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Mike Riffle has many years of experience in bioinformatics, data science, and statistics; and has worked with several leading academic proteomics laboratories in the US. He is a proponent of open and reproducible science, and has produced several open source tools for proteomics data visualization, including Proxl (protein cross-linking) and, more recently, Limelight (generalized proteomics data visualization and sharing). Michael has a Bachelors in Molecular and Cellular Biology from the University of Washington and his Masters in Computer Science from the University of Illinois Urbana-Champaign. |
An automated computational workflow can solve many problems in data analysis. It dramatically simplifies installing and running the constituent steps, manages versions, reduces user errors, provides data provenance, and is essential for data reproducibility.
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Here we present a standardized DIA workflow written in Nextflow that begins by converting vendor RAW files, searches the data with either EncyclopeDIA or DIA-NN, performs optional imputation on missing values, creates a Skyline document, runs user-defined and QC reports, and optionally uploads to PanoramaWeb. The workflow is simple to run and orchestrates the running of all steps, which may be run on your local machine, a computer cluster, or AWS Batch. The workflow is strictly version controlled, ensuring anyone may run precisely the same workflow and reproduce your results. All steps are containerized using Docker, so there is no need to install any analysis software or manage versions. The workflow is open source and freely available on GitHub.
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