Top Tweets for #MSFragger
Conventional proteomics searches struggle with many modifications and open searches may be difficult to interpret. We introduce a "detailed" mass offset search in #MSFragger boosting interpretability and localization especially in complex cases like FPOP: https://t.co/JeqUNAjrwO
DDA is still great for many applications, and #MSFragger-DDA+ improves peptide identification rates via full isolation window search. Huge boosts in IDs, including Astral DDA! Fully integrated in #FragPipe, simply annotate your DDA files as DDA+ and RUN. https://t.co/pDI3s8WGjC
Very nice paper in Science, showing that N-glycosylation can also function as a degradation signal, similar to Ubiq: https://t.co/O39BA5RcEa. Glad to see the authors used our #FragPipe/#MSFragger-Glyco “Glyco-N-LFQ” workflow for quantitative glycoproteomics data analysis.
Assessment of Data-Independent Acquisition Mass Spectrometry (DIA-MS) for the Identification of Single Amino Acid Variants https://t.co/Zz7JmDjQdF. Glad to see #FragPipe/#MSFragger showing the most conservative and effective performance (lowest false discovery match ratio (FDMR))
Rethink the analysis of DDA data! Blurring the boundary between DDA, WWA, and DIA, #MSFragger-DDA+ Enhances Peptide Identification Sensitivity with Full Isolation Window Search. Fully integrated in #FragPipe, simply annotate your DDA files as DDA+ and RUN. https://t.co/ThsqgTeqKw
@michal_bassani @FlorianHuber_ @HippHupo Congratulations on your publication, and thank you for incorporating #MSfragger and #FragPipe for immunopeptide identification and quantification in your NeoDisc antigen discovery pipeline.
#FragPipe with #MSFragger is great for FPOP data. Happy to contribute to Dr Jones lab efforts: Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments https://t.co/J77hKnMBw4
#FragPipe with #MSFragger Open and Labile PTM searches plus the Diagnostic Ion Mining module help to unravel novel RNA adducts in RNA-protein crosslinking studies: "Mass Spectrometric Evidence for the Under-represented RNA–Protein Cross-Links" | ACS Omega https://t.co/S7q1zFC10b
@pwilmarth @DavidGomezZep After a lot of tests, these are #MSFragger settings in #FragPipe for nonspecific-HLA-C57 workflow: C+57 as fixed and cysteinylation (+119) as a variable mod (with +62 mass shift). In nonspecific-HLA (the most commonly used workflow), there is just C+119 as variable and no C+57.
So happy to see #MSFragger and #FragPipe used in an increasing number of educational workshops and short courses. Thanks @PHorvatovich and Dave Tabb! Hope the tutorial went well.
X-Omics training (day 2) on Proteogenomics in Groningen with David Fenyo, David Tabb and Groningen X-Omics Proteogenomics team with David Tabb showing how to use MSFragger and Fragpipe for proteogenomics analysis and Yanick Hagemeijer presenting the PW. #XOmics #Proteogenomics
Have you tried the #MSFragger DDA+ mode in #FragPipe that provides big boost in IDs via full isolation window search of DDA data? While we are still working on the manuscript, I am thrilled to see our users are getting great results, e.g. in this new study https://t.co/zP0a7qu3L0
@neely615 @ypriverol @chrashwood @wfondrie @Pdorrestein1 @pwilmarth @michaellazear Yeah. Speaking of chimericity, you could try our DDA+ mode with the latest #FragPipe and #MSFragger 😎 We will have a manuscript soon.
@ypriverol We normally run #MSFragger and check the mass calibration table printed in the log. Then, we decide whether we need to increase the initial tolerances if there are large mass deviations, such as the example in figure I posted.
@ypriverol Automatically decided by #MSFragger mass calibration https://t.co/1na4GNvtHR
This is one of the major advantages over other search engines: it automatically calibrates the masses and adjusts the tolerances to optimal values. 😎
Led by @DanPolasky and in collaboration with @Smith_Chem_Wisc lab, we improved #FragPipe for O-glycoproteomics analysis. It provides fast (#MSFragger-Glyco), proteome-wide, quantitative results (LFQ,TMT), and incorporates O-Pair for glycosite localization! https://t.co/43KvEj69BO
Our small contribution to the world of modified proteins. We ask: is #MSFragger open search identification consistent for differential analysis? YES!!! you can confidently use it to identify deferentially present peptidoforms!
Beyond thrilled to see one of @kourtisSavva's brilliant ideas published today in @ScientificData @NaturePortfolio
🔥🔥Detection of differential bait proteoforms through immunoprecipitation-mass spectrometry data analysis🔥🔥
https://t.co/qYmx5UNZjr
A interesting manuscript describing a workflow for the detection of differentially present peptidoforms in AP-MS interactome experiments using #FragPipe with #MSFragger Open Search, and differential analysis between conditions using SAINTexpress. https://t.co/YRGeCGZCxg
Interesting paper showing how to significantly boost the number of N-glycopeptide IDs on a timsTOF Pro platform. The authors tuned ion optics, optimized collision energies, ion mobility isolation width and more, and used #MSFragger-Glyco for the analysis. https://t.co/xyugwog3ij
Read our new manuscript Efficient Analysis of Proteome-Wide FPOP Data by FragPipe in Anal Chem. Thanks to #MSFragger mass offset mode, #FragPipe solves the FPOP data analysis challenge providing a 2x boost in PSMs and runs 50 times faster compared to PD. https://t.co/y17HJPhuC3
New workflows include #Fragpipe:
▶️ HLA- glyco - new workflow, new atlas! - why not try it on your biology questions?
▶️ Analyst - PCA, UMAP, etc.
▶️ DIA - combined with #DIANN released last week - blurring the lines between DDA/DIA
#BSPREUPA2023 #Bioinformatics #MassSpec
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