DART-ID Increases Single Cell Proteome Coverage
DART-ID is a computational method which can increase the coverage of LC-MS/MS proteomics experiments – specifically those relying on MS2-based quantitation (i.e., isobaric tag labeling, TMT). It uses inferred and observed peptide retention time (RT) to update the peptide identification confidence, and usually results in up to 50% more peptides observed at 1% FDR.
- Read the preprint on BioRxiv: https://www.biorxiv.org/content/10.1101/399121v2.
- Check out the
- Check out the scripts for analysis and figure generation: https://github.com/SlavovLab/DART-ID_2018
The paper is currently under review at PLOS Computational Biology.
Welcome to my twitter poster! DART-ID leverages retention time reproducibility to increase proteome coverage in LC-MS/MS experiments. Feel free to comment/ask questions— Albert Chen (@atchen_) March 5, 2019
Special thanks to @slavovLab @afranks53 @NUBioE1 #RSCPoster #RSCAnalytical pic.twitter.com/N0pOFxs7K1
As first author, I lead the development of most of the project shortly after its conception. Working with my collaborator Alex Franks from UCSB, I first explored various iterations of the method on our SCOPE-MS method development data. The first version of DART-ID used pairwise alignment between experiments, and chose a reference experiment based on a couple of heuristics. Alex implemented a global alignment method in the Stan modeling language that greatly improved our performance and we iterated on that method from then on out.
After tweaking our RT alignment model to a good place, I decided to port the DART-ID tool from
python to increase its usability and speed. (the
R version in the DART-ID_2018 repo is still functional but it lacks some key features).
I wrote the original drafts of the manuscript, conducted the analyses, and created the figures/visualizations shown in the paper. I also lead the work of responding to our first round of reviews. Hopefully PLOS will start publishing these reviews openly…
Of course – thanks to my co-authors Alex Franks and Nikolai Slavov, as well as members of the Slavov Lab for their input and feedback. It truly would not have been possible without their help.
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