Dataset Information

A streamlined clinical metagenomic sequencing protocol for rapid pathogen identification.

ABSTRACT: Metagenomic next-generation sequencing (mNGS) holds promise as a diagnostic tool for unbiased pathogen identification and precision medicine. However, its medical utility depends largely on assay simplicity and reproducibility. In the current study, we aimed to develop a streamlined Illumina and Oxford Nanopore-based DNA/RNA library preparation protocol and rapid data analysis pipeline. The Illumina sequencing-based mNGS method was first developed and evaluated using a set of samples with known aetiology. Its sensitivity for RNA viruses (influenza A, H1N1) was < 6.4 × 10² EID50/mL, and a good correlation between viral loads and mapped reads was observed. Then, the rapid turnaround time of Nanopore sequencing was tested by sequencing influenza A virus and adenoviruses. Furthermore, 11 respiratory swabs or sputum samples pre-tested for a panel of pathogens were analysed, and the pathogens identified by Illumina sequencing showed 81.8% concordance with qPCR results. Additional sequencing of cerebrospinal fluid (CSF) samples from HIV-1-positive patients with meningitis/encephalitis detected HIV-1 RNA and Toxoplasma gondii sequences. In conclusion, we have developed a simplified protocol that realizes efficient metagenomic sequencing of a variety of clinical samples and pathogen identification in a clinically meaningful time frame.

SUBMITTER: Jia X

PROVIDER: S-EPMC7902651 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A streamlined clinical metagenomic sequencing protocol for rapid pathogen identification.

Jia Xiaofang X Hu Lvyin L Wu Min M Ling Yun Y Wang Wei W Lu Hongzhou H Yuan Zhenghong Z Yi Zhigang Z Zhang Xiaonan X

Scientific reports 20210223 1

Metagenomic next-generation sequencing (mNGS) holds promise as a diagnostic tool for unbiased pathogen identification and precision medicine. However, its medical utility depends largely on assay simplicity and reproducibility. In the current study, we aimed to develop a streamlined Illumina and Oxford Nanopore-based DNA/RNA library preparation protocol and rapid data analysis pipeline. The Illumina sequencing-based mNGS method was first developed and evaluated using a set of samples with known ...[more]

PMID: 33623127

Similar Datasets

Project description:Introduction: Metagenomic next-generation sequencing (mNGS) has emerged as a powerful tool for rapid pathogen identification in clinical practice. However, the parameters used to interpret mNGS data, such as read count, genus rank, and coverage, lack explicit performance evaluation. In this study, the developed indicators as well as novel parameters were assessed for their performance in bacterium detection. Methods: We developed several relevant parameters, including 10M normalized reads, double-discard reads, Genus Rank Ratio, King Genus Rank Ratio, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank. These parameters, together with frequently used read indicators including raw reads, reads per million mapped reads (RPM), transcript per kilobase per million mapped reads (TPM), Genus Rank, and coverage were analyzed for their diagnostic efficiency in bronchoalveolar lavage fluid (BALF), a common source for detecting eight bacterium pathogens: Acinetobacter baumannii, Klebsiella pneumoniae, Streptococcus pneumoniae, Staphylococcus aureus, Hemophilus influenzae, Stenotrophomonas maltophilia, Pseudomonas aeruginosa, and Aspergillus fumigatus. Results: The results demonstrated that these indicators exhibited good diagnostic efficacy for the eight pathogens. The AUC values of all indicators were almost greater than 0.9, and the corresponding sensitivity and specificity values were almost greater than 0.8, excepted coverage. The negative predictive value of all indicators was greater than 0.9. The results showed that the use of double-discarded reads, Genus Rank Ratio*Genus Rank, and King Genus Rank Ratio*Genus Rank exhibited better diagnostic efficiency than that of raw reads, RPM, TPM, and in Genus Rank. These parameters can serve as a reference for interpreting mNGS data of BALF. Moreover, precision filters integrating our novel parameters were built to detect the eight bacterium pathogens in BALF samples through machine learning. Summary: In this study, we developed a set of novel parameters for pathogen identification in clinical mNGS based on reads and ranking. These parameters were found to be more effective in diagnosing pathogens than traditional approaches. The findings provide valuable insights for improving the interpretation of mNGS reports in clinical settings, specifically in BALF analysis.

Dataset Information

A streamlined clinical metagenomic sequencing protocol for rapid pathogen identification.

Publications

A streamlined clinical metagenomic sequencing protocol for rapid pathogen identification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets