Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing
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, t...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-11-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1266990/full |
| _version_ | 1797596195862872064 |
|---|---|
| author | Xiwen Jiang Jinghai Yan Hao Huang Lu Ai Xuegao Yu Pengqiang Zhong Yili Chen Zhikun Liang Wancen Qiu Huiying Huang Wenyan Yan Yan Liang Peisong Chen Ruizhi Wang |
| author_facet | Xiwen Jiang Jinghai Yan Hao Huang Lu Ai Xuegao Yu Pengqiang Zhong Yili Chen Zhikun Liang Wancen Qiu Huiying Huang Wenyan Yan Yan Liang Peisong Chen Ruizhi Wang |
| author_sort | Xiwen Jiang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T02:47:12Z |
| format | Article |
| id | doaj.art-d6eddcb89235414ba0578c81c4ec7e59 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-11T02:47:12Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-d6eddcb89235414ba0578c81c4ec7e592023-11-18T09:15:29ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-11-011410.3389/fgene.2023.12669901266990Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencingXiwen Jiang0Jinghai Yan1Hao Huang2Lu Ai3Xuegao Yu4Pengqiang Zhong5Yili Chen6Zhikun Liang7Wancen Qiu8Huiying Huang9Wenyan Yan10Yan Liang11Peisong Chen12Ruizhi Wang13College of Biological Science and Engineering, Fuzhou University, Fuzhou, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, ChinaGuangzhou Darui Biotechnology Co., Ltd., Guangzhou, ChinaGuangzhou Darui Biotechnology Co., Ltd., Guangzhou, ChinaGuangzhou Darui Biotechnology Co., Ltd., Guangzhou, ChinaGuangzhou Darui Biotechnology Co., Ltd., Guangzhou, ChinaGuangzhou Darui Biotechnology Co., Ltd., Guangzhou, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, ChinaIntroduction: 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.https://www.frontiersin.org/articles/10.3389/fgene.2023.1266990/fullmNGSROC curveindicatorsreadsrank |
| spellingShingle | Xiwen Jiang Jinghai Yan Hao Huang Lu Ai Xuegao Yu Pengqiang Zhong Yili Chen Zhikun Liang Wancen Qiu Huiying Huang Wenyan Yan Yan Liang Peisong Chen Ruizhi Wang Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing Frontiers in Genetics mNGS ROC curve indicators reads rank |
| title | Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing |
| title_full | Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing |
| title_fullStr | Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing |
| title_full_unstemmed | Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing |
| title_short | Development of novel parameters for pathogen identification in clinical metagenomic next-generation sequencing |
| title_sort | development of novel parameters for pathogen identification in clinical metagenomic next generation sequencing |
| topic | mNGS ROC curve indicators reads rank |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1266990/full |
| work_keys_str_mv | AT xiwenjiang developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT jinghaiyan developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT haohuang developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT luai developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT xuegaoyu developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT pengqiangzhong developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT yilichen developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT zhikunliang developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT wancenqiu developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT huiyinghuang developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT wenyanyan developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT yanliang developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT peisongchen developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing AT ruizhiwang developmentofnovelparametersforpathogenidentificationinclinicalmetagenomicnextgenerationsequencing |