Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Quantifying Selection with Pool-Seq Time Series Data.

Molecular biology and evolution - 1 Nov 2017

Taus Thomas, Futschik Andreas, Schlötterer Christian

Abstract excerpt

Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Adaptation, Physiological
Share to Adaptation, Biological
Share to Biological Evolution

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Computer Simulation

Share to Evolution, Molecular

Share to Genome

Share to Selection, Genetic

Share to Whole Genome Sequencing

Source

Open in PubMed Open full text

Identifiers

PMID: 28961717
DOI: 10.1093/molbev/msx225
PMCID: PMC5850601

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Quantifying Selection with Pool-Seq Time Series Data.PMID 28961717Read ↗

Increased time sampling in an evolve-and-resequence experiment with outcrossing Saccharomyces cerevisiae reveals multiple paths of adaptive change.Semantic proximity Read ↗

Reconstruction of Haplotype-Blocks Selected during Experimental Evolution.Semantic proximity Read ↗

Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variation.Semantic proximity Read ↗

A guide for the design of evolve and resequencing studies.Semantic proximity Read ↗

Forecasting Genomic Change with Time Series Sequence Data.Semantic proximity Read ↗

Estimating the genome-wide contribution of selection to temporal allele frequency change.Semantic proximity Read ↗

Estimating the Effective Population Size from Temporal Allele Frequency Changes in Experimental Evolution.Semantic proximity Read ↗

Quantifying selection acting on a complex trait using allele frequency time series data.Semantic proximity Read ↗

Uncovering the genetic signature of quantitative trait evolution with replicated time series data.Semantic proximity Read ↗

Elucidating the molecular architecture of adaptation via evolve and resequence experiments.Semantic proximity Read ↗

grenedalf: population genetic statistics for the next generation of pool sequencing.Semantic proximity Read ↗

Continuously fluctuating selection reveals fine granularity of adaptation.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.