Evidence map›Paper›PMID 34987185›Full record

ArticleHeredity2022

Inferring the distribution of fitness effects in patient-sampled and experimental virus populations: two case studies.

Ana Y Morales-Arce, Parul Johri, Jeffrey D Jensen

Abstract read
In one paragraph

Article in Heredity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Inferring the demographic history of aye-ayes (bioRxiv : the preprint server for biology · 2024
    Article
  10. Article
  11. Article
  12. 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.

Ana Y Morales-Arce *Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA.ORCID http://orcid.org/0000-0002-2934-3141
Parul Johri *Center for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA.
Jeffrey D JensenCenter for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA. jeffrey.d.jensen@asu.edu.

Funding

On differentiating selective and neutral evolutionary processesR35GM139383 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI JENSEN, JEFFREY D · 2021 to 2025
$3.0M
Developing novel statistics and coalescent approaches for the improved study of virus evolution R01GM135899 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI JENSEN, JEFFREY D · 2019 to 2020
$367k
NIGMS NIH HHS R01 GM135899NIGMS NIH HHS R35 GM139383
6 · The paper itself

Abstract

We here propose an analysis pipeline for inferring the distribution of fitness effects (DFE) from either patient-sampled or experimentally-evolved viral populations, that explicitly accounts for non-Wright-Fisher and non-equilibrium population dynamics inherent to pathogens. We examine the performance of this approach via extensive power and performance analyses, and highlight two illustrative applications - one from an experimentally-passaged RNA virus, and the other from a clinically-sampled DNA virus. Finally, we discuss how such DFE inference may shed light on major research questions in virus evolution, ranging from a quantification of the population genetic processes governing genome size, to the role of Hill-Robertson interference in dictating adaptive outcomes, to the potential design of novel therapeutic approaches to eradicate within-patient viral populations via induced mutational meltdown.

Indexed as

Models, GeneticVirusesDNA VirusesEvolution, MolecularGenetic FitnessHumansMutationSelection, Genetic

Identifiers

PMID34987185
PMCPMC8728706

What OpenQuestion holds

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Registered trials

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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.