Show simple item record Shams, Daniel P en Early, Bryan P en Fellers, Ryan T en Greer, Joseph B en Thomas, Paul M en Fornelli, Luca en LeDuc, Richard D en Schwab, David J en Kelleher, Neil L en 2016-06-23T17:39:23Z en 2016-06-23T17:39:23Z en
dc.identifier.citation Forthcoming en
dc.identifier.uri en
dc.description Click on the PURL link below in the "External Files" section to download the dataset. en
dc.description.abstract Within the last five years, top down proteomics (TDP) has emerged as a high throughput technique for protein identification in addition to characterization and quantitation of thousands of modified proteoforms. Here, a framework for calculating an accurate false discovery rate (FDR) that considers both protein and proteoform levels was used to evaluate local dependencies when aggregating results from replicate LC-MS/MS runs searched with different search modes and parameters.Ê We find that proteoform identifications are not statistically independent of each other and that correcting the FDR locally within a given LC-MS/MS run is not sufficient to control FDR globally across a large experiment.Ê A series of corrections used previously in genomics was implemented to address these issues and produce a global FDR calculation that scales well. ÊThe validity of the system is assessed by analyzing two previously published experimental datasets.ÊWeb-based access via a new TDPORTAL to high-performance computation enables all steps necessary to create a set of results utilizing the accurate and scalable FDR estimation described here. Also, a new application called TOP DOWN VIEWER enables viewing, analyzing, and sharing result sets via .tdReport files and is available at en
dc.language.iso en_US en
dc.publisher Forthcoming en
dc.relation.ispartofseries Forthcoming; en
dc.relation.isversionof Forthcoming en
dc.relation.uri en
dc.rights n. Creative Commons Attribution 4.0 license en
dc.rights.uri en
dc.subject False Discovery Rate, Top Down Proteomics, Statistical Dependency, Molecular Levels, Protein Identification, Proteoform, Search Engine, Multiple Hypothesis Testing en
dc.title Accurate Estimation of False Discovery Rates for Protein and Proteoform Identification in Top Down Proteomics en
dc.type Dataset en
dc.altmetrics.display true en

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