Biology subjects

Hasan, N. A.

Publications and source records attributed to Hasan, N. A..

2 recordsLinked to original sources

bioRxiv2017

Metagenomic sequencing to replace semi-quantitative urine culture for detection of urinary tract infections: a proof of concept

ABSTRACTSemi-quantitative bacterial culture is the standard method to diagnose urinary tract infections (UTI), but bacterial growth rate limits diagnostic speed and it is unreliable when patients have been pre-treated with antibiotics. Metagenomics could increase diagnostic speed and accuracy by sequencing the microbiome and resistome directly from urine samples, bypassing culture. However, a semi-quantitative approach - as needed for diagnosing UTIs - has not been established.\n\nMetagenomics was deployed to identify and semi-quantify bacterial presence indicative of UTI, predict antimicrobial susceptibility (AMR), and results were compared to semi-quantitative culture. Whole genome sequencing of the corresponding uropathogens was done for comparison. Analysis time and cost were tracked.\n\nForty-one consecutive urine samples underwent metagenomic analysis. All culture positive samples contained >200ng of DNA, suggestive of a threshold below which UTI could be ruled out solely based on DNA quantity. A semi-quantitative Diagnostic Index (DI) was created by multiplying the total DNA quantity by the relative abundance of uropathogens per urine sample. The DI allowed discrimination of UTI from non-UTI samples in all but 1 case. Metagenomic detection of AMR determinants correctly predicted the phenotype of uropathogens in 20 of 32 cases. The metagenomic work-flow was 31h and cost {euro}116 per sample, but could be reduced to 4.5h and {euro}5 for low-DNA-yield non-UTI samples.\n\nThe genomic determinants of AMR and their distribution across uropathogens need to be better understood for prediction of AMR phenotypes by metagenomics. The introduction of the DI demonstrates the potential of semi-quantitative metagenomics to replace culture as rapid diagnostic method for UTI.

microbiology↗

bioRxiv2017

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

BackgroundOne of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest (n=35) to date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of eleven metagenomics classifiers. We also assess the effects of filtering and combining tools to reduce the number of false positives.\n\nResultsTools were characterized on the basis of their ability to (1) identify taxa at the genus, species, and strain levels, (2) quantify relative abundance measures of taxa, and (3) classify individual reads to the species level. Strikingly, the number of species identified by the eleven tools can differ by over three orders of magnitude on the same datasets. However, various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Indeed, leveraging tools with different heuristics is beneficial for improved precision. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species and where customized tools may be required.\n\nConclusionsThe results of this study provide positive controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision and recall. We show that proper experimental design and analysis parameters, including depth of sequencing, choice of classifier or classifiers, database size, and filtering, can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

genomics↗