A Systematic Model of the LC-MS Proteomics Pipeline

Overview

Journal BMC Genomics

Publisher Biomed Central

Specialty Genetics

Date 2012 Nov 9

PMID 23134670

Citations 5

Authors

Youting Sun

Ulisses Braga-Neto

Edward R Dougherty

Affiliations

Soon will be listed here.

Abstract

Motivation: Mass spectrometry is a complex technique used for large-scale protein profiling with clinical and pharmaceutical applications. While individual components in the system have been studied extensively, little work has been done to integrate various modules and evaluate them from a systems point of view.

Results: In this work, we investigate this problem by putting together the different modules in a typical proteomics work flow, in order to capture and analyze key factors that impact the number of identified peptides and quantified proteins, protein quantification error, differential expression results, and classification performance. The proposed proteomics pipeline model can be used to optimize the work flow as well as to pinpoint critical bottlenecks worth investing time and resources into for improving performance. Using the model-based approach proposed here, one can study systematically the critical problem of proteomic biomarker discovery, by means of simulation using ground-truthed synthetic MS data.

Citing Articles

Effects of Growth Hormone Treatment and Rehabilitation in Incomplete Chronic Traumatic Spinal Cord Injury: Insight from Proteome Analysis.

Martin-Rojas T, Sastre-Oliva T, Esclarin-Ruz A, Gil-Dones F, Mourino-Alvarez L, Corbacho-Alonso N J Pers Med. 2020; 10(4).

PMID: 33096745 PMC: 7720149. DOI: 10.3390/jpm10040183.

Modeling and systematic analysis of biomarker validation using selected reaction monitoring.

Atashpaz-Gargari E, Braga-Neto U, Dougherty E EURASIP J Bioinform Syst Biol. 2017; 2014:17.

PMID: 28194167 PMC: 5270363. DOI: 10.1186/s13637-014-0017-y.

Bayesian ABC-MCMC Classification of Liquid Chromatography-Mass Spectrometry Data.

Banerjee U, Braga-Neto U Cancer Inform. 2017; 14(Suppl 5):175-182.

PMID: 28096647 PMC: 5224349. DOI: 10.4137/CIN.S30798.

Modeling the next generation sequencing sample processing pipeline for the purposes of classification.

Ghaffari N, Yousefi M, Johnson C, Ivanov I, Dougherty E BMC Bioinformatics. 2013; 14:307.

PMID: 24118904 PMC: 3819514. DOI: 10.1186/1471-2105-14-307.

Selected articles from the IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS'2011).

Pal R, Huang Y, Chen Y BMC Genomics. 2012; 13 Suppl 6:S1.

PMID: 23134705 PMC: 3481482. DOI: 10.1186/1471-2164-13-S6-S1.

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