Evidence map›Paper›PMID 25446316›Full record

ReviewNature reviews. Genetics2015

Single-cell and multivariate approaches in genetic perturbation screens.

Prisca Liberali, Berend Snijder, Lucas Pelkmans

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing 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

55 citing papers in PubMed.

  1. Article
  2. Integrating Biological Knowledge for Robust Microscopy Image Profiling onProceedings. IEEE International Conference on Computer Vision · 2025
    Article
  3. Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024
    Review
  4. A systematic evaluation of computational methods for cell segmentation.bioRxiv : the preprint server for biology · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Organoids in image-based phenotypic chemical screens.Experimental & molecular medicine · 2021
    Review
  14. Review
  15. Article
  16. Article
  17. Review
  18. Frontiers in aging · 2021
    Review
  19. Article
  20. Review
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

3 authors.

Prisca LiberaliInstitute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland.
Berend Snijder1] Institute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland. [2] CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, A-1090 Vienna, Austria.
Lucas PelkmansInstitute of Molecular Life Sciences, University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large-scale genetic perturbation screens are a classical approach in biology and have been crucial for many discoveries. New technologies can now provide unbiased quantification of multiple molecular and phenotypic changes across tens of thousands of individual cells from large numbers of perturbed cell populations simultaneously. In this Review, we describe how these developments have enabled the discovery of new principles of intracellular and intercellular organization, novel interpretations of genetic perturbation effects and the inference of novel functional genetic interactions. These advances now allow more accurate and comprehensive analyses of gene function in cells using genetic perturbation screens.

Indexed as

Genetic TechniquesPhenotypeRNA InterferenceMultivariate AnalysisMutagenesisSingle-Cell Analysis

Identifiers

PMID25446316

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.