Functional and taxonomic classification of a greenhouse water drain metagenome.
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ABSTRACT:
SUBMITTER: Lopez-Leal G
PROVIDER: S-EPMC6173933 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
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Project description:Oligonucleotide signatures, especially tetranucleotide signatures, have been used as method for homology binning by exploiting an organism's inherent biases towards the use of specific oligonucleotide words. Tetranucleotide signatures have been especially useful in environmental metagenomics samples as many of these samples contain organisms from poorly classified phyla which cannot be easily identified using traditional homology methods, including NCBI BLAST. This study examines oligonucleotide signatures across 1,424 completed genomes from across the tree of life, substantially expanding upon previous work. A comprehensive analysis of mononucleotide through nonanucleotide word lengths suggests that longer word lengths substantially improve the classification of DNA fragments across a range of sizes of relevance to high throughput sequencing. We find that, at present, heptanucleotide signatures represent an optimal balance between prediction accuracy and computational time for resolving taxonomy using both genomic and metagenomic fragments. We directly compare the ability of tetranucleotide and heptanucleotide world lengths (tetranucleotide signatures are the current standard for oligonucleotide word usage analyses) for taxonomic binning of metagenome reads. We present evidence that heptanucleotide word lengths consistently provide more taxonomic resolving power, particularly in distinguishing between closely related organisms that are often present in metagenomic samples. This implies that longer oligonucleotide word lengths should replace tetranucleotide signatures for most analyses. Finally, we show that the application of longer word lengths to metagenomic datasets leads to more accurate taxonomic binning of DNA scaffolds and have the potential to substantially improve taxonomic assignment and assembly of metagenomic data.
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Project description:The explosive growth in taxonomic metagenome profiling methods over the past years has created a need for systematic comparisons using relevant performance criteria. The Open-community Profiling Assessment tooL (OPAL) implements commonly used performance metrics, including those of the first challenge of the initiative for the Critical Assessment of Metagenome Interpretation (CAMI), together with convenient visualizations. In addition, we perform in-depth performance comparisons with seven profilers on datasets of CAMI and the Human Microbiome Project. OPAL is freely available at https://github.com/CAMI-challenge/OPAL .
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Project description:Transaminases remain one of the most promising biocatalysts for use in chiral amine synthesis, however their industrial implementation has been hampered by their general instability towards, for example, high amine donor concentrations and organic solvent content. Herein we describe the identification, cloning and screening of 29 novel transaminases from a household drain metagenome. The most promising enzymes were fully characterised and the effects of pH, temperature, amine donor concentration and co-solvent determined. Several enzymes demonstrated good substrate tolerance as well as an unprecedented robustness for a wild-type transaminase. One enzyme in particular readily accepted IPA as an amine donor giving the same conversion with 2-50 equivalents, as well as being tolerant to a number of co-solvents, and operational in up to 50% DMSO - a characteristic as yet unobserved in a wild-type transaminase. This work highlights the value of using metagenomics for biocatalyst discovery from niche environments, and here has led to the identification of one of the most robust native transaminases described to date, with respect to IPA and DMSO tolerance.
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Project description:A tight association between microbial function and taxonomy is the basis of functional prediction based on taxonomy, but such associations have been controversial in water biomes largely due to the probable prevalence of functional redundancy. However, previous studies on this topic used a relatively coarse resolution of ecosystem functioning, potentially inflating the estimated functional redundancy. Thus, a comprehensive evaluation of the association between high-resolution functional traits and taxonomic diversity obtained from fresh and saline water metagenomic data is urgently needed. Here, we examined 938 functionally and taxonomically annotated water metagenomes obtained worldwide to scrutinize the connection between function and taxonomy, and to identify the key driver of water metagenomes function or taxonomic composition at a global scale. We found that pairwise similarity of function was significantly associated with taxonomy, though taxonomy had higher global dissimilarity than function. Classification into six water biomes resulted in greater variation in taxonomic compositions than functional profiles, as the key regulating factor was salinity. Fresh water microbes harbored distinct functional and taxonomic structures from microbes in saline water biomes, despite that taxonomy was more susceptible to gradient of geography and climate than function. In summary, our results find a significant relationship between taxonomic diversity and microbial functioning in global water metagenomes, although microbial taxonomic compositions vary to a larger extent than functional profiles in aquatic ecosystems, suggesting the possibility and necessity for functional prediction of microorganisms based on taxonomy in global aquatic ecosystems.
