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Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics

  • Mathias Walzer
  • Juan Antonio VizcaínoEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2051)

Abstract

In any analytical discipline, data analysis reproducibility is closely interlinked with data quality. In this book chapter focused on mass spectrometry-based proteomics approaches, we introduce how both data analysis reproducibility and data quality can influence each other and how data quality and data analysis designs can be used to increase robustness and improve reproducibility. We first introduce methods and concepts to design and maintain robust data analysis pipelines such that reproducibility can be increased in parallel. The technical aspects related to data analysis reproducibility are challenging, and current ways to increase the overall robustness are multifaceted. Software containerization and cloud infrastructures play an important part.

We will also show how quality control (QC) and quality assessment (QA) approaches can be used to spot analytical issues, reduce the experimental variability, and increase confidence in the analytical results of (clinical) proteomics studies, since experimental variability plays a substantial role in analysis reproducibility. Therefore, we give an overview on existing solutions for QC/QA, including different quality metrics, and methods for longitudinal monitoring. The efficient use of both types of approaches undoubtedly provides a way to improve the experimental reliability, reproducibility, and level of consistency in proteomics analytical measurements.

Key words

Computational mass spectrometry Quality control approaches Large scale data analysis Cloud technology Reproducible analysis pipelines 

Abbreviations

BSA

Bovine serum albumin

CWL

Common workflow language

DAC

Data Access Compliance

DACO

Data Access Compliance Office

DDA

Data-dependent acquisition

DIA

Data-independent acquisition

FDR

False discovery rate

GUI

Graphical user interface

HPC

High-performance computing

HUPO

Human Proteome Organization

LC

Liquid chromatography

LCL

Lower control level

MS

Mass spectrometry

PCAWG

Pan-cancer analysis of whole genomes

PSI

Proteomics Standards Initiative

QA

Quality assessment

QC

Quality control

SOP

Standard operating procedure

SPC

Statistical process control

SRM

Selected reaction monitoring

UCL

Upper control level

WMS

Workflow management system

Notes

Acknowledgments

The authors would wish to acknowledge funding from ELIXIR Implementation Studies, BBSRC [grant number BB/P024599/1], Wellcome Trust [grant number 208391/Z/17/Z], and EMBL core funding.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL-EBI)CambridgeUK

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