ArticleCell discovery2019
MAP: model-based analysis of proteomic data to detect proteins with significant abundance changes.
Article in Cell discovery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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.
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Who cites it
13 citing papers in PubMed.
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- Omic AI reveals new autophagy regulators from the Atg1 interactome inFrontiers in cell and developmental biology · 2025Article
- Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics.BME frontiers · 2025Review
- A proteogenomic analysis of cervical cancer reveals therapeutic and biological insights.Nature communications · 2024Article
- zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation.Genome biology · 2024Article
- The Crk4-Cyc4 complex regulates GThe EMBO journal · 2024Article
- Article
- ATR activity controls stem cell quiescence via the cyclin F-SCF complex.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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- Assessing the statistical training in animal science graduate programs in the United States: survey on statistical training.Journal of animal science · 2021Article
- Article
- Bioinformatic Analysis of Temporal and Spatial Proteome Alternations During Infections.Frontiers in genetics · 2021Review
- Protein-driven mechanism of multiorgan damage in COVID-19.Medicine in drug discovery · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Isotope-labeling-based mass spectrometry (MS) is widely used in quantitative proteomic studies. With this technique, the relative abundance of thousands of proteins can be efficiently profiled in parallel, greatly facilitating the detection of proteins differentially expressed across samples. However, this task remains computationally challenging. Here we present a new approach, termed Model-based Analysis of Proteomic data (MAP), for this task. Unlike many existing methods, MAP does not require technical replicates to model technical and systematic errors, and instead utilizes a novel step-by-step regression analysis to directly assess the significance of observed protein abundance changes. We applied MAP to compare the proteomic profiles of undifferentiated and differentiated mouse embryonic stem cells (mESCs), and found it has superior performance compared with existing tools in detecting proteins differentially expressed during mESC differentiation. A web-based application of MAP is provided for online data processing at http://bioinfo.sibs.ac.cn/shaolab/MAP.
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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.