Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

FFPopSim: an efficient forward simulation package for the evolution of large populations.

ABSTRACT:

Motivation

The analysis of the evolutionary dynamics of a population with many polymorphic loci is challenging, as a large number of possible genotypes needs to be tracked. In the absence of analytical solutions, forward computer simulations are an important tool in multi-locus population genetics. The run time of standard algorithms to simulate sexual populations increases as 8(L) with the number of loci L, or with the square of the population size N.

Results

We have developed algorithms to simulate large populations with arbitrary genetic maps, including multiple crossovers, with a run time that scales as 3(L). If the number of crossovers is restricted to at most one, the run time is reduced to L2(L). The algorithm is based on an analogue of the Fast Fourier Transform (FFT) and allows for arbitrary fitness functions (i.e. any epistasis). In addition, we include a streamlined individual-based framework. The library is implemented as a collection of C++ classes and a Python interface.

SUBMITTER: Zanini F

PROVIDER: S-EPMC3519462 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

FFPopSim: an efficient forward simulation package for the evolution of large populations.

Zanini Fabio F Neher Richard A RA

Bioinformatics (Oxford, England) 20121024 24

<h4>Motivation</h4>The analysis of the evolutionary dynamics of a population with many polymorphic loci is challenging, as a large number of possible genotypes needs to be tracked. In the absence of analytical solutions, forward computer simulations are an important tool in multi-locus population genetics. The run time of standard algorithms to simulate sexual populations increases as 8(L) with the number of loci L, or with the square of the population size N.<h4>Results</h4>We have developed al ...[more]

PMID: 23097421

Similar Datasets

A C++ template library for efficient forward-time population genetic simulation of large populations.

Project description:fwdpp is a C++ library of routines intended to facilitate the development of forward-time simulations under arbitrary mutation and fitness models. The library design provides a combination of speed, low memory overhead, and modeling flexibility not currently available from other forward simulation tools. The library is particularly useful when the simulation of large populations is required, as programs implemented using the library are much more efficient than other available forward simulation programs.

| S-EPMC4174927 | biostudies-literature

Exact and efficient phylodynamic simulation from arbitrarily large populations.

Project description:Many biological studies involve inferring the genealogical history of a sample of individuals from a large population and interpreting the reconstructed tree. Such an ascertained tree typically represents only a small part of a comprehensive population tree and is distorted by survivorship and sampling biases. Inferring evolutionary parameters from ascertained trees requires modeling both the underlying population dynamics and the ascertainment process. A crucial component of this phylodynamic modeling involves tree simulation, which is used to benchmark probabilistic inference methods. To simulate an ascertained tree, one must first simulate the full population tree and then prune unobserved lineages. Consequently, the computational cost is determined not by the size of the final simulated tree, but by the size of the population tree in which it is embedded. In most biological scenarios, simulations of the entire population are prohibitively expensive due to computational demands placed on lineages without sampled descendants. Here, we address this challenge by proving that, for any partially ascertained process from a general multi-type birth-death-mutation-sampling (BDMS) model, there exists an equivalent pure birth process (i.e., no death) with mutation and complete sampling. The final trees generated under these processes have exactly the same distribution. Leveraging this property, we propose a highly efficient algorithm for simulating trees under a general BDMS model. Our algorithm scales linearly with the size of the final simulated tree and is independent of the population size, enabling simulations from extremely large populations beyond the reach of current methods but essential for various biological applications. We anticipate that this unprecedented speedup will significantly advance the development of novel inference methods that require extensive training data.

| S-EPMC10925381 | biostudies-literature

STUN: forward-time simulation on TUnable fitNess landscapes in recombining populations

Project description: Not available

| S-EPMC10703519 | biostudies-literature

satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect

Project description:We use stable expression of a cystathionine-beta synthase (CBS) variant library in HEK293T cells to identify CBS coding sequence variants that alter mRNA abundance

2022-04-22 | GSE201057 | GEO

Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes.

Project description:A central challenge in the analysis of genetic variation is to provide realistic genome simulation across millions of samples. Present day coalescent simulations do not scale well, or use approximations that fail to capture important long-range linkage properties. Analysing the results of simulations also presents a substantial challenge, as current methods to store genealogies consume a great deal of space, are slow to parse and do not take advantage of shared structure in correlated trees. We solve these problems by introducing sparse trees and coalescence records as the key units of genealogical analysis. Using these tools, exact simulation of the coalescent with recombination for chromosome-sized regions over hundreds of thousands of samples is possible, and substantially faster than present-day approximate methods. We can also analyse the results orders of magnitude more quickly than with existing methods.

| S-EPMC4856371 | biostudies-literature

phastSim: Efficient simulation of sequence evolution for pandemic-scale datasets.

Project description:Sequence simulators are fundamental tools in bioinformatics, as they allow us to test data processing and inference tools, and are an essential component of some inference methods. The ongoing surge in available sequence data is however testing the limits of our bioinformatics software. One example is the large number of SARS-CoV-2 genomes available, which are beyond the processing power of many methods, and simulating such large datasets is also proving difficult. Here, we present a new algorithm and software for efficiently simulating sequence evolution along extremely large trees (e.g. > 100, 000 tips) when the branches of the tree are short, as is typical in genomic epidemiology. Our algorithm is based on the Gillespie approach, and it implements an efficient multi-layered search tree structure that provides high computational efficiency by taking advantage of the fact that only a small proportion of the genome is likely to mutate at each branch of the considered phylogeny. Our open source software allows easy integration with other Python packages as well as a variety of evolutionary models, including indel models and new hypermutability models that we developed to more realistically represent SARS-CoV-2 genome evolution.

| S-EPMC9094560 | biostudies-literature

SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks.

