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Improvements since Skyline 19.1 include:

• Prosit spectrum and iRT prediction support directly integrated into the UI
  • Building libraries for targeted peptides in a document through Peptide Settings – Library – Build button.
  • Prosit spectrum prediction viewing in the Spectrum Match plot with new right-click menus, including mirror plotting
  • Settings in Tools > Options > Prosit
• Support for spectral library building from MS Fragger pepXML search results
• Support for diaPASEF!
  • We have run this with 2 separate 3-organism datasets through the LFQBench statistical assessment and that works.
• Improved ddaPASEF and initial prmPASEF support.
• Performance gains in importing Agilent and Waters IMS data as much as 2x or more.
• Parallel file import with proteomewide DIA in the UI or by default on the command-line has performance similar to what was previously only available from the command-line using –import-process-count. Choose “Many” on your next import or just ignore threading the next time you import from the command-line.
• Optimized spectrum memory handling for instrument vendors with .NET data reader libraries, benefitting Agilent, SCIEX, and Thermo
• A new “Consistency” tab in the Refine > Advanced form, supporting CV and q value cut-offs
• New checkbox for Refine > Advanced – Results tab Max precursor peak only
• Support for Multiple Attribute Model (MAM) grouping with Peptide.AttributeGroupID and PeptideResults.AttrributeAreaProportion
• Added File.SampleID and .SerialNumber (of the instrument) as fields in Document Grid custom reports
• Transitions Settings – Full-Scan – MS/MS filtering has been extended to apply to all non-MS1 spectra (e.g. MS3) as long as the MS1-level precursor matches the target precursor m/z The redundant library filtering phase of spectral library building is around 20x
• Improved iRT calibration UI making it easy to create new sets of standards based on existing sets that can be used in spectral library building and the Import Peptide Search wizard
• More iRT improvements including more intelligent use of 80+ CiRT peptides when CiRT is chosen during library building
• New right-click > Quantitative menu item for changing the Quantitative property on transitions in the Targets view TIC and BPC now come from raw data files and do not need to be extracted from MS1 spectra which has performance benefits for MS1 filtering
• New global “QC” transitions have been added such as the pressure trace
• Calibration curve fixes to make ImCal (Isotopolog Calibration Curves) work
• New “Calculated” annotations have been added which support storing Skyline calculated values in annotations for future use with AutoQC
• Support KEGG IDs as molecular identifiers in small molecule targets.
• Improved support for D used in chemical formulae in place of the Skyline default H’
• Added support for Thermo Exploris and Eclipse instruments
• Support for opening .skyp files downloaded directly from Panorama

Wow. Do I ever love ProteomeXchange!

Skip my reading and go to MASSIVE and get proteomic data from cancer cells infected with a coronavirus and — if you’re into that sort of thing — you can get metabolomic data here and lipidomic data here! 

The RAW files currently heating my apartment may not have been published yet, but they are publicly available and I just contacted the uploader, but I’m moving fast because this data is 1) awesome and 2) pertinent

The Wuhan Coronavirus (2019-CoV) has a very close neighbor (possibly the closest according to my rough pBLAST of the entire translated sequence, but that may just be a consequence that it emerged more recently, as sequencing technology has gotten cheaper and more common — leading to more data) — that is called the MERS-CoV (here is the entry from UniProt) or Middle East respiratory syndrome-related coronavirus.

The experiment is 9 files from infected Calu cells (appears to be an immortalized and/or human cancer cell line) infected with the virus and 3 files from “Mock” (presumably uninfected).

The files were acquired on an Orbitrap Velos in “high/low” mode (120k resolution MS1 and CID ion trap fragmentation). The files appear to originate from PNNL, where it is rumored they know a thing or two about running mass spectrometers.

MetaMorpheus recalibration shows the MS1 is spot on, something like -1ppm off actual when compared against human and — get this — I can get >70% coverage of the main capsule protein from the virus in the virus infected proteomes. This is really cool because that protein is well conserved betweent the 2 (by pBLAST score, anyway).

Update: More fast moving science!! Just because the pBLAST scores line up, it doesn’t mean that the peptides do — check this out!

Again — big disclaimer — this is a mass spectrometrist’s blog. I know very little about viruses and is just interested in this topic!

You should check out this new preprint here! 

What a great week or 10 days for proteomics. Holy cow. January was kind of laggy and then — BOOOOOOOOOOOM –!!

Okay — in yet another that is going into a file called “January 2020 papers you must read!!” — which — is too many words for this cursed Windows 10 thing —

(BERNIE BOMB!)

Back to the paper — if you just read the abstract, the phrasing will make you think that or friends at the Max Planck jumped on the ScoPE-MS electric Porsche into the future but you’ll find inside that more standard plasma profiling (which looks a lot to me like at lot of the clinical proteomics proof – of – concept work we’ve seen from the Mann lab — high fractionation, rapid HF runs [relatively affordable instrument!!] for individual patients and MBR). You can read my rambling about one of my favorite of these recent studies here.

Couple that to high throughput single cell transcriptomics and then using machine learning to link the plasma proteome features to the single cell transcripts across 31 clinically derived factors from these patients and — it looks like the future to me, but it appears they took the Tesla.

…which…of course, that is a thing, right?

Since I’m still rambling — this preprint was posted in medrXiV, which has some great disclaimers.

…and the 2020 Grammy for most eye grabbing title goes to….

…this brand new study that is the first or second thing I get to once I’m safely behind the publisher’s financial security of a university library paywall….

To be clear, I haven’t read this and I’ll probably doubly verify the QC/QA checks on my baloney detectors before I do. But if you think there is a force on earth that can keep me from reading this today —

…I mean…besides the paywall….$8.99….

Edit 2/10/2020: UniProt has resource up. These are better. You can check them all out here!

I was looking for a complete protein FASTA database for the Wuhan coronavirus and came up empty.

The NCBI database was just updated yesterday (direct link here) so I pulled the newest sequences and just assembled them into a single file.

You can download the complete protein FASTA from this Google drive link here.

Hit me up if you have any issues with it.

Image above is from this preprint which was updated on 1/22 after it was ORIGINALLY POSTED ON 1/21!! This is how fast science can be, people!

Yikes — okay, well I guess the way that blew up I wasn’t the only person looking for it.

Disclaimers: I’m a loud mouthed mass spectrometrist who knows very little about viruses. I just put all the sequences NCBI translated into one file so the common proteomics software on my computers will accept it.