Version 1
: Received: 30 September 2023 / Approved: 2 October 2023 / Online: 3 October 2023 (03:06:42 CEST)
How to cite:
Almas, S.; Carpenter, R. E.; Tamrakar, V. K.; Singh, A.; Sharma, A.; Sharma, R. Precision Metagenomic Next-Generation Sequencing Data of Respiratory Infection. Preprints2023, 2023100082. https://doi.org/10.20944/preprints202310.0082.v1
Almas, S.; Carpenter, R. E.; Tamrakar, V. K.; Singh, A.; Sharma, A.; Sharma, R. Precision Metagenomic Next-Generation Sequencing Data of Respiratory Infection. Preprints 2023, 2023100082. https://doi.org/10.20944/preprints202310.0082.v1
Almas, S.; Carpenter, R. E.; Tamrakar, V. K.; Singh, A.; Sharma, A.; Sharma, R. Precision Metagenomic Next-Generation Sequencing Data of Respiratory Infection. Preprints2023, 2023100082. https://doi.org/10.20944/preprints202310.0082.v1
APA Style
Almas, S., Carpenter, R. E., Tamrakar, V. K., Singh, A., Sharma, A., & Sharma, R. (2023). Precision Metagenomic Next-Generation Sequencing Data of Respiratory Infection. Preprints. https://doi.org/10.20944/preprints202310.0082.v1
Chicago/Turabian Style
Almas, S., Aditya Sharma and Rahul Sharma. 2023 "Precision Metagenomic Next-Generation Sequencing Data of Respiratory Infection" Preprints. https://doi.org/10.20944/preprints202310.0082.v1
Abstract
Understanding microbial composition in upper respiratory infections (URIs) is critical for effective diagnosis and treatment. Precision Metagenomic next-generation sequencing p(mNGS) can provide a comprehensive yet clinically relevant profile of respiratory infection. The hybridization capture-based targeted sequencing generated a precision metagenomics profile of 29 patients with acute URIs. Nasopharyngeal samples were collected from the subjects suspected of respiratory infection, and p(mNGS) was performed using the Illumina®/IDbyDNA Respiratory Pathogen ID/AMR Panel (RPIP). The dataset obtained from the mNGS analysis contains a wealth of information on the composition of acute URI microbiota, including the relative abundance of known pathogens and potential clinical implications. The dataset represents a valuable resource for future research endeavors in respiratory medicine, infectious disease epidemiology, and therapeutic interventions. In addition, the dataset offers significant potential for reuse and integration with other omics datasets. The comprehensive nature of the mNGS data allows the exploration of associations between the respiratory microbiota and host factors such as clinical outcomes, immune responses, or genetic predisposition. Combining this dataset with other relevant datasets, such as transcriptomics or metabolomics, could provide a deeper understanding of the complex interactions between the microbiota and the host in acute URIs.
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.