Evidence map›Paper›PMID 24995987›Full record

ArticleCell2014

Expansion of biological pathways based on evolutionary inference.

Yang Li, Sarah E Calvo, Roee Gutman, Jun S Liu, Vamsi K Mootha

Abstract read
In one paragraph

Article in Cell, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing 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

71 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Mitochondrial proteome research: the road ahead.Nature reviews. Molecular cell biology · 2024
    Review
  11. Article
  12. Article
  13. Article
  14. Identification of hidden associations among eukaryotic genes through statistical analysis of coevolutionary transitions.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
  20. Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Yang LiHoward Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
Sarah E CalvoHoward Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute, Cambridge, MA 02141, USA.
Roee GutmanDepartment of Biostatistics, Brown University, Providence, RI 02912, USA.
Jun S LiuDepartment of Statistics, Harvard University, Cambridge, MA 02138, USA. Electronic address: jliu@stat.harvard.edu.
Vamsi K MoothaHoward Hughes Medical Institute and Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute, Cambridge, MA 02141, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA. Electronic address: vamsi@hms.harvard.edu.

Funding

Characterization of the Mitochondrial ProteomeR01GM077465 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MOOTHA, VAMSI KRISHNA · 2007 to 2016
$4.3M
Howard Hughes Medical InstituteNIGMS NIH HHS GM0077465NIGMS NIH HHS R01 GM077465
6 · The paper itself

Abstract

The availability of diverse genomes makes it possible to predict gene function based on shared evolutionary history. This approach can be challenging, however, for pathways whose components do not exhibit a shared history but rather consist of distinct "evolutionary modules." We introduce a computational algorithm, clustering by inferred models of evolution (CLIME), which inputs a eukaryotic species tree, homology matrix, and pathway (gene set) of interest. CLIME partitions the gene set into disjoint evolutionary modules, simultaneously learning the number of modules and a tree-based evolutionary history that defines each module. CLIME then expands each module by scanning the genome for new components that likely arose under the inferred evolutionary model. Application of CLIME to ∼1,000 annotated human pathways and to the proteomes of yeast, red algae, and malaria reveals unanticipated evolutionary modularity and coevolving components. CLIME is freely available and should become increasingly powerful with the growing wealth of eukaryotic genomes.

Indexed as

AlgorithmsCluster AnalysisPhylogenyHumansMitochondriaPlasmodium falciparumProteomeRhodophytaSignal TransductionYeastsProteome

Identifiers

PMID24995987
PMCPMC4171950

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.