Evidence map›Paper›PMID 31443461›Full record

ReviewProteomes2019

What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows.

Matthew B O'Rourke, Stephanie E L Town, Penelope V Dalla, Fiona Bicknell, Naomi Koh Belic, Jake P Violi, Joel R Steele, Matthew P Padula

Abstract readReview
In one paragraph

Review in Proteomes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Multivariate Analysis with the R Package mixOmics.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Subcellular proteomics.Nature reviews. Methods primers · 2021
    Article
  17. Article
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Matthew B O'RourkeBowel Cancer & Biomarker Lab, Northern Clinical School, Faculty of Medicine and Health, The University of Sydney Lvl 8, Kolling Institute. Royal North Shore Hospital, St. Leonards, NSW 2065, Australia.
Stephanie E L TownSchool of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia.
Penelope V DallaSchool of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia.
Fiona BicknellSchool of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia.
Naomi Koh BelicSchool of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia.
Jake P VioliSchool of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia.
Joel R SteeleSchool of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia.
Matthew P PadulaSchool of Life Sciences and Proteomics Core Facility, Faculty of Science, The University of Technology Sydney, Ultimo 2007, Australia. matthew.padula@uts.edu.au.ORCID 0000-0002-8283-0643

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accurate quantification of changes in the abundance of proteins is one of the main applications of proteomics. The maintenance of accuracy can be affected by bias and error that can occur at many points in the experimental process, and normalization strategies are crucial to attempt to overcome this bias and return the sample to its regular biological condition, or normal state. Much work has been published on performing normalization on data post-acquisition with many algorithms and statistical processes available. However, there are many other sources of bias that can occur during experimental design and sample handling that are currently unaddressed. This article aims to cast light on the potential sources of bias and where normalization could be applied to return the sample to its normal state. Throughout we suggest solutions where possible but, in some cases, solutions are not available. Thus, we see this article as a starting point for discussion of the definition of and the issues surrounding the concept of normalization as it applies to the proteomic analysis of biological samples. Specifically, we discuss a wide range of different normalization techniques that can occur at each stage of the sample preparation and analysis process.

Indexed as

2D-PAGELC-MS/MSnormalizationtop down proteomics

Identifiers

PMID31443461
PMCPMC6789750

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.