MetaSim—A Sequencing Simulator for Genomics and Metagenomics

Richter, Daniel C.; Ott, Felix; Auch, Alexander F.; Schmid, RM; Huson, Daniel H.

doi:10.1371/journal.pone.0003373

Cited by 401 publications

(319 citation statements)

References 42 publications

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“…Unfortunately, this method did not allow us to model different error rates conditioned on different underlying bases, which we felt was important. We also considered several other software packages for modeling Illumina style reads, including metasim (Richter et al 2008), PEMer (Korbel et al 2009), ReSeqSim (Du et al 2009), SimNext (http://evolution.sysu.edu.cn/ english/software/simnext.htm), Flux Simulator (http://flux.sammeth. net/index.html), and Mason (part of the SeqAn package) (Döring et al 2008), all of which lack one or more of the criteria we desired.…”

Section: Read Simulationmentioning

confidence: 99%

Assemblathon 1: A competitive assessment of de novo short read assembly methods

Earl¹,

Bradnam²,

John³

et al. 2011

Genome Res.

467

422

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Low-cost short read sequencing technology has revolutionized genomics, though it is only just becoming practical for the high-quality de novo assembly of a novel large genome. We describe the Assemblathon 1 competition, which aimed to comprehensively assess the state of the art in de novo assembly methods when applied to current sequencing technologies. In a collaborative effort, teams were asked to assemble a simulated Illumina HiSeq data set of an unknown, simulated diploid genome. A total of 41 assemblies from 17 different groups were received. Novel haplotype aware assessments of coverage, contiguity, structure, base calling, and copy number were made. We establish that within this benchmark: (1) It is possible to assemble the genome to a high level of coverage and accuracy, and that (2) large differences exist between the assemblies, suggesting room for further improvements in current methods. The simulated benchmark, including the correct answer, the assemblies, and the code that was used to evaluate the assemblies is now public and freely available from http://www.assemblathon.org/.

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Section: Read Simulationmentioning

confidence: 99%

Assemblathon 1: A competitive assessment of de novo short read assembly methods

Earl¹,

Bradnam²,

John³

et al. 2011

Genome Res.

467

422

View full text Add to dashboard Cite

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“…Thus, the rRNA sequences obtained from the SOLiD-3 data were not analyzed further and will not be discussed further here. Simulations performed by the method of Richter et al (2008) showed that the upper limit for unique mapping errors, when allowing three to five mismatches for 50-bp sequences, was B0.007 and 3.12% for non-uniquely mapped sequences (Supplementary Material and Supplementary Table S3). These are estimated upper limits because the simulated data includes rRNA sequences and other repeat sequences (for example, insertion sequences, transposases and other repeated sequences) that were actually removed (or were not counted) by the transcript mapping procedure used in this study.…”

Section: Solid-3 Sequencing and Transcription Of Photosynthesis Genesmentioning

confidence: 99%

Metatranscriptomic analyses of chlorophototrophs of a hot-spring microbial mat

et al. 2011

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The phototrophic microbial mat community of Mushroom Spring, an alkaline siliceous hot spring in Yellowstone National Park, was studied by metatranscriptomic methods. RNA was extracted from mat specimens collected at four timepoints during light-to-dark and dark-to-light transitions in one diel cycle, and these RNA samples were analyzed by both pyrosequencing and SOLiD technologies. Pyrosequencing was used to assess the community composition, which showed that B84% of the rRNA was derived from members of four kingdoms Cyanobacteria, Chloroflexi, Chlorobi and Acidobacteria. Transcription of photosynthesis-related genes conclusively demonstrated the phototrophic nature of two newly discovered populations; these organisms, which were discovered through metagenomics, are currently uncultured and previously undescribed members of Chloroflexi and Chlorobi. Data sets produced by SOLiD sequencing of complementary DNA provided 4100-fold greater sequence coverage. The much greater sequencing depth allowed transcripts to be detected from B15 000 genes and could be used to demonstrate statistically significant differential transcription of thousands of genes. Temporal differences for in situ transcription patterns of photosynthesis-related genes suggested that the six types of chlorophototrophs in the mats may use different strategies for maximizing their solar-energy capture, usage and growth. On the basis of both temporal pattern and transcript abundance, intra-guild gene expression differences were also detected for two populations of the oxygenic photosynthesis guild. This study showed that, when community-relevant genomes and metagenomes are available, SOLiD sequencing technology can be used for metatranscriptomic analyses, and the results suggested that this method can potentially reveal new insights into the ecophysiology of this model microbial community.

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“…2. Short reads of ~100 nt were simulated from the 7 selected complete genomes (at 10X coverage) using MetaSim [18] (Version 0.9.1). For simplicity, no sequencing errors were introduced in the simulation.…”

Section: Simulated Short Readsmentioning

confidence: 99%

Artificial Functional Difference Between Microbial Communities Caused by Length Difference of Sequencing Reads

Zhang

Doak

2011

Biocomputing 2012

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Homology-based approaches are often used for the annotation of microbial communities, providing functional profiles that are used to characterize and compare the content and the functionality of microbial communities. Metagenomic reads are the starting data for these studies, however considerable differences are observed between the functional profiles-built from sequencing reads produced by different sequencing techniques-for even the same microbial community. Using simulation experiments, we show that such functional differences are likely to be caused by the actual difference in read lengths, and are not the results of a sampling bias of the sequencing techniques. Furthermore, the functional differences derived from different sequencing techniques cannot be fully explained by the read-count bias, i.e. 1) the higher fraction of unannotated shorter reads (i.e., "read length matters"), and 2) the different lengths of proteins in different functional categories. Instead, we show here that specific functional categories are under-annotated, because similarity-search-based functional annotation tools tend to miss more reads from functional categories that contain less conserved genes/proteins. In addition, the accuracy of functional annotation of short reads for different functions varies, further skewing the functional profiles. To address these issues, we present a simple yet efficient method to improve the frequency estimates of different functional categories in the functional profiles of metagenomes, based on the functional annotation of simulated reads from complete microbial genomes.

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MetaSim—A Sequencing Simulator for Genomics and Metagenomics

Cited by 401 publications

References 42 publications

Assemblathon 1: A competitive assessment of de novo short read assembly methods

Assemblathon 1: A competitive assessment of de novo short read assembly methods

Metatranscriptomic analyses of chlorophototrophs of a hot-spring microbial mat

Artificial Functional Difference Between Microbial Communities Caused by Length Difference of Sequencing Reads

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