Evidence map›Paper›PMID 26682651›Full record

ArticleeLife2015

Evidence for a common evolutionary rate in metazoan transcriptional networks.

Anne-Ruxandra Carvunis, Tina Wang, Dylan Skola, Alice Yu, Jonathan Chen, Jason F Kreisberg, Trey Ideker

Abstract read
In one paragraph

Article in eLife, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Embryonic reprogramming of the tumor vasculature reveals targets for cancer therapy.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Article
  4. SAGA Complex Subunits inFrontiers in cellular and infection microbiology · 2022
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. The rewiring of transcription circuits in evolution.Current opinion in genetics & development · 2017
    Review
  13. Article
  14. Review
  15. Comparative transcriptomics in human and mouse.Nature reviews. Genetics · 2017
    Review
  16. Article
  17. Review
  18. Article
  19. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Anne-Ruxandra CarvunisDepartment of Medicine, University of California, San Diego, La Jolla, United States.ORCID http://orcid.org/0000-0002-6474-6413
Tina WangDepartment of Medicine, University of California, San Diego, La Jolla, United States.
Dylan SkolaDepartment of Medicine, University of California, San Diego, La Jolla, United States.
Alice YuDepartment of Medicine, University of California, San Diego, La Jolla, United States.
Jonathan ChenDepartment of Medicine, University of California, San Diego, La Jolla, United States.
Jason F KreisbergDepartment of Medicine, University of California, San Diego, La Jolla, United States.
Trey IdekerDepartment of Medicine, University of California, San Diego, La Jolla, United States.

Funding

SYNTHETIC SYSTEMSP50GM085764 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WINZELER, ELIZABETH A · 2010 to 2018
$23.3M
TR&D 3 - Network Guided Machine LearningP41GM103504 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI IDEKER, TREY · 2012 to 2024
$17.3M
Training Program in Basic Clinical GeneticsT32GM008666 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HAMILTON, BRUCE A · 1998 to 2021
$8.6M
Comparative Analysis of Genetic Interaction NetworksR01GM084279 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI IDEKER, TREY, KROGAN, NEVAN J · 2008 to 2017
$3.9M
Deciphering the mechanisms and dynamics of proto-gene evolutionR00GM108865 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CARVUNIS, ANNE-RUXANDRA · 2017 to 2019
$747k
Deciphering the mechanisms and dynamics of proto-gene evolutionK99GM108865 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CARVUNIS, ANNE-RUXANDRA · 2014 to 2015
$180k
NIGMS NIH HHS K99 GM108865NIGMS NIH HHS P41 GM103504NIGMS NIH HHS P50 GM085764NIGMS NIH HHS R00 GM108865NIGMS NIH HHS R01 GM084279NIGMS NIH HHS T32 GM008666
6 · The paper itself

Abstract

Genome sequences diverge more rapidly in mammals than in other animal lineages, such as birds or insects. However, the effect of this rapid divergence on transcriptional evolution remains unclear. Recent reports have indicated a faster divergence of transcription factor binding in mammals than in insects, but others found the reverse for mRNA expression. Here, we show that these conflicting interpretations resulted from differing methodologies. We performed an integrated analysis of transcriptional network evolution by examining mRNA expression, transcription factor binding and cis-regulatory motifs across >25 animal species, including mammals, birds and insects. Strikingly, we found that transcriptional networks evolve at a common rate across the three animal lineages. Furthermore, differences in rates of genome divergence were greatly reduced when restricting comparisons to chromatin-accessible sequences. The evolution of transcription is thus decoupled from the global rate of genome sequence evolution, suggesting that a small fraction of the genome regulates transcription.

Indexed as

Evolution, MolecularGene Regulatory NetworksAnimalsBirdsInsectaMammalschickencis-regulatory sequencescomparative genomicscomputational biologyD. melanogasterevolutionevolutionary biologygenome evolutiongenomicshumanmouseratsystems biologytranscriptiontranscription factor binding

Identifiers

PMID26682651
PMCPMC4764585

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.