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Functional and taxonomic classification of a greenhouse water drain metagenome.

ABSTRACT: Microbiome sequencing has become the standard procedure in the study of new ecological and human-constructed niches. To our knowledge, this is the first report of a metagenome from the water of a greenhouse drain. We found that the greenhouse is not a diverse niche, mainly dominated by Rhizobiales and Rodobacterales. The analysis of the functions encoded in the metagenome showed enrichment of characteristic features of soil and root-associated bacteria such as ABC-transporters and hydrolase enzymes. Additionally, we found antibiotic resistances genes principally for spectinomycin, tetracycline, and aminoglycosides. This study aimed to identify the bacteria and functional gene composition of a greenhouse water drain sample and also provide a genomic resource to search novel proteins from a previously unexplored niche. All the metagenome proteins and their annotations are available to the scientific community via http://microbiomics.ibt.unam.mx/tools/metagreenhouse/.

SUBMITTER: Lopez-Leal G

PROVIDER: S-EPMC6173933 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Functional and taxonomic classification of a greenhouse water drain metagenome.

López-Leal Gamaliel G Cornejo-Granados Fernanda F Hurtado-Ramírez Juan Manuel JM Mendoza-Vargas Alfredo A Ochoa-Leyva Adrian A

Standards in genomic sciences 20181005

Microbiome sequencing has become the standard procedure in the study of new ecological and human-constructed niches. To our knowledge, this is the first report of a metagenome from the water of a greenhouse drain. We found that the greenhouse is not a diverse niche, mainly dominated by <i>Rhizobiales</i> and Rodobacterales. The analysis of the functions encoded in the metagenome showed enrichment of characteristic features of soil and root-associated bacteria such as ABC-transporters and hydrola ...[more]

PMID: 30323887

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Project description:BACKGROUND: Understanding the taxonomic composition of a sample, whether from patient, food or environment, is important to several types of studies including pathogen diagnostics, epidemiological studies, biodiversity analysis and food quality regulation. With the decreasing costs of sequencing, metagenomic data is quickly becoming the preferred typed of data for such analysis. RESULTS: Rapidly defining the taxonomic composition (both taxonomic profile and relative frequency) in a metagenomic sequence dataset is challenging because the task of mapping millions of sequence reads from a metagenomic study to a non-redundant nucleotide database such as the NCBI non-redundant nucleotide database (nt) is a computationally intensive task. We have developed a robust subsampling-based algorithm implemented in a tool called CensuScope meant to take a 'sneak peak' into the population distribution and estimate taxonomic composition as if a census was taken of the metagenomic landscape. CensuScope is a rapid and accurate metagenome taxonomic profiling tool that randomly extracts a small number of reads (based on user input) and maps them to NCBI's nt database. This process is repeated multiple times to ascertain the taxonomic composition that is found in majority of the iterations, thereby providing a robust estimate of the population and measures of the accuracy for the results. CONCLUSION: CensuScope can be run on a laptop or on a high-performance computer. Based on our analysis we are able to provide some recommendations in terms of the number of sequence reads to analyze and the number of iterations to use. For example, to quantify taxonomic groups present in the sample at a level of 1% or higher a subsampling size of 250 random reads with 50 iterations yields a statistical power of >99%. Windows and UNIX versions of CensuScope are available for download at https://hive.biochemistry.gwu.edu/dna.cgi?cmd=censuscope. CensuScope is also available through the High-performance Integrated Virtual Environment (HIVE) and can be used in conjunction with other HIVE analysis and visualization tools.

Project description:BackgroundStudies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction-associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or not explore the full potential of RMS data.ResultsWe suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock community datasets show the potential to clearly separate strains even when the 16S is 100% identical, and genome-wide differences is < 0.02, indicating RMS has a very high resolution. From a simulation study, we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real dataset of infant guts, we show that RMS is capable of detecting a strain diversity gradient for Escherichia coli across time.ConclusionWe find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain level. Like shotgun metagenomics, it requires a good database of reference genomes and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS . Video abstract.

Dataset Information

Functional and taxonomic classification of a greenhouse water drain metagenome.

Publications

Functional and taxonomic classification of a greenhouse water drain metagenome.

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