A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data

Yang, Chao; Chowdhury, Debajyoti; Zhang, Zhenmiao; Cheung, William K.; Lu, Aiping; Bian, Zhaoxiang; Zhang, Lu

doi:10.1016/j.csbj.2021.11.028

Cited by 118 publications

(90 citation statements)

References 155 publications

(241 reference statements)

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“…Markov state model (MSM) is a mathematical framework to describe the dynamics of time-series data [76] , [77] , [78] . It has been used as an invaluable tool in understanding the dynamics during MD simulation [79] , [80] .…”

Section: Methodsmentioning

confidence: 99%

Elucidation of the conformational dynamics and assembly of Argonaute–RNA complexes by distinct yet coordinated actions of the supplementary microRNA

Zhuang

Fan

et al. 2022

Computational and Structural Biotechnology Journal

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Section: Methodsmentioning

confidence: 99%

Elucidation of the conformational dynamics and assembly of Argonaute–RNA complexes by distinct yet coordinated actions of the supplementary microRNA

Zhuang

Fan

et al. 2022

Computational and Structural Biotechnology Journal

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“…Similarly, another recent study adopted a hybrid approach of short- and long-(Nanopore) read sequencing and produced large numbers (over 1000) of high-quality MAGs from complex microbial communities (of Danish wastewater treatment plants) [ 115 ]. A number of complex bioinformatics pipelines are currently being applied and developed to analyse such challenging data (for comprehensive reviews see [ 116 , 117 ]). Cell-free plasma Next-Generation Sequencing (cfNGS), a form of plasma metagenomics, is already being adopted with success in certain clinical settings such as paediatric complicated pneumonia [ 118 ].…”

Section: The Shape Of Things To Comementioning

confidence: 99%

The Notable Achievements and the Prospects of Bacterial Pathogen Genomics

2022

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Throughout the entirety of human history, bacterial pathogens have played an important role and even shaped the fate of civilizations. The application of genomics within the last 27 years has radically changed the way we understand the biology and evolution of these pathogens. In this review, we discuss how the short- (Illumina) and long-read (PacBio, Oxford Nanopore) sequencing technologies have shaped the discipline of bacterial pathogen genomics, in terms of fundamental research (i.e., evolution of pathogenicity), forensics, food safety, and routine clinical microbiology. We have mined and discuss some of the most prominent data/bioinformatics resources such as NCBI pathogens, PATRIC, and Pathogenwatch. Based on this mining, we present some of the most popular sequencing technologies, hybrid approaches, assemblers, and annotation pipelines. A small number of bacterial pathogens are of very high importance, and we also present the wealth of the genomic data for these species (i.e., which ones they are, the number of antimicrobial resistance genes per genome, the number of virulence factors). Finally, we discuss how this discipline will probably be transformed in the near future, especially by transitioning into metagenome-assembled genomes (MAGs), thanks to long-read sequencing.

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“…Finally, most binning methods are developed only on short-read assemblies [5], and only very few binners have been developed with a focus on long-read assemblies [18, 23]. While long-read sequencing technologies have gained traction, there is still a lack of benchmarks and studies on long-read sequencing for metagenomics [24, 25, 26, 27].…”

Section: Introductionmentioning

confidence: 99%

“…To retrieve metagenome assembled genomes (MAGs) from complex metagenomes, several binners have been proposed based on composition and abundance features [5]. Composition refers to the k-mer frequencies of a particular contig and can be used to distinguish between different species [6].…”

Section: Introductionmentioning

confidence: 99%

Metagenomic binning with assembly graph embeddings

Lamurias

Sereika

Albertsen

et al. 2022

Preprint

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Despite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning. We propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared to state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning.

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A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data

Abstract: Graphical abstract

Cited by 118 publications

References 155 publications

Elucidation of the conformational dynamics and assembly of Argonaute–RNA complexes by distinct yet coordinated actions of the supplementary microRNA

Elucidation of the conformational dynamics and assembly of Argonaute–RNA complexes by distinct yet coordinated actions of the supplementary microRNA

The Notable Achievements and the Prospects of Bacterial Pathogen Genomics

Metagenomic binning with assembly graph embeddings

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