A review of methods and databases for metagenomic classification and assembly

Breitwieser, Florian P.; Lu, Jennifer; Salzberg, Steven L.

doi:10.1093/bib/bbx120

Cited by 438 publications

(386 citation statements)

References 163 publications

(171 reference statements)

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“…Ranges of bioinformatics tools facilitate data analysis, e.g., Kraken [75], Kaiju [76], VirusFinder [77]. Different workflows have recently been benchmarked and comprehensively overviewed elsewhere [78][79][80].…”

Section: Problems Of Metagenomic Approachmentioning

confidence: 99%

See 1 more Smart Citation

Current Trends in Diagnostics of Viral Infections of Unknown Etiology

Kiselev

Matsvay

Абрамов

et al. 2020

Viruses

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Viruses are evolving at an alarming rate, spreading and inconspicuously adapting to cutting-edge therapies. Therefore, the search for rapid, informative and reliable diagnostic methods is becoming urgent as ever. Conventional clinical tests (PCR, serology, etc.) are being continually optimized, yet provide very limited data. Could high throughput sequencing (HTS) become the future gold standard in molecular diagnostics of viral infections? Compared to conventional clinical tests, HTS is universal and more precise at profiling pathogens. Nevertheless, it has not yet been widely accepted as a diagnostic tool, owing primarily to its high cost and the complexity of sample preparation and data analysis. Those obstacles must be tackled to integrate HTS into daily clinical practice. For this, three objectives are to be achieved: (1) designing and assessing universal protocols for library preparation, (2) assembling purpose-specific pipelines, and (3) building computational infrastructure to suit the needs and financial abilities of modern healthcare centers. Data harvested with HTS could not only augment diagnostics and help to choose the correct therapy, but also facilitate research in epidemiology, genetics and virology. This information, in turn, could significantly aid clinicians in battling viral infections.

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Section: Problems Of Metagenomic Approachmentioning

confidence: 99%

“…The former option is very demanding in terms of computational power, since millions of reads undergo a complex analysis [84]. The latter is subject to faults of its own: partial or low-quality reference sequences, false mapping and high genetic diversity resulting in numerous polymorphisms [79].…”

Section: Problems Of Metagenomic Approachmentioning

confidence: 99%

Current Trends in Diagnostics of Viral Infections of Unknown Etiology

Kiselev

Matsvay

Абрамов

et al. 2020

Viruses

View full text Add to dashboard Cite

show abstract

“…Sequences are then clustered into bins called ‘Operational Taxonomic Units’ based on similarity and are typically annotated based upon a representative sequence . 16S rRNA offers a cheap and fast method to analyse the microbial composition (‘who is there?’), but it incorporates amplification and systematic biases, and is not ideal for comprehensive phylogenetic assignation, and no information about the functional properties of the bacterial communities can be drawn . Alternatively, MGS is accomplished by untargeted sequencing of the whole genome of all microorganisms present in a sample .…”

Section: Microbial Composition and Its Functional Potentialmentioning

confidence: 99%

“…16S rRNA offers a cheap and fast method to analyse the microbial composition (‘who is there?’), but it incorporates amplification and systematic biases, and is not ideal for comprehensive phylogenetic assignation, and no information about the functional properties of the bacterial communities can be drawn . Alternatively, MGS is accomplished by untargeted sequencing of the whole genome of all microorganisms present in a sample . Although more expensive than 16S rRNA, MGS avoids amplification biases and extends the information provided by 16S rRNA, allowing the identification of viruses, fungi and protozoa and the simultaneous characterization of both the microbial composition and genes.…”

Section: Microbial Composition and Its Functional Potentialmentioning

confidence: 99%

Exploration of the microbiota and metabolites within body fluids could pinpoint novel disease mechanisms

Mayneris‐Perxachs

Fernández‐Real

2019

The FEBS Journal

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Thanks to the emergence and recent advances in high‐throughput sequencing technologies, it is becoming more evident every day that changes in the microbiome composition are linked to a myriad of health conditions. Despite this, the mechanisms of host–microbiota signalling remain largely unknown. The microbiome has an extensive metabolic activity that leads to the generation of a large number of compounds that are likely to influence host health. Therefore, the microbiome–host cross‐talk is in part mediated by microbial‐derived metabolites. Unlike metagenomics, which only provides information about microbial genes and thus the microbiome functional potential, metabolic phenotyping is well suited to capture their actual metabolic activity. Here, we provide an overview of these approaches and propose an integration of metagenomics, as a microbiome compositional readout, with faecal and plasma/urine metabolomics, as a functional readout, to unravel novel mechanisms linking the microbiome to host health and disease.

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“…While being faster in most cases, alignment-free methods are limited to the detection of sequences, whereas alignment-based methods potentially allow for a more detailed characterization of genomes. Existing approaches based on unbiased full genome sequencing of metagenomic samples are facing various obstacles, especially concerning the ranking of the results according to their clinical relevance and the long overall turnaround time [41][42][43][44][45][46][47][48]. A central issue in NGS-based pathogen detection is that the clinically relevant data is very hard to identify.…”

Section: Introductionmentioning

confidence: 99%

PathoLive – Real-time pathogen identification from metagenomic Illumina datasets

Tausch

Loka

et al. 2018

Preprint

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Over the past years, NGS has been applied in time critical applications such as pathogen diagnostics with promising results. Yet, long turnaround times have to be accepted to generate sufficient data, as the analysis can only be performed sequentially after the sequencing has finished. Additionally, the interpretation of results can be further complicated by various types of contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data. We designed and implemented PathoLive, a real-time diagnostics pipeline which allows the detection of pathogens from clinical samples up to several days before the sequencing procedure is even finished and currently available tools may start to run. We adapted the core algorithm of HiLive, a real-time read mapper, and enhanced its accuracy for our use case. Furthermore, common contaminations, low-entropy areas, and sequences of widespread, nonpathogenic organisms are automatically marked beforehand using NGS datasets from healthy humans as a baseline. The results are visualized in an interactive taxonomic tree that provides an intuitive overview and detailed measures regarding the relevance of each identified potential pathogen. We applied the pipeline on a human plasma sample that was spiked in vitro with vaccinia virus, yellow fever virus, mumps virus, Rift Valley fever virus, adenovirus, and mammalian orthoreovirus. The sample was then sequenced on an Illumina HiSeq. All spiked agents were detected after the completion of only 12% of the sequencing procedure and were ranked more accurately throughout the run than by any of the tested tools on the complete data. We also found a large number of other sequences and these were correctly marked as clinically irrelevant in the resulting visualization. This tagging allows the user to obtain the correct assessment of the situation at first glance.

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A review of methods and databases for metagenomic classification and assembly

Cited by 438 publications

References 163 publications

Current Trends in Diagnostics of Viral Infections of Unknown Etiology

Current Trends in Diagnostics of Viral Infections of Unknown Etiology

Exploration of the microbiota and metabolites within body fluids could pinpoint novel disease mechanisms

PathoLive – Real-time pathogen identification from metagenomic Illumina datasets

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