Guest blog report, ASMS2022 (@SpecInformatics, otherwise known as Conor Jenkins) part 2
So the first full day of ASMS is over
and there have been some awesome talks!
First off, let’s start on the software side.
Fragpipe 18.0 Just dropped from Alexey, bringing a host of new features with it. There is now integrated spectrum visualization of results, a “Headless Mode” that enables fragpipe to be run without the GUI and in the command line so now you can spin it up on your server, and something called diagnostic feature discovery apparently giving you a boost in IDs. New verison can be found here (https://github.com/Nesvilab/FragPipe/releases)
Ben interrupting: There is currently a search for mnemonic devices to assist with the spelling of Alexey’s last name. Word is he doesn’t even know how many “i”s are in it. (As someone with a silent “r” in my last name, I’m allowed to say things like this, the rest of you should try being less insensitive).
Proteome Discoverer 3.0 is finally coming out. I don’t know about you but ever since I updated PD last, the multithreading capabilities have been less than desirable. Well apparently they have fixed this issues and processing data especially on large sample sets has been noticeably faster! This isn’t the only happy boost. The Chimerys Machine learning node can now be brought into PD. Now looking at a vendor poster today (just for disclosure), they are reporting that this node improves your label free quantification data with a 19% boost in peptides and a 7% boost in protein IDs.
Ben interrupting: For my impression of CPU based machine learning rescoring with Impetus in PD 2.5, please see this post. Y’all, more peptide numbers is cool and all but not if we can’t back them up. I am pumped for PD 3.0, but more numbers without evidence is just going to burn this whole proteomics party down right after we got credibility again. Sorry to be a jerk again. I should really check it out again.
Now for the new stuff!
You may remember DeepRescore from a couple years ago: https://doi.org/10.1002/pmic.201900334. Well that team is back with DeepRescore2 and some pretty amazing results. They are posting a 40% improvement in the number of PSMs and a 10% improvement in phospho localization and identification. I don’t think that they have put the code online yet because a simple google isn’t giving my anything but they are putting DeepRescore2 in a nextflow workflow to make it easily adopted.
The Kuster lab is putting out something that may change the way that we do TMT and label free analyses….Instead of doing a match between runs approach, heavily depending on chromatography, they developed a spectra clustering algo called SIMSI-Transfer. So from my understanding, the spectal scan are grouped into clusters by their similarity. If a spectra doesn’t have a match in the cluster from a search, the identify of that spectra is assigned based on which cluster it belongs too. They showed a >35% improvement in PSMs, >15% improvement in Peptide IDs and >5% protein IDs over match-between runs! This is available right now on github https://github.com/kusterlab/simsi-transfer !
The Kuster lab is putting out something that may change the way that we do TMT and label free analyses….Instead of doing a match between runs approach, heavily depending on chromatography, they developed a spectra clustering algo called SIMSI-Transfer. So from my understanding, the spectal scan are grouped into clusters by their similarity. If a spectra doesn’t have a match in the cluster from a search, the identify of that spectra is assigned based on which cluster it belongs too. They showed a >35% improvement in PSMs, >15% improvement in Peptide IDs and >5% protein IDs over match-between runs! This is available right now on github https://github.com/kusterlab/simsi-transfer !
Finally, I have no idea that Metamorpheous could do this, but did you know you can search raw files that were collected in a top down method and a bottom up method at the same time and it kicks ass at identifying more proteoforms than a single search of top down run???? Basically you add your bottom up and top down data to a search but change the protease to topdown for your top down file and then let it fly! They are reporting that a Top down search along identified about 121 different proteoforms, but when you added the bottom up, BAM! Over 8,000 proteoforms identified!
Cool advancement on the software side!