Data and Presentations
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
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.
https://github.com/PNNL-Comp-Mass-Spec/Kaiko_Publication - includes Jupyter notebooks for making the paper's figures
https://github.com/PNNL-Comp-Mass-Spec/Kaiko
Blazing Signature Filter: a library for fast pairwise similarity comparisons.
https://github.com/PNNL-Comp-Mass-Spec/BSF_publication - includes Jupyter notebooks for making the paper's figures
https://github.com/PNNL-Comp-Mass-Spec/bsf-core
https://github.com/PNNL-Comp-Mass-Spec/bsf-py
Informed-Proteomics: open-source software package for top-down proteomics.
https://github.com/PNNL-Comp-Mass-Spec/Informed-Proteomics
Ancient Regulatory Role of Lysine Acetylation in Central Metabolism.
https://github.com/samuelpayne/Biodiversity.Acetylation.Supplement.Coverage