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