MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota

Olekhnovich, Evgenii I.; Vasilyev, Artem T; Ulyantsev, Vladimir; Kostryukova, Elena S.; Tyakht, Alexander V.

doi:10.1093/bioinformatics/btx681

Cited by 32 publications

(35 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also positioned MetaCherchant software [18] as a valuable post-processing tool for our bioinformatics pipeline, allowing to obtain a reconstruction of the genomic context around detected ARG. While our original intention was to benchmark MetaCherchant with our pipelines, it could not directly compete in terms of ARG detection precision given the high similarity between ARG sequences in our ARG RDB.…”

Section: Discussion/conclusionmentioning

confidence: 99%

“…In addition to offering pathogen and ARG detection, our bioinformatics pipeline TBwDM can be complemented for understanding genomic context in the neighborhood of detected ARG. To illustrate this, we ran MetaCherchant [18], a graph-based algorithm for extracting ARG and their genomic context (sequence environment) from metagenomic data, on the simulated polymicrobial infections. Table 4 shows MetaCherchant performance at the sample level (i.e.…”

Section: Additional Insight On Detected Argmentioning

confidence: 99%

“…Moreover, assuming that an ARG is present in the metagenome, it is important from a clinical perspective to distinguish between its presence in commensal bacteria and the more clinically relevant situation where the ARG is harbored by one or several species of pathogenic bacteria. This objective is also addressed by MetaCherchant [18] which aims at extracting the genomic environment of ARG detected in a metagenome and thus infer the link with the host bacteria and possible transmission of ARG between bacteria.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples

Tournoud

Ruppé

Perrin

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Shortening the time-to-result for pathogen detection and identification and antibiotic susceptibility testing for patients with Hospital-Acquired and Ventilator-Associated pneumonia (HAP-VAP) is of great interest. For this purpose, clinical metagenomics is a promising non-hypothesis driven alternative to traditional culture-based solutions: when mature, it would allow direct sequencing all microbial genomes present in a BronchoAlveolar Lavage (BAL) sample with the purpose of simultaneously identifying pathogens and Antibiotic Resistance Genes (ARG). In this study, we describe a new bioinformatics method to detect pathogens and their ARG with good accuracy, both in mono-and polymicrobial samples.Methods: The standard approach (hereafter called TBo), that consists in taxonomic binning of metagenomic reads followed by an assembly step, suffers from lack of sensitivity for ARG detection. Thus, we propose a new bioinformatics approach (called TBwDM) with both models and databases optimized for HAP-VAP, that performs reads mapping against ARG reference database in parallel to taxonomic binning, and joint reads assembly.Results: In in-silico simulated monomicrobial samples, the recall for ARG detection increased from 51% with TBo to 97.3% with TBwDM ; in simulated polymicrobial infections, it increased from 41.8% to 82%. In real sequenced BAL samples (mono and polymicrobial), detected pathogens were also confirmed by traditional culture approaches. Moreover, both recall and precision for ARG detection were higher with TBwDM than with TBo (35 points difference for recall, and 7 points difference for precision).Conclusions: We present a new bioinformatics pipeline to identify pathogens and ARG in BAL samples from patients with HAP-VAP, with higher sensitivity for ARG recovery than standard approaches and the ability to link ARG to their host pathogens.

show abstract

Section: Discussion/conclusionmentioning

confidence: 99%

Section: Additional Insight On Detected Argmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples

Tournoud

Ruppé

Perrin

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Efficient graph algorithms provide novel tools for investigating graph neighborhoods. Recent work has shown that incorporating the structure of the assembly graph into the analysis of metagenome data can provide a more complete picture of gene content (21,22). While this has provided evidence that it is useful to analyze sequence that has small graph distance from a query (is in a "neighborhood"), this approach has not been widely adopted.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, most assembly graphs undergo substantial heuristic error pruning and may not contain relevant content (11,12). Graph queries have shown promise for recovering sequence from regions that do not assemble well but are graph-proximal to the query (21,22). However, many graph query algorithms are NP-hard and hence computationally intractable in the general case; compounding the computational challenge, metagenome assembly graphs are frequently large, with millions of nodes, and require 10s to 100s of gigabytes of RAM for storage.…”

mentioning

confidence: 99%

Exploring neighborhoods in large metagenome assembly graphs reveals hidden sequence diversity

Brown¹,

Moritz

O’Brien

et al. 2018

Preprint

View full text Add to dashboard Cite

Genomes computationally inferred from large metagenomic data sets are often incomplete and may be missing functionally important content and strain variation. We introduce an information retrieval system for large metagenomic data sets that exploits the sparsity of DNA assembly graphs to efficiently extract subgraphs surrounding an inferred genome. We apply this system to recover missing content from genome bins and show that substantial genomic sequence variation is present in a real metagenome. Our software implementation is available at https://github.com/spacegraphcats/ spacegraphcats under the 3-Clause BSD License. metagenomics | sequence assembly | strain variation | bounded expansion | dominating set * an invertible function that defines both an index and the corresponding inverted index

show abstract

Exploring Foodborne Pathogen Ecology and Antimicrobial Resistance in the Light of Shotgun Metagenomics

Bridier

2018

Methods in Molecular Biology

View full text Add to dashboard Cite

MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 32 publications

References 46 publications

Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples

Clinical metagenomics bioinformatics pipeline for the identification of hospital-acquired pneumonia pathogens antibiotic resistance genes from bronchoalveolar lavage samples

Exploring neighborhoods in large metagenome assembly graphs reveals hidden sequence diversity

Exploring Foodborne Pathogen Ecology and Antimicrobial Resistance in the Light of Shotgun Metagenomics

Contact Info

Product

Resources

About