BinSPreader: Refine binning results for fuller MAG reconstruction

Tolstoganov, Ivan; Kamenev, Yuri; Kruglikov, Roman; Ochkalova, Sofia; Korobeynikov, Anton

doi:10.1016/j.isci.2022.104770

Cited by 5 publications

(8 citation statements)

References 42 publications

(92 reference statements)

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“…MetaBAT-LR adopts a different algorithm from two other popular software tools based on Hi-C, bin3C ( DeMaere & Darling, 2019 ) and ProxiMeta ( Press et al, 2017 ), as well as the newly published BinSPreader ( Tolstoganov et al, 2022 ) and hicSPAdes ( Ivanova et al, 2022 ). We compared the binning performance of these five software tools on the Zymo Mock Synthetic dataset, the CAT Fecal microbiome dataset, and the Human Fecal microbiome data sets.…”

Section: Resultsmentioning

confidence: 99%

“…BinSPreader v3.16.0.dev ( Tolstoganov et al, 2022 ), bin3C v0.1.1 ( DeMaere & Darling, 2019 ), hicSPAdes ( Ivanova et al, 2022 ), and ProxiMeta Web version ( Press et al, 2017 ) were run with default settings. The final metagenome bins generated by these software tools were compared with those of metaBAT-LR.…”

Section: Methodsmentioning

confidence: 99%

“…Phase Genomics ProxiMeta is proprietary software that applies an algorithm based on Markov Chain Monte Carlo on Hi-C linkage information to form metagenome bins ( Press et al, 2017 ), and its implementation has not been described in full detail. The recently published BinSPreader is able to use long-range information such as long reads, or short reads derived from Hi-C experiments, combined with the assembly graph to refine existing binning results ( Tolstoganov et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Integrating chromatin conformation information in a self-supervised learning model improves metagenome binning

Ho,

Chovatia,

Egan

et al. 2023

PeerJ

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Metagenome binning is a key step, downstream of metagenome assembly, to group scaffolds by their genome of origin. Although accurate binning has been achieved on datasets containing multiple samples from the same community, the completeness of binning is often low in datasets with a small number of samples due to a lack of robust species co-abundance information. In this study, we exploited the chromatin conformation information obtained from Hi-C sequencing and developed a new reference-independent algorithm, Metagenome Binning with Abundance and Tetra-nucleotide frequencies—Long Range (metaBAT-LR), to improve the binning completeness of these datasets. This self-supervised algorithm builds a model from a set of high-quality genome bins to predict scaffold pairs that are likely to be derived from the same genome. Then, it applies these predictions to merge incomplete genome bins, as well as recruit unbinned scaffolds. We validated metaBAT-LR’s ability to bin-merge and recruit scaffolds on both synthetic and real-world metagenome datasets of varying complexity. Benchmarking against similar software tools suggests that metaBAT-LR uncovers unique bins that were missed by all other methods. MetaBAT-LR is open-source and is available at https://bitbucket.org/project-metabat/metabat-lr.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating chromatin conformation information in a self-supervised learning model improves metagenome binning

Ho,

Chovatia,

Egan

et al. 2023

PeerJ

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“…All obtained reads from three samples were co-assembled with setting of SPAdes as described above. Then, we mapped individual samples on the scaffold, used vRhyme to get bins, extended them from the assembly graph with binSPReader, pre-release ( 37 ). The extended nudivirus bin was checked with Virsorter2 v2.2.4 ( 38 ) and DRAM v1.4.6 ( 39 ) to ensure that all contigs we have gained had a majority of Nudiviridae alignments.…”

Section: Methodsmentioning

confidence: 99%

Discovery and characterization of novel DNA viruses in Apis mellifera : expanding the honey bee virome through metagenomic analysis

Kadlečková,

Saláková,

Erban

et al. 2024

mSystems

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To date, many viruses have been discovered to infect honey bees. In this study, we used high-throughput sequencing to expand the known virome of the honey bee, Apis mellifera , by identifying several novel DNA viruses. While the majority of previously identified bee viruses are RNA, our study reveals nine new genomes from the Parvoviridae family, tentatively named Bee densoviruses 1 to 9. In addition, we characterized a large DNA virus, Apis mellifera filamentous-like virus (AmFLV), which shares limited protein identities with the known Apis mellifera filamentous virus. The complete sequence of AmFLV, obtained by a combination of laboratory techniques and bioinformatics, spans 152,678 bp. Linear dsDNA genome encodes for 112 proteins, of which 49 are annotated. Another large virus we discovered is Apis mellifera nudivirus, which belongs to a group of Alphanudivirus . The virus has a length of 129,467 bp and a circular dsDNA genome, and has 106 protein encoding genes. The virus contains most of the core genes of the family Nudiviridae . This research demonstrates the effectiveness of viral binning in identifying viruses in honey bee virology, showcasing its initial application in this field. IMPORTANCE Honey bees contribute significantly to food security by providing pollination services. Understanding the virome of honey bees is crucial for the health and conservation of bee populations and also for the stability of the ecosystems and economies for which they are indispensable. This study unveils previously unknown DNA viruses in the honey bee virome, expanding our knowledge of potential threats to bee health. The use of the viral binning approach we employed in this study offers a promising method to uncovering and understanding the vast viral diversity in these essential pollinators.

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“…For complete details on the use and execution of this protocol, please refer to Tolstoganov et al. 1 …”

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confidence: 99%

Protocol for refining metagenomic binning with BinSPreader

et al. 2023

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BinSPreader: Refine binning results for fuller MAG reconstruction

Cited by 5 publications

References 42 publications

Integrating chromatin conformation information in a self-supervised learning model improves metagenome binning

Integrating chromatin conformation information in a self-supervised learning model improves metagenome binning

Discovery and characterization of novel DNA viruses in Apis mellifera : expanding the honey bee virome through metagenomic analysis

Protocol for refining metagenomic binning with BinSPreader

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