Dataset Information

MetaSim: a sequencing simulator for genomics and metagenomics.

ABSTRACT: 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.

SUBMITTER: Richter DC

PROVIDER: S-EPMC2556396 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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MetaSim: a sequencing simulator for genomics and metagenomics.

Richter Daniel C DC Ott Felix F Auch Alexander F AF Schmid Ramona R Huson Daniel H DH

PloS one 20081008 10

<h4>Background</h4>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.<h4>Methodology/principal findings</h4>To facilitate the development and improvement of metagenomic tools and the ...[more]

PMID: 18841204

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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.

Project description:The canonical pathway for sucrose metabolism in haloarchaea utilizes a modified Embden-Meyerhof-Parnas pathway (EMP), in which ketohexokinase and 1-phosphofructokinase phosphorylate fructose released from sucrose hydrolysis. However, our survey of haloarchaeal genomes determined that ketohexokinase and 1-phosphofructokinase genes were not present in all species known to utilize fructose and sucrose, thereby indicating that alternative mechanisms exist for fructose metabolism. A fructokinase gene was identified in the majority of fructose- and sucrose-utilizing species, whereas only a small number possessed a ketohexokinase gene. Analysis of a range of hypersaline metagenomes revealed that haloarchaeal fructokinase genes were far more abundant (37 times) than haloarchaeal ketohexokinase genes. We used proteomic analysis of Halohasta litchfieldiae (which encodes fructokinase) and identified changes in protein abundance that relate to growth on sucrose. Proteins inferred to be involved in sucrose metabolism included fructokinase, a carbohydrate primary transporter, a putative sucrose hydrolase, and two uncharacterized carbohydrate-related proteins encoded in the same gene cluster as fructokinase and the transporter. Homologs of these proteins were present in the genomes of all haloarchaea that use sugars for growth. Enzymes involved in the semiphosphorylative Entner-Doudoroff pathway also had higher abundances in sucrose-grown H. litchfieldiae cells, consistent with this pathway functioning in the catabolism of the glucose moiety of sucrose. The study revises the current understanding of fundamental pathways for sugar utilization in haloarchaea and proposes alternatives to the modified EMP pathway used by haloarchaea for sucrose and fructose utilization.IMPORTANCE Our ability to infer the function that microorganisms perform in the environment is predicated on assumptions about metabolic capacity. When genomic or metagenomic data are used, metabolic capacity is inferred from genetic potential. Here, we investigate the pathways by which haloarchaea utilize sucrose. The canonical haloarchaeal pathway for fructose metabolism involving ketohexokinase occurs only in a small proportion of haloarchaeal genomes and is underrepresented in metagenomes. Instead, fructokinase genes are present in the majority of genomes/metagenomes. In addition to genomic and metagenomic analyses, we used proteomic analysis of Halohasta litchfieldiae (which encodes fructokinase but lacks ketohexokinase) and identified changes in protein abundance that related to growth on sucrose. In this way, we identified novel proteins implicated in sucrose metabolism in haloarchaea, comprising a transporter and various catabolic enzymes (including proteins that are annotated as hypothetical).

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MetaSim: a sequencing simulator for genomics and metagenomics.

Publications

MetaSim: a sequencing simulator for genomics and metagenomics.

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