Our goal is to advance science. We believe that this is easier if data and methodology are open and available. Where possible, we have worked to expose the underlying data in our publications through technologies like Jupyter and GitHub. Below are the resources associated with publications.
Individual variability of protein expression in human tissues Kushner et al. JPR 2018
https://github.com/PNNL-Comp-Mass-Spec/Tissue_Classification - includes Jupyter notebooks for making the paper's figures
Proteomics of natural bacterial isolates powered by deep learning-based de novo identification. Lee et al. bioRxiv 2018
https://github.com/PNNL-Comp-Mass-Spec/Kaiko_Publication - includes Jupyter notebooks for making the paper's figures
Blazing Signature Filter: a library for fast pairwise similarity comparisons., Lee et al. BMC Bioinformatics 2018.
https://github.com/PNNL-Comp-Mass-Spec/BSF_publication - includes Jupyter notebooks for making the paper's figures
Informed-Proteomics: open-source software package for top-down proteomics., Park et al. Nature Methods 2017
Ancient Regulatory Role of Lysine Acetylation in Central Metabolism., Nakayasu et al. mBio 2017