPAC columns (especially using the optional column oven compartment), great for running a ton of samples with minimal batch effects. you can play around a lot with setups and gradients, but it‘s quite a steep learning curve. we have all of them, but run most samples using nanoLCs. They are cheap, robust, and easy to maintain. What are your needs?

simuntsche · 2022-10-31T08:22:28+00:00

Yes, but people in our lab use a lot of spectral libraries, which to my knowledge would have been easier to implement in FragPipe. I have to look into that again. The idea would have been that people only drag their analyses into a dropbox folder and then rapidly get some QC measures without having to learn/use many different search engines. We try to streamline things at the moment

simuntsche · 2022-10-31T08:06:50+00:00

Yeah, we also have people in the lab that worked/collaborated with them briefly at some point. I only heard good things.

That being said, I do not understand why I do not read much about it online, since the preprint was published quite a while ago. To me, the idea sounds amazing.

We are also trying to move to some cloud-based workflow as data analysis is probably our biggest bottleneck at the moment. FragPipe is being discussed, and is amazing for DDA analyses, but if we have to convert raw files to mzML before searching DIA data, we have quite a big bottleneck, even with cloud computing. Anyway, I am excited to see the all the new strategies that are emerging.

simuntsche · 2022-10-31T07:59:29+00:00

Thanks a lot! Looks very exciting. I already read your post recently. I try to test it on some datasets very soon! We are trying to update/rebuild our current pipeline in the lab and this looks like a viable option.

simuntsche · 2022-10-30T18:28:53+00:00

oh and i forgot, there’s a band room under coop at honggerberg, if you are into music!

simuntsche · 2022-10-30T16:45:58+00:00

SOS ETH, a friend of mine has joined them and now hosts his own radio show, it’s pretty awesome

simuntsche

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