Evidence map›Paper›PMID 24229407›Full record

ArticleJournal of proteome research2014

PECA: a novel statistical tool for deconvoluting time-dependent gene expression regulation.

Guoshou Teo, Christine Vogel, Debashis Ghosh, Sinae Kim, Hyungwon Choi

Open access · greenAbstract read
In one paragraph

Article in Journal of proteome research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
0.9field-weighted citation impact, top 25% of its field
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

15 citing papers in PubMed, 30 citations in OpenAlex.

  1. Estimating protein isoform abundances with [Formula: see text].Proceedings of the National Academy of Sciences of the United States of America · 2026
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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

5 authors at 4 institutions in 2 countries.

Guoshou TeoDepartment of Statistics and Applied Probability, National University of Singapore , Block S16, Level 7, 6 Science Drive 2, 117546 Singapore.
Christine Vogel
Debashis Ghosh
Sinae Kim
Hyungwon Choi
National University of Singapore · SGNew York University · USPennsylvania State University · USRutgers, The State University of New Jersey · US

Funding

Next generation gene expression analysisR35GM127089 · NIGMS · NEW YORK UNIVERSITY · PI Christine Vogel · 2018 to 2026
$4.0M
Robust statistical approaches for decoding protein and mRNA expression regulationR01GM113237 · NIGMS · NEW YORK UNIVERSITY · PI VOGEL, CHRISTINE · 2014 to 2017
$1.5M
Statistical Methods for the Analysis of Microarray DataR01GM072007 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GHOSH, DEBASHIS · 2004 to 2008
$1.1M
NIGMS NIH HHS R01 GM072007NIGMS NIH HHS R01 GM113237NIGMS NIH HHS R35 GM127089
6 · The paper itself

Abstract

Protein expression varies as a result of intricate regulation of synthesis and degradation of messenger RNAs (mRNA) and proteins. Studies of dynamic regulation typically rely on time-course data sets of mRNA and protein expression, yet there are no statistical methods that integrate these multiomics data and deconvolute individual regulatory processes of gene expression control underlying the observed concentration changes. To address this challenge, we developed Protein Expression Control Analysis (PECA), a method to quantitatively dissect protein expression variation into the contributions of mRNA synthesis/degradation and protein synthesis/degradation, termed RNA-level and protein-level regulation respectively. PECA computes the rate ratios of synthesis versus degradation as the statistical summary of expression control during a given time interval at each molecular level and computes the probability that the rate ratio changed between adjacent time intervals, indicating regulation change at the time point. Along with the associated false-discovery rates, PECA gives the complete description of dynamic expression control, that is, which proteins were up- or down-regulated at each molecular level and each time point. Using PECA, we analyzed two yeast data sets monitoring the cellular response to hyperosmotic and oxidative stress. The rate ratio profiles reported by PECA highlighted a large magnitude of RNA-level up-regulation of stress response genes in the early response and concordant protein-level regulation with time delay. However, the contributions of RNA- and protein-level regulation and their temporal patterns were different between the two data sets. We also observed several cases where protein-level regulation counterbalanced transcriptomic changes in the early stress response to maintain the stability of protein concentrations, suggesting that proteostasis is a proteome-wide phenomenon mediated by post-transcriptional regulation.

Indexed as

Gene Expression RegulationModels, StatisticalOxidative StressRNA, MessengerRNA Processing, Post-TranscriptionalSchizosaccharomycesRNA, Messenger

Identifiers

PMID24229407
PMCPMC10332356
OpenAlexW2323041333

What OpenQuestion holds

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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.