ArticleScientific reports2016
Quantifying evolutionary dynamics from variant-frequency time series.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Article
- Contemporary NMolecular ecology resources · 2021Article
- Allele frequency divergence reveals ubiquitous influence of positive selection in Drosophila.PLoS genetics · 2021Article
- Neutral syndrome.Nature human behaviour · 2020Review
- Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules ofNAR genomics and bioinformatics · 2020Article
- Robust Estimation of Recent Effective Population Size from Number of Independent Origins in Soft Sweeps.Molecular biology and evolution · 2019Article
- Inferring population genetics parameters of evolving viruses using time-series data.Virus evolution · 2019Article
- Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.Genetics · 2018Article
- Quantifying Selection with Pool-Seq Time Series Data.Molecular biology and evolution · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
From Kimura's neutral theory of protein evolution to Hubbell's neutral theory of biodiversity, quantifying the relative importance of neutrality versus selection has long been a basic question in evolutionary biology and ecology. With deep sequencing technologies, this question is taking on a new form: given a time-series of the frequency of different variants in a population, what is the likelihood that the observation has arisen due to selection or neutrality? To tackle the 2-variant case, we exploit Fisher's angular transformation, which despite being discovered by Ronald Fisher a century ago, has remained an intellectual curiosity. We show together with a heuristic approach it provides a simple solution for the transition probability density at short times, including drift, selection and mutation. Our results show under that under strong selection and sufficiently frequent sampling these evolutionary parameters can be accurately determined from simulation data and so they provide a theoretical basis for techniques to detect selection from variant or polymorphism frequency time-series.
Identifiers
What OpenQuestion holds
Registered trials
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.