Project description:Gene co-expression network analysis is extremely useful in interpreting a complex biological process. The recent droplet-based single-cell technology is able to generate much larger gene expression data routinely with thousands of samples and tens of thousands of genes. To analyze such a large-scale gene-gene network, remarkable progress has been made in rigorous statistical inference of high-dimensional Gaussian graphical model (GGM). These approaches provide a formal confidence interval or a p-value rather than only a single point estimator for conditional dependence of a gene pair and are more desirable for identifying reliable gene networks. To promote their widespread use, we herein introduce an extensive and efficient R package named SILGGM (Statistical Inference of Large-scale Gaussian Graphical Model) that includes four main approaches in statistical inference of high-dimensional GGM. Unlike the existing tools, SILGGM provides statistically efficient inference on both individual gene pair and whole-scale gene pairs. It has a novel and consistent false discovery rate (FDR) procedure in all four methodologies. Based on the user-friendly design, it provides outputs compatible with multiple platforms for interactive network visualization. Furthermore, comparisons in simulation illustrate that SILGGM can accelerate the existing MATLAB implementation to several orders of magnitudes and further improve the speed of the already very efficient R package FastGGM. Testing results from the simulated data confirm the validity of all the approaches in SILGGM even in a very large-scale setting with the number of variables or genes to a ten thousand level. We have also applied our package to a novel single-cell RNA-seq data set with pan T cells. The results show that the approaches in SILGGM significantly outperform the conventional ones in a biological sense. The package is freely available via CRAN at https://cran.r-project.org/package=SILGGM.

| S-EPMC6107288 | biostudies-literature

Evolution favors protein mutational robustness in sufficiently large populations.

Project description:BackgroundAn important question is whether evolution favors properties such as mutational robustness or evolvability that do not directly benefit any individual but can influence the course of future evolution. Functionally similar proteins can differ substantially in their robustness to mutations and capacity to evolve new functions, but it has remained unclear whether any of these differences might be due to evolutionary selection for these properties.ResultsHere, we use laboratory experiments to demonstrate that evolution favors protein mutational robustness if the evolving population is sufficiently large. We neutrally evolve cytochrome P450 proteins under identical selection pressures and mutation rates in populations of different sizes, and show that proteins from the larger and thus more polymorphic population tend towards higher mutational robustness. Proteins from the larger population also evolve greater stability, a biophysical property that is known to enhance both mutational robustness and evolvability. The excess mutational robustness and stability is well described by mathematical theory, and can be quantitatively related to the way that the proteins occupy their neutral network.ConclusionOur work is the first experimental demonstration of the general tendency of evolution to favor mutational robustness and protein stability in highly polymorphic populations. We suggest that this phenomenon could contribute to the mutational robustness and evolvability of viruses and bacteria that exist in large populations.

| S-EPMC1995189 | biostudies-literature

Evolution of Mutation Rate in Astronomically Large Phytoplankton Populations.

Project description:Genetic diversity is expected to be proportional to population size, yet, there is a well-known, but unexplained lack of genetic diversity in large populations-the "Lewontin's paradox." Larger populations are expected to evolve lower mutation rates, which may help to explain this paradox. Here, we test this conjecture by measuring the spontaneous mutation rate in a ubiquitous unicellular marine phytoplankton species Emiliania huxleyi (Haptophyta) that has modest genetic diversity despite an astronomically large population size. Genome sequencing of E. huxleyi mutation accumulation lines revealed 455 mutations, with an unusual GC-biased mutation spectrum. This yielded an estimate of the per site mutation rate µ = 5.55×10-10 (CI 95%: 5.05×10-10 - 6.09×10-10), which corresponds to an effective population size Ne ∼ 2.7×106. Such a modest Ne is surprising for a ubiquitous and abundant species that accounts for up to 10% of global primary productivity in the oceans. Our results indicate that even exceptionally large populations do not evolve mutation rates lower than ∼10-10 per nucleotide per cell division. Consequently, the extreme disparity between modest genetic diversity and astronomically large population size in the plankton species cannot be explained by an unusually low mutation rate.

| S-EPMC7486954 | biostudies-literature

Hi-Corrector: a fast, scalable and memory-efficient package for normalizing large-scale Hi-C data.

Project description:Genome-wide proximity ligation assays, e.g. Hi-C and its variant TCC, have recently become important tools to study spatial genome organization. Removing biases from chromatin contact matrices generated by such techniques is a critical preprocessing step of subsequent analyses. The continuing decline of sequencing costs has led to an ever-improving resolution of the Hi-C data, resulting in very large matrices of chromatin contacts. Such large-size matrices, however, pose a great challenge on the memory usage and speed of its normalization. Therefore, there is an urgent need for fast and memory-efficient methods for normalization of Hi-C data. We developed Hi-Corrector, an easy-to-use, open source implementation of the Hi-C data normalization algorithm. Its salient features are (i) scalability-the software is capable of normalizing Hi-C data of any size in reasonable times; (ii) memory efficiency-the sequential version can run on any single computer with very limited memory, no matter how little; (iii) fast speed-the parallel version can run very fast on multiple computing nodes with limited local memory.The sequential version is implemented in ANSI C and can be easily compiled on any system; the parallel version is implemented in ANSI C with the MPI library (a standardized and portable parallel environment designed for solving large-scale scientific problems). The package is freely available at http://zhoulab.usc.edu/Hi-Corrector/.

| S-EPMC4380031 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data