Pathogen detection in RNA-seq data with Pathonoia.
Metagenomics
Pathogen detection
RNA sequencing
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
17 Feb 2023
17 Feb 2023
Historique:
received:
29
08
2022
accepted:
10
01
2023
entrez:
21
2
2023
pubmed:
22
2
2023
medline:
25
2
2023
Statut:
epublish
Résumé
Bacterial and viral infections may cause or exacerbate various human diseases and to detect microbes in tissue, one method of choice is RNA sequencing. The detection of specific microbes using RNA sequencing offers good sensitivity and specificity, but untargeted approaches suffer from high false positive rates and a lack of sensitivity for lowly abundant organisms. We introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host interactions by correlating the microbial to the host gene expression. Pathonoia outperforms state-of-the-art methods in microbial detection specificity, both on in silico and real datasets. Two case studies in human liver and brain show how Pathonoia can support novel hypotheses on microbial infection exacerbating disease. The Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub.
Sections du résumé
BACKGROUND
BACKGROUND
Bacterial and viral infections may cause or exacerbate various human diseases and to detect microbes in tissue, one method of choice is RNA sequencing. The detection of specific microbes using RNA sequencing offers good sensitivity and specificity, but untargeted approaches suffer from high false positive rates and a lack of sensitivity for lowly abundant organisms.
RESULTS
RESULTS
We introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host interactions by correlating the microbial to the host gene expression. Pathonoia outperforms state-of-the-art methods in microbial detection specificity, both on in silico and real datasets.
CONCLUSION
CONCLUSIONS
Two case studies in human liver and brain show how Pathonoia can support novel hypotheses on microbial infection exacerbating disease. The Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub.
Identifiants
pubmed: 36803415
doi: 10.1186/s12859-023-05144-z
pii: 10.1186/s12859-023-05144-z
pmc: PMC9938591
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
53Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : CRU306 P-C
Organisme : Forschungs- und Wissenschaftsstiftung Hamburg
ID : LFF 78
Informations de copyright
© 2023. The Author(s).
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