Engineering topics

Wilson, Ryan

Publications and source records attributed to Wilson, Ryan.

5 records

DOE OSTI2018

ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data

This report presents the results from the 2016 ABRF Proteome Informatics Research Group (iPRG) study on proteoform inference and FDR estimation from bottom-up proteomics data. For this study, three replicate Q Exactive Orbitrap LC-MS/MS datasets were generated from each of four E. coli samples spiked with different equimolar mixtures of small recombinant proteins selected to mimic pairs of homologous proteins. Participants were given raw data and a sequence file, and asked to identify the proteins and provide estimates on the false discovery rate at the proteoform level. As part of this study, we tested a new submission system with a format validator running on a virtual private server (VPS) and allowed methods to be provided as executable R Markdown or IPython Notebooks. The task was perceived as difficult, and only eight unique submissions were received, though those who participated did well, with no one method performing best on all samples. However, none of the submissions included a complete Markdown or Notebook, even though examples were provided. Future iPRG studies need to be more successful in promoting and encouraging participation. The VPS and submission validator easily scale to much larger numbers of participants in these types of studies. The unique “ground-truth” dataset for proteoform identification generated for this study is now available to the research community, as are the server-side scripts for validating and managing submissions.

Proteomics, proteoforms, inference, false discover↗

DOE OSTI2018

Blazing Signature Filter: a library for fast pairwise similarity comparisons

Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data. A significant practical drawback of large-scale data mining is the vast majority of pairwise comparisons are unlikely to be relevant, meaning that they do not share a signature of interest. It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible and exclude them from more time-intensive similarity calculations. The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. As a result, the BSF can scale to high dimensionality and rapidly filter unproductive pairwise comparison. Furthermore, two bioinformatics applications of the tool are presented to demonstrate the ability to scale to billions of pairwise comparisons and the usefulness of this approach.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Metabolomics Collision Cross Section Database and Server

This is a the code and data for a website created to display collision cross section data for metabolites and small molecules. This repository hosts two things. First is the source code (js) for a website created to display collision cross section data for metabolites and other small molecules. The second is the underlying CCS dataset. The growing interest in metabolomic and exposomic studies is also inciting a need for new techniques to analyze these diverse molecules. Mass spectrometry-based studies are often preferred for the confident identification of small molecules. Ion mobility spectrometry (IMS) is capable of separating molecules that have the same m/z but different conformational arrangements, which is very useful in metabolomic and exposomic analyses. As the desire for IMS measurements of metabolites continues to grow, so does the need for high quality collision cross section (CCS) values.

Payne, Sam↗

DOE OSTI2018

MetaboliteValidation

This is a simple C# console program to validate and update the data from MetabolomicsCCS

Monroe, Matthew↗

DOE OSTI2018

BSF_publication

All the data and materials for the publication about the Blazing Signature Filter

Wilson, Ryan↗