Evidence map›Paper›PMID 27145880›Full record

ReviewAnnual review of biophysics2016

Transcription Dynamics in Living Cells.

Tineke L Lenstra, Joseph Rodriguez, Huimin Chen, Daniel R Larson

Abstract readReview
In one paragraph

Review in Annual review of biophysics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed
–field-weighted citation impact
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

93 citing papers in PubMed.

  1. Article
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  3. Review
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  5. Review
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  17. Review
  18. Article
  19. Review
  20. Article

33 more citing papers are in PubMed but not listed here.

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

4 authors.

Tineke L LenstraLaboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892; email: dan.larson@nih.gov.
Joseph RodriguezLaboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892; email: dan.larson@nih.gov.
Huimin ChenLaboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892; email: dan.larson@nih.gov.
Daniel R LarsonLaboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland 20892; email: dan.larson@nih.gov.

Funding

Transcription Dynamics in Single CellsZIABC011383 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI LARSON, DANIEL · 2011 to 2025
$23.8M
Intramural NIH HHS Z99 CA999999
6 · The paper itself

Abstract

The transcription cycle can be roughly divided into three stages: initiation, elongation, and termination. Understanding the molecular events that regulate all these stages requires a dynamic view of the underlying processes. The development of techniques to visualize and quantify transcription in single living cells has been essential in revealing the transcription kinetics. They have revealed that (a) transcription is heterogeneous between cells and (b) transcription can be discontinuous within a cell. In this review, we discuss the progress in our quantitative understanding of transcription dynamics in living cells, focusing on all parts of the transcription cycle. We present the techniques allowing for single-cell transcription measurements, review evidence from different organisms, and discuss how these experiments have broadened our mechanistic understanding of transcription regulation.

Indexed as

Transcription, GeneticAnimalsBacteriaGene Expression RegulationHumansRNASingle-Cell AnalysisSpecies SpecificityRNAburstingfluorescenceheterogeneityimagingkineticssingle-cellsingle-molecule

Identifiers

PMID27145880
PMCPMC6300980

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.