ArticleJournal of proteomics & bioinformatics2019
Interactive Web Tool for Standardizing Proteomics Workflow for Liquid Chromatography-Mass Spectrometry Data.
Article in Journal of proteomics & bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
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- An Optimized Comparative Proteomic Approach as a Tool in Neurodegenerative Disease Research.Cells · 2022Review
- Combined exposure to polychlorinated biphenyls and high-fat diet modifies the global epitranscriptomic landscape in mouse liver.Environmental epigenetics · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
introductionThe proteomics experiments involve several steps and there are many choices available for each step in the workflow. Therefore, standardization of proteomics workflow is an essential task for design of proteomics experiments. However, there are challenges associated with the quantitative measurements based on liquid chromatography-mass spectrometry such as heterogeneity due to technical variability and missing values.
methodsWe introduce a web application, Proteomics Workflow Standardization Tool (PWST) to standardize the proteomics workflow. The tool will be helpful in deciding the most suitable choice for each step of the experimentation. This is based on identifying steps/choices with least variability such as comparing Coefficient of Variation (CV). We demonstrate the tool on data with categorical and continuous variables. We have used the special cases of general linear model, analysis of covariance and analysis of variance with fixed effects to study the effects due to various sources of variability. We have provided various options that will aid in finding the contribution of sum of squares for each variable and the CV. The user can analyze the data variability at protein and peptide level even in the presence of missing values. AVAILABILITY AND IMPLEMENTATION: The source code for "PWST" is written in R and implemented as shiny web application that can be accessed freely from https://ulbbf.shinyapps.io/pwst/.
Indexed as
Identifiers
32148360PMC7059686W2958877445What OpenQuestion holds
Registered trials
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.