Evidence map›Paper›PMID 28472498›Full record

ArticleNucleic acids research2017

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

Cedric C Laczny, Christina Kiefer, Valentina Galata, Tobias Fehlmann, Christina Backes, Andreas Keller

Abstract read
In one paragraph

Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed.

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  11. Genomic Assessment of the Contribution of theInternational journal of molecular sciences · 2023
    Article
  12. Review
  13. The toxins of vertically transmittedFrontiers in microbiology · 2023
    Article
  14. Recovery and Analysis of Long-Read Metagenome-Assembled Genomes.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  15. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Cedric C LacznyChair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.
Christina KieferChair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.
Valentina GalataChair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.
Tobias FehlmannChair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.
Christina BackesChair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.
Andreas KellerChair for Clinical Bioinformatics, Saarland University, Campus Building E2.1, 66123 Saarbrücken, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

MetagenomicsSoftwareDrug Resistance, MicrobialInternetMachine LearningMolecular Sequence AnnotationWorkflow

Identifiers

PMID28472498
PMCPMC5570254

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.