ArticleBMC bioinformatics2013
Rapid forward-in-time simulation at the chromosome and genome level.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Efficient pedigree recording for fast population genetics simulation.PLoS computational biology · 2018Article
- Inference of the Distribution of Selection Coefficients for New Nonsynonymous Mutations Using Large Samples.Genetics · 2017Article
- Clotho: addressing the scalability of forward time population genetic simulation.BMC bioinformatics · 2015Article
- XSim: Simulation of Descendants from Ancestors with Sequence Data.G3 (Bethesda, Md.) · 2015Article
- Robust forward simulations of recurrent hitchhiking.Genetics · 2014Article
- The power to detect quantitative trait loci using resequenced, experimentally evolved populations of diploid, sexual organisms.Molecular biology and evolution · 2014Article
- forqs: forward-in-time simulation of recombination, quantitative traits and selection.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn population genetics, simulation is a fundamental tool for analyzing how basic evolutionary forces such as natural selection, recombination, and mutation shape the genetic landscape of a population. Forward simulation represents the most powerful, but, at the same time, most compute-intensive approach for simulating the genetic material of a population.
resultsWe introduce AnA-FiTS, a highly optimized forward simulation software, that is up to two orders of magnitude faster than current state-of-the-art software. In addition, we present a novel algorithm that further improves runtimes by up to an additional order of magnitude, for simulations where a fraction of the mutations is neutral (e.g., only 10% of mutations have an effect on fitness). Apart from simulated sequences, our tool also generates a graph structure that depicts the complete observable history of neutral mutations.
conclusionsThe substantial performance improvements allow for conducting forward simulations at the chromosome and genome level. The graph structure generated by our algorithm can give rise to novel approaches for visualizing and analyzing the output of forward simulations.
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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.