Dataset Information

Bayesian proteoform modeling improves protein quantification of global proteomic measurements

ABSTRACT: Data used for the implementation of a Bayesian Proteoform Quantification model (BP-Quant) that uses statistically derived peptide signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. BP-Quant is available on GitHub as both MatLab and R packages: https://github.com/PNNL-Comp-Mass-Spec/BP-Quant Plasma samples collected from standard inbred mice were digested with trypsin then analyzed with an LTQ-Orbitrap Velos mass spectrometer. Data was searched with SEQUEST using PNNL's DMS processing pipeline.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Mus Musculus (ncbitaxon:10090)

SUBMITTER: Richard Smith Katrina Waters

PROVIDER: MSV000087701 | MassIVE | Thu Jun 24 18:24:00 BST 2021

REPOSITORIES: MassIVE

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Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

Webb-Robertson Bobbie-Jo M BJ Matzke Melissa M MM Datta Susmita S Payne Samuel H SH Kang Jiyun J Bramer Lisa M LM Nicora Carrie D CD Shukla Anil K AK Metz Thomas O TO Rodland Karin D KD Smith Richard D RD Tardiff Mark F MF McDermott Jason E JE Pounds Joel G JG Waters Katrina M KM

Molecular & cellular proteomics : MCP 20141201 12

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as al ...[more]

PMID: 25433089

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