MetaSim—A Sequencing Simulator for Genomics and Metagenomics

By Daniel C. Richter, Felix Ott, Alexander F. Auch, R. Schmid, Daniel H. Huson. PLoS ONE, 2008

461 citations recorded by the source corpus.

Background The new research field of metagenomics is providing exciting insights into various, previously unclassified ecological systems. Next-generation sequencing technologies are producing a rapid increase of environmental data in public databases. There is great need for specialized software solutions and statistical methods for dealing with complex metagenome data sets. Methodology/Principal Findings To facilitate the development and improvement of metagenomic tools and the planning of metagenomic projects, we introduce a sequencing simulator called MetaSim. Our software can be used to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets. Based on a database of given genomes, the program allows the user to design a metagenome by specifying the number of genomes present at different levels of the NCBI taxonomy, and then to collect reads from the metagenome using a simulation of a number of different sequencing technologies. A population sampler optionally produces evolved sequences based on source genomes and a given evolutionary tree. Conclusions/Significance MetaSim allows the user to simulate individual read datasets that can be used as standardized test scenarios for planning sequencing projects or for benchmarking metagenomic software.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8ef57b9d43092057891945209b722f5a03e01b0f

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Citations in the corpus

Modelling and simulating generic RNA-Seq experiments with the flux simulator (2012, background): “insertions/deletions (indels) typically occur in reads produced by Roche pyrosequencing (22), whereas Illumina sequencing platforms mainly exhibit read sequences with an increased rate of nucleotide substitutions—and a correspondingly decreased quality—towards the read end (6).”
SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data (2012, background): “Additionally, the user can work with his or her own RNA databases.”
LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets (2012, methodology): “Simulated reads were generated using Metasim (RefSeq entry NC_000913 as reference) with error rates and number of reads set to those observed from the real dataset.”
Scalable metagenomic taxonomy classification using a reference genome database (2013, methodology): “MetaSim was used with a 100 bp (Barthelson et al., 2011) and 80 bp (Richter et al., 2008) Illumina error model to generate the novel bacterial and viral dataset, respectively.”
BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons (2011, methodology): “To illustrate this feature, we have simulated unas-sembled Illumina data using MetaSim [21] by randomly sampling one million 100 base pair sequences from the complete genome sequences shown in Figure 3A and applying an Illumina error model.”
WGSQuikr: Fast Whole-Genome Shotgun Metagenomic Classification (2014, methodology): “In particular, these parameters were chosen as follows: read lengths normally distributed with a mean of 150 bp and a standard deviation of 5 bp, 1 M total reads, a power law abundance model, a diversity of 10 species, and the homopolymer error model as in [19].”
MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm (2014, methodology): “Simulated metagenomes with 10 species and 100 species were generated by MetaSim [22] and assembled by Velvet assembler 1.”
Orphelia: predicting genes in metagenomic sequencing reads (2009, methodology): “lated using the MetaSim software (10) and the same test species as before.”
FragGeneScan: predicting genes in short and error-prone reads (2010, methodology): “%) were simulated from these genomes using MetaSim (10).”
ART: a next-generation sequencing read simulator (2012, unknown): “We currently support all three major commercial next-generation sequencing platforms: Roche’s 454, Illumina’s Solexa and Applied Biosystems’ SOLiD.”