ArticlePloS one2010
Occupancy modeling, maximum contig size probabilities and designing metagenomics experiments.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Terabase-scale metagenome coassembly with MetaHipMer.Scientific reports · 2020Article
- Estimating the Optimum Coverage and Quality of Amplicon Sequencing With Taylor's Power Law Extensions.Frontiers in bioengineering and biotechnology · 2020Article
- MetLab: An In Silico Experimental Design, Simulation and Analysis Tool for Viral Metagenomics Studies.PloS one · 2016Article
- Estimating coverage in metagenomic data sets and why it matters.The ISME journal · 2014Article
- Coverage theories for metagenomic DNA sequencing based on a generalization of Stevens' theorem.Journal of mathematical biology · 2013Article
- Evaluation of high-throughput sequencing for identifying known and unknown viruses in biological samples.Journal of clinical microbiology · 2011Article
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1 author.
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Abstract
Mathematical aspects of coverage and gaps in genome assembly have received substantial attention by bioinformaticians. Typical problems under consideration suppose that reads can be experimentally obtained from a single genome and that the number of reads will be set to cover a large percentage of that genome at a desired depth. In metagenomics experiments genomes from multiple species are simultaneously analyzed and obtaining large numbers of reads per genome is unlikely. We propose the probability of obtaining at least one contig of a desired minimum size from each novel genome in the pool without restriction based on depth of coverage as a metric for metagenomic experimental design. We derive an approximation to the distribution of maximum contig size for single genome assemblies using relatively few reads. This approximation is verified in simulation studies and applied to a number of different metagenomic experimental design problems, ranging in difficulty from detecting a single novel genome in a pool of known species to detecting each of a random number of novel genomes collectively sized and with abundances corresponding to given distributions in a single pool.
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