Dataset Information

Gargammel: a sequence simulator for ancient DNA.

ABSTRACT: Summary:Ancient DNA has emerged as a remarkable tool to infer the history of extinct species and past populations. However, many of its characteristics, such as extensive fragmentation, damage and contamination, can influence downstream analyses. To help investigators measure how these could impact their analyses in silico , we have developed gargammel, a package that simulates ancient DNA fragments given a set of known reference genomes. Our package simulates the entire molecular process from post-mortem DNA fragmentation and DNA damage to experimental sequencing errors, and reproduces most common bias observed in ancient DNA datasets. Availability and Implementation:The package is publicly available on github: https://grenaud.github.io/gargammel/ and released under the GPL. Contact:gabriel.renaud@snm.ku.dk. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Renaud G

PROVIDER: S-EPMC5408798 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

gargammel: a sequence simulator for ancient DNA.

Renaud Gabriel G Hanghøj Kristian K Willerslev Eske E Orlando Ludovic L

Bioinformatics (Oxford, England) 20170201 4

<h4>Summary</h4>Ancient DNA has emerged as a remarkable tool to infer the history of extinct species and past populations. However, many of its characteristics, such as extensive fragmentation, damage and contamination, can influence downstream analyses. To help investigators measure how these could impact their analyses in silico , we have developed gargammel, a package that simulates ancient DNA fragments given a set of known reference genomes. Our package simulates the entire molecular proces ...[more]

PMID: 27794556

Similar Datasets

Project description:Background Simulating DNA evolution has been done through coevolution-agnostic probabilistic frameworks for the past 3 decades. The most common implementation is by using the converse of the probabilistic approach used to infer phylogenies which, in the simplest form, simulates a single sequence at a time. However, biological systems are multi-genic, and gene products can affect each other’s evolutionary paths through coevolution. These crucial evolutionary dynamics still remain to be simulated, and we believe that modelling them can lead to profound insights for comparative genomics. Results Here we present CastNet, a genome evolution simulator that assumes each genome is a collection of genes with constantly evolving regulatory interactions in between them. The regulatory interactions produce a phenotype in the form of gene expression profiles, upon which fitness is calculated. A genetic algorithm is then used to evolve a population of such entities through a user-defined phylogeny. Importantly, the regulatory mutations are a response to sequence mutations, thus making a 1–1 relationship between the rate of evolution of sequences and of regulatory parameters. This is, to our knowledge, the first time the evolution of sequences and regulation have been explicitly linked in a simulation, despite there being a multitude of sequence evolution simulators, and a handful of models to simulate Gene Regulatory Network (GRN) evolution. In our test runs, we see a coevolutionary signal among genes that are active in the GRN, and neutral evolution in genes that are not included in the network, showing that selective pressures imposed on the regulatory output of the genes are reflected in their sequences. Conclusion We believe that CastNet represents a substantial step for developing new tools to study genome evolution, and more broadly, coevolutionary webs and complex evolving systems. This simulator also provides a new framework to study molecular evolution where sequence coevolution has a leading role. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-023-05366-1.

Project description:BackgroundThere are many programs available for generating simulated whole-genome shotgun sequence reads. The data generated by many of these programs follow predefined models, which limits their use to the authors' original intentions. For example, many models assume that read lengths follow a uniform or normal distribution. Other programs generate models from actual sequencing data, but are limited to reads from single-genome studies. To our knowledge, there are no programs that allow a user to generate simulated data following non-parametric read-length distributions and quality profiles based on empirically-derived information from metagenomics sequencing data.ResultsWe present BEAR (Better Emulation for Artificial Reads), a program that uses a machine-learning approach to generate reads with lengths and quality values that closely match empirically-derived distributions. BEAR can emulate reads from various sequencing platforms, including Illumina, 454, and Ion Torrent. BEAR requires minimal user input, as it automatically determines appropriate parameter settings from user-supplied data. BEAR also uses a unique method for deriving run-specific error rates, and extracts useful statistics from the metagenomic data itself, such as quality-error models. Many existing simulators are specific to a particular sequencing technology; however, BEAR is not restricted in this way. Because of its flexibility, BEAR is particularly useful for emulating the behaviour of technologies like Ion Torrent, for which no dedicated sequencing simulators are currently available. BEAR is also the first metagenomic sequencing simulator program that automates the process of generating abundances, which can be an arduous task.ConclusionsBEAR is useful for evaluating data processing tools in genomics. It has many advantages over existing comparable software, such as generating more realistic reads and being independent of sequencing technology, and has features particularly useful for metagenomics work.

Dataset Information

Gargammel: a sequence simulator for ancient DNA.

Publications

gargammel: a sequence simulator for ancient DNA.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets