ArticleAdvances in biological regulation2018
Normalization of mass spectrometry data (NOMAD).
Article in Advances in biological regulation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed, 18 citations in OpenAlex.
- Studying Autophagy Using a TMT-Based Quantitative Proteomics Approach.Methods in molecular biology (Clifton, N.J.) · 2022Article
- The mRNA (New York, N.Y.) · 2021Article
- Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections.Frontiers in genetics · 2021Review
- What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows.Proteomes · 2019Review
- Distinct Cancer-Promoting Stromal Gene Expression Depending on Lung Function.American journal of respiratory and critical care medicine · 2019Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
iTRAQ and TMT reagent-based mass spectrometry (MS) are commonly used technologies for quantitative proteomics in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. Such MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that reference normalization does not effectively remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data sets. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality. NOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD effectively removes bias which improves valid across MS run comparisons.
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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.