Literature DB >> 27990823

ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC-MS/MS Experiments.

Meena Choi1, Zeynep F Eren-Dogu2, Christopher Colangelo3, John Cottrell4, Michael R Hoopmann5, Eugene A Kapp6, Sangtae Kim7, Henry Lam8, Thomas A Neubert9, Magnus Palmblad10, Brett S Phinney11, Susan T Weintraub12, Brendan MacLean13, Olga Vitek1.   

Abstract

Detection of differentially abundant proteins in label-free quantitative shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments requires a series of computational steps that identify and quantify LC-MS features. It also requires statistical analyses that distinguish systematic changes in abundance between conditions from artifacts of biological and technical variation. The 2015 study of the Proteome Informatics Research Group (iPRG) of the Association of Biomolecular Resource Facilities (ABRF) aimed to evaluate the effects of the statistical analysis on the accuracy of the results. The study used LC-tandem mass spectra acquired from a controlled mixture, and made the data available to anonymous volunteer participants. The participants used methods of their choice to detect differentially abundant proteins, estimate the associated fold changes, and characterize the uncertainty of the results. The study found that multiple strategies (including the use of spectral counts versus peak intensities, and various software tools) could lead to accurate results, and that the performance was primarily determined by the analysts' expertise. This manuscript summarizes the outcome of the study, and provides representative examples of good computational and statistical practice. The data set generated as part of this study is publicly available.

Entities:  

Keywords:  LC−MS/MS; bioinformatics; differential abundance; mass spectrometry; quantitative proteomics; statistics

Mesh:

Substances:

Year:  2017        PMID: 27990823     DOI: 10.1021/acs.jproteome.6b00881

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  17 in total

1.  Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics.

Authors:  Matthew The; Lukas Käll
Journal:  Mol Cell Proteomics       Date:  2018-11-27       Impact factor: 5.911

2.  Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments.

Authors:  Tsung-Heng Tsai; Meena Choi; Balazs Banfai; Yansheng Liu; Brendan X MacLean; Tom Dunkley; Olga Vitek
Journal:  Mol Cell Proteomics       Date:  2020-03-31       Impact factor: 5.911

3.  Mapping in vivo target interaction profiles of covalent inhibitors using chemical proteomics with label-free quantification.

Authors:  Eva J van Rooden; Bogdan I Florea; Hui Deng; Marc P Baggelaar; Annelot C M van Esbroeck; Juan Zhou; Herman S Overkleeft; Mario van der Stelt
Journal:  Nat Protoc       Date:  2018-03-22       Impact factor: 13.491

4.  ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion.

Authors:  Niels Hulstaert; Jim Shofstahl; Timo Sachsenberg; Mathias Walzer; Harald Barsnes; Lennart Martens; Yasset Perez-Riverol
Journal:  J Proteome Res       Date:  2019-12-06       Impact factor: 4.466

Review 5.  A Review of the Scientific Rigor, Reproducibility, and Transparency Studies Conducted by the ABRF Research Groups.

Authors:  Sheenah M Mische; Nancy C Fisher; Susan M Meyn; Katia Sol-Church; Rebecca L Hegstad-Davies; Frances Weis-Garcia; Marie Adams; John M Ashton; Kym M Delventhal; Julie A Dragon; Laura Holmes; Pratik Jagtap; Kristopher E Kubow; Christopher E Mason; Magnus Palmblad; Brian C Searle; Christoph W Turck; Kevin L Knudtson
Journal:  J Biomol Tech       Date:  2020-04

6.  P-MartCancer-Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets.

Authors:  Bobbie-Jo M Webb-Robertson; Lisa M Bramer; Jeffrey L Jensen; Markus A Kobold; Kelly G Stratton; Amanda M White; Karin D Rodland
Journal:  Cancer Res       Date:  2017-11-01       Impact factor: 12.701

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

Authors:  Joon-Yong Lee; Hyungwon Choi; Christopher M Colangelo; Darryl Davis; Michael R Hoopmann; Lukas Käll; Henry Lam; Samuel H Payne; Yasset Perez-Riverol; Matthew The; Ryan Wilson; Susan T Weintraub; Magnus Palmblad
Journal:  J Biomol Tech       Date:  2018-06-21

8.  Physicochemical Characterization and In Vitro Digestibility Study of an In Silico Designed Recombinant Protein Enriched with Large Neutral Amino Acids and Lacking Phenylalanine for Phenylketonuria.

Authors:  Prakruthi Appaiah; L Sunil; Asha Martin; Prasanna Vasu
Journal:  Protein J       Date:  2022-01-22       Impact factor: 2.371

9.  QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories.

Authors:  Cristina Chiva; Roger Olivella; Eva Borràs; Guadalupe Espadas; Olga Pastor; Amanda Solé; Eduard Sabidó
Journal:  PLoS One       Date:  2018-01-11       Impact factor: 3.240

10.  Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences.

Authors:  Bo Zhang; Mohammad Pirmoradian; Roman Zubarev; Lukas Käll
Journal:  Mol Cell Proteomics       Date:  2017-03-16       Impact factor: 5.911

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