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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.
| S-EPMC4218995 | biostudies-literature
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.
| S-EPMC8008692 | biostudies-literature
Project description:BackgroundExcept for bacteria, the taxonomic diversity of the human fecal metagenome has not been widely studied, despite the potential importance of viruses and eukaryotes. Widely used bioinformatic tools contain limited numbers of non-bacterial species in their databases compared to available genomic sequences and their methodologies do not favour classification of rare sequences which may represent only a small fraction of their parent genome. In seeking to optimise identification of non-bacterial species, we evaluated five widely-used metagenome classifier programs (BURST, Kraken2, Centrifuge, MetaPhlAn2 and CCMetagen) for their ability to correctly assign and count simulations of bacterial, viral and eukaryotic DNA sequence reads, including the effect of taxonomic order of analysis of bacteria, viruses and eukaryotes and the effect of sequencing depth.ResultsWe found that the precision of metagenome classifiers varied significantly between programs and between taxonomic groups. When classifying viruses and eukaryotes, ordering the analysis such that bacteria were classified first significantly improved classification precision. Increasing sequencing depth decreased classification precision and did not improve recall of rare species.ConclusionsChoice of metagenome classifier program can have a marked effect on results with respect to precision of species assignment in different taxonomic groups. The order of taxonomic classification can markedly improve precision. Increasing sequencing depth can decrease classification precision and yields diminishing returns in probability of species detection.
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Project description:BackgroundMany metagenome analysis tools are presently available to classify sequences and profile environmental samples. In particular, taxonomic profiling and binning methods are commonly used for such tasks. Tools available among these two categories make use of several techniques, e.g., read mapping, k-mer alignment, and composition analysis. Variations on the construction of the corresponding reference sequence databases are also common. In addition, different tools provide good results in different datasets and configurations. All this variation creates a complicated scenario to researchers to decide which methods to use. Installation, configuration and execution can also be difficult especially when dealing with multiple datasets and tools.ResultsWe propose MetaMeta: a pipeline to execute and integrate results from metagenome analysis tools. MetaMeta provides an easy workflow to run multiple tools with multiple samples, producing a single enhanced output profile for each sample. MetaMeta includes a database generation, pre-processing, execution, and integration steps, allowing easy execution and parallelization. The integration relies on the co-occurrence of organisms from different methods as the main feature to improve community profiling while accounting for differences in their databases.ConclusionsIn a controlled case with simulated and real data, we show that the integrated profiles of MetaMeta overcome the best single profile. Using the same input data, it provides more sensitive and reliable results with the presence of each organism being supported by several methods. MetaMeta uses Snakemake and has six pre-configured tools, all available at BioConda channel for easy installation (conda install -c bioconda metameta). The MetaMeta pipeline is open-source and can be downloaded at: https://gitlab.com/rki_bioinformatics .
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Project description:Metagenomics enables the study of complex microbial communities from myriad sources, including the remains of oral and gut microbiota preserved in archaeological dental calculus and paleofeces, respectively. While accurate taxonomic assignment is essential to this process, DNA damage characteristic of ancient samples (e.g., reduction in fragment size and cytosine deamination) may reduce the accuracy of read taxonomic assignment. Using a set of in silico-generated metagenomic data sets, we investigated how the addition of ancient DNA (aDNA) damage patterns influences microbial taxonomic assignment by five widely used profilers: QIIME/UCLUST, MetaPhlAn2, MIDAS, CLARK-S, and MALT. In silico-generated data sets were designed to mimic dental plaque, consisting of 40, 100, and 200 microbial species/strains, both with and without simulated aDNA damage patterns. Following taxonomic assignment, the profiles were evaluated for species presence/absence, relative abundance, alpha diversity, beta diversity, and specific taxonomic assignment biases. Unifrac metrics indicated that both MIDAS and MetaPhlAn2 reconstructed the most accurate community structure. QIIME/UCLUST, CLARK-S, and MALT had the highest number of inaccurate taxonomic assignments; false-positive rates were highest by CLARK-S and QIIME/UCLUST. Filtering out species present at <0.1% abundance greatly increased the accuracy of CLARK-S and MALT. All programs except CLARK-S failed to detect some species from the input file that were in their databases. The addition of ancient DNA damage resulted in minimal differences in species detection and relative abundance between simulated ancient and modern data sets for most programs. Overall, taxonomic profiling biases are program specific rather than damage dependent, and the choice of taxonomic classification program should be tailored to specific research questions. IMPORTANCE Ancient biomolecules from oral and gut microbiome samples have been shown to be preserved in the archaeological record. Studying ancient microbiome communities using metagenomic techniques offers a unique opportunity to reconstruct the evolutionary trajectories of microbial communities through time. DNA accumulates specific damage over time, which could potentially affect taxonomic classification and our ability to accurately reconstruct community assemblages. It is therefore necessary to assess whether ancient DNA (aDNA) damage patterns affect metagenomic taxonomic profiling. Here, we assessed biases in community structure, diversity, species detection, and relative abundance estimates by five popular metagenomic taxonomic classification programs using in silico-generated data sets with and without aDNA damage. Damage patterns had minimal impact on the taxonomic profiles produced by each program, while false-positive rates and biases were intrinsic to each program. Therefore, the most appropriate classification program is one that minimizes the biases related to the questions being addressed.
| S-EPMC6050634 | biostudies-literature