ArticleNPJ systems biology and applications2018
PECAplus: statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments.
Article in NPJ systems biology and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer.Nature communications · 2025Article
- Integration of a multi-omics stem cell differentiation dataset using a dynamical model.PLoS genetics · 2023Article
- Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover.Proteomics · 2023Article
- BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples.Analytical chemistry · 2021Article
- Proteome Turnover in the Spotlight: Approaches, Applications, and Perspectives.Molecular & cellular proteomics : MCP · 2021Review
- Chemical chaperones reverse early suppression of regulatory circuits during unfolded protein response in B cells from common variable immunodeficiency patients.Clinical and experimental immunology · 2020Article
- Exploiting Interdata Relationships in Next-generation Proteomics Analysis.Molecular & cellular proteomics : MCP · 2019Review
- Article
- Integration of large-scale multi-omic datasets: a protein-centric view.Current opinion in systems biology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Simultaneous dynamic profiling of mRNA and protein expression is increasingly popular, and there is a critical need for algorithms to identify regulatory layers and time dependency of gene expression. A group of scientists from United States and Singapore present PECAplus, a comprehensive set of statistical analysis tools to address this challenge. Protein expression control analysis (PECA) computes the probability scores for change in mRNA and protein-level regulatory parameters at each time point, deconvoluting gene expression regulation in the presence of measurement noise. PECAplus adapted PECA's mass action model to a variety of proteomic data including pulsed SILAC and generic protein expression data. It also features analysis modules to fit smooth curves on rugged time series observations, and to facilitate time-dependent interpretation of the data for genes and biological functions. They demonstrate the core modules with two time course datasets of mammalian cells responding to unfolded proteins and pathogens.
Identifiers
What 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.