Improving metagenomic binning results with overlapped bins using assembly graphs

Mallawaarachchi, Vijini; Wickramarachchi, Anuradha; Lin, Yu

doi:10.1186/s13015-021-00185-6

Cited by 14 publications

(12 citation statements)

References 43 publications

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“…Reads from such regions are likely to be assigned to any one of the species because LRBinner currently does not support overlapped binning. Similar to GraphBin2 [ 33 ], LRBinner can be extended to detect such long reads and improve on the functionality of overlapped binning among distinct species. Secondly, LRBinner uses the valley-to-peak ratio to find candidate clusters which depends on the seed points.…”

Section: Discussionmentioning

confidence: 99%

Binning long reads in metagenomics datasets using composition and coverage information

Wickramarachchi

Lin

2022

Algorithms Mol Biol

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Background Advancements in metagenomics sequencing allow the study of microbial communities directly from their environments. Metagenomics binning is a key step in the species characterisation of microbial communities. Next-generation sequencing reads are usually assembled into contigs for metagenomics binning mainly due to the limited information within short reads. Third-generation sequencing provides much longer reads that have lengths similar to the contigs assembled from short reads. However, existing contig-binning tools cannot be directly applied on long reads due to the absence of coverage information and the presence of high error rates. The few existing long-read binning tools either use only composition or use composition and coverage information separately. This may ignore bins that correspond to low-abundance species or erroneously split bins that correspond to species with non-uniform coverages. Here we present a reference-free binning approach, LRBinner, that combines composition and coverage information of complete long-read datasets. LRBinner also uses a distance-histogram-based clustering algorithm to extract clusters with varying sizes. Results The experimental results on both simulated and real datasets show that LRBinner achieves the best binning accuracy in most cases while handling the complete datasets without any sampling. Moreover, we show that binning reads using LRBinner prior to assembly reduces computational resources required for assembly while attaining satisfactory assembly qualities. Conclusion LRBinner shows that deep-learning techniques can be used for effective feature aggregation to support the metagenomics binning of long reads. Furthermore, accurate binning of long reads supports improvements in metagenomics assembly, especially in complex datasets. Binning also helps to reduce the resources required for assembly. Source code for LRBinner is freely available at https://github.com/anuradhawick/LRBinner.

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Section: Discussionmentioning

confidence: 99%

Binning long reads in metagenomics datasets using composition and coverage information

Wickramarachchi

Lin

2022

Algorithms Mol Biol

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“…7 ), indicating that they make poor use of sequence composition and sequencing coverage or poorly combine additional information with the basis of sequence composition and sequencing coverage. Therefore, novel tools should first make full use of sequence composition and sequencing coverage as a base and then integrate other information such as co-alignment information, pair-end read linkage, SCGs, assembly graphs [ 65 ], and DNA methylation [ 66 ] to further improve binning, or assembly graphs [ 55 , 56 ] and a combination of assembly and paired-end graphs [ 57 ] to further refine binning of initial binners such as MaxBin2 [ 56 , 57 ].…”

Section: Discussionmentioning

confidence: 99%

“…Ensemble binning tools can be separated into two classes: (1) the stand-alone binners such as MaxBin [12] and CONCOCT [20]; (2) the binners refining results of other binners, such as GraphBin [55], GraphBin2 [56], METAMVGL [57] and MetaWRAP [58]. Here, we used this constructed dataset to assess the 8 aforementioned first-class stand-alone binners (for details, see Methods).…”

Section: Evaluating Metagenomic Binners With the Constructed Datasetmentioning

confidence: 99%

Constructing metagenome-assembled genomes for almost all components in a real bacterial consortium for binning benchmarking

Wang

Zeng

et al. 2022

BMC Genomics

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Background So far, a lot of binning approaches have been intensively developed for untangling metagenome-assembled genomes (MAGs) and evaluated by two main strategies. The strategy by comparison to known genomes prevails over the other strategy by using single-copy genes. However, there is still no dataset with all known genomes for a real (not simulated) bacterial consortium yet. Results Here, we continue investigating the real bacterial consortium F1RT enriched and sequenced by us previously, considering the high possibility to unearth all MAGs, due to its low complexity. The improved F1RT metagenome reassembled by metaSPAdes here utilizes about 98.62% of reads, and a series of analyses for the remaining reads suggests that the possibility of containing other low-abundance organisms in F1RT is greatly low, demonstrating that almost all MAGs are successfully assembled. Then, 4 isolates are obtained and individually sequenced. Based on the 4 isolate genomes and the entire metagenome, an elaborate pipeline is then in-house developed to construct all F1RT MAGs. A series of assessments extensively prove the high reliability of the herein reconstruction. Next, our findings further show that this dataset harbors several properties challenging for binning and thus is suitable to compare advanced binning tools available now or benchmark novel binners. Using this dataset, 8 advanced binning algorithms are assessed, giving useful insights for developing novel approaches. In addition, compared with our previous study, two novel MAGs termed FC8 and FC9 are discovered here, and 7 MAGs are solidly unearthed for species without any available genomes. Conclusion To our knowledge, it is the first time to construct a dataset with almost all known MAGs for a not simulated consortium. We hope that this dataset will be used as a routine toolkit to complement mock datasets for evaluating binning methods to further facilitate binning and metagenomic studies in the future.

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“…However, these approaches have certain limitations. Genomic sequencing of microbial isolates is not applicable to unculturable microorganisms, and genome-resolved metagenomics often suffers from binning errors of metagenomic reads ( Mallawaarachchi et al, 2021 ). Therefore, it is necessary to develop a highly precise, scalable, and universal approach to obtain the genomic information of individual microbial species in rice rhizosphere.…”

Section: Introductionmentioning

confidence: 99%

Massively parallel single-cell genomics of microbiomes in rice paddies

Aoki

Kogawa²,

Matsuda³

et al. 2022

Front. Microbiol.

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Plant growth-promoting microbes (PGPMs) have attracted increasing attention because they may be useful in increasing crop yield in a low-input and sustainable manner to ensure food security. Previous studies have attempted to understand the principles underlying the rhizosphere ecology and interactions between plants and PGPMs using ribosomal RNA sequencing, metagenomic sequencing, and genome-resolved metagenomics; however, these approaches do not provide comprehensive genomic information for individual species and do not facilitate detailed analyses of plant–microbe interactions. In the present study, we developed a pipeline to analyze the genomic diversity of the rice rhizosphere microbiome at single-cell resolution. We isolated microbial cells from paddy soil and determined their genomic sequences by using massively parallel whole-genome amplification in microfluidic-generated gel capsules. We successfully obtained 3,237 single-amplified genomes in a single experiment, and these genomic sequences provided insights into microbial functions in the paddy ecosystem. Our approach offers a promising platform for gaining novel insights into the roles of microbes in the rice rhizomicrobiome and to develop microbial technologies for improved and sustainable rice production.

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Improving metagenomic binning results with overlapped bins using assembly graphs

Cited by 14 publications

References 43 publications

Binning long reads in metagenomics datasets using composition and coverage information

Binning long reads in metagenomics datasets using composition and coverage information

Constructing metagenome-assembled genomes for almost all components in a real bacterial consortium for binning benchmarking

Massively parallel single-cell genomics of microbiomes in rice paddies

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