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BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation.

ABSTRACT: Metagenomics-based studies of mixed microbial communities are impacting biotechnology, life sciences and medicine. Computational binning of metagenomic data is a powerful approach for the culture-independent recovery of population-resolved genomic sequences, i.e. from individual or closely related, constituent microorganisms. Existing binning solutions often require a priori characterized reference genomes and/or dedicated compute resources. Extending currently available reference-independent binning tools, we developed the BusyBee Web server for the automated deconvolution of metagenomic data into population-level genomic bins using assembled contigs (Illumina) or long reads (Pacific Biosciences, Oxford Nanopore Technologies). A reversible compression step as well as bootstrapped supervised binning enable quick turnaround times. The binning results are represented in interactive 2D scatterplots. Moreover, bin quality estimates, taxonomic annotations and annotations of antibiotic resistance genes are computed and visualized. Ground truth-based benchmarks of BusyBee Web demonstrate comparably high performance to state-of-the-art binning solutions for assembled contigs and markedly improved performance for long reads (median F1 scores: 70.02-95.21%). Furthermore, the applicability to real-world metagenomic datasets is shown. In conclusion, our reference-independent approach automatically bins assembled contigs or long reads, exhibits high sensitivity and precision, enables intuitive inspection of the results, and only requires FASTA-formatted input. The web-based application is freely accessible at: https://ccb-microbe.cs.uni-saarland.de/busybee.

SUBMITTER: Laczny CC

PROVIDER: S-EPMC5570254 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation.

Laczny Cedric C CC Kiefer Christina C Galata Valentina V Fehlmann Tobias T Backes Christina C Keller Andreas A

Nucleic acids research 20170701 W1

Metagenomics-based studies of mixed microbial communities are impacting biotechnology, life sciences and medicine. Computational binning of metagenomic data is a powerful approach for the culture-independent recovery of population-resolved genomic sequences, i.e. from individual or closely related, constituent microorganisms. Existing binning solutions often require a priori characterized reference genomes and/or dedicated compute resources. Extending currently available reference-independent bi ...[more]

PMID: 28472498

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Project description:Metagenomics sequencing provides deep insights into microbial communities. To investigate their taxonomic structure, binning assembled contigs into discrete clusters is critical. Many binning algorithms have been developed, but their performance is not always satisfactory, especially for complex microbial communities, calling for further development.According to previous studies, relative sequence compositions are similar across different regions of the same genome, but they differ between distinct genomes. Generally, current tools have used the normalized frequency of k-tuples directly, but this represents an absolute, not relative, sequence composition. Therefore, we attempted to model contigs using relative k-tuple composition, followed by measuring dissimilarity between contigs using [Formula: see text]. The [Formula: see text] was designed to measure the dissimilarity between two long sequences or Next-Generation Sequencing data with the Markov models of the background genomes. This method was effective in revealing group and gradient relationships between genomes, metagenomes and metatranscriptomes. With many binning tools available, we do not try to bin contigs from scratch. Instead, we developed [Formula: see text] to adjust contigs among bins based on the output of existing binning tools for a single metagenomic sample. The tool is taxonomy-free and depends only on k-tuples. To evaluate the performance of [Formula: see text], five widely used binning tools with different strategies of sequence composition or the hybrid of sequence composition and abundance were selected to bin six synthetic and real datasets, after which [Formula: see text] was applied to adjust the binning results. Our experiments showed that [Formula: see text] consistently achieves the best performance with tuple length k?=?6 under the independent identically distributed (i.i.d.) background model. Using the metrics of recall, precision and ARI (Adjusted Rand Index), [Formula: see text] improves the binning performance in 28 out of 30 testing experiments (6 datasets with 5 binning tools). The [Formula: see text] is available at https://github.com/kunWangkun/d2SBin .Experiments showed that [Formula: see text] accurately measures the dissimilarity between contigs of metagenomic reads and that relative sequence composition is more reasonable to bin the contigs. The [Formula: see text] can be applied to any existing contig-binning tools for single metagenomic samples to obtain better binning results.

Dataset Information

BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation.

Publications

BusyBee Web: metagenomic data analysis by bootstrapped supervised binning and annotation.

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