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Cited by 406

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

IDseq—An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring

Open access
Kalantar, Katrina L, Carvalho, Tiago, de Bourcy, Charles F A, Dimitrov, Boris, Dingle, Greg, Egger, Rebecca, Han, Julie, Holmes, Olivia B, Juan, Yun-Fang, King, Ryan, Kislyuk, Andrey, Lin, Michael F, Mariano, Maria, Morse, Todd, Reynoso, Lucia V, Cruz, David Rissato, Sheu, Jonathan, Tang, Jennifer, Wang, James, Zhang, Mark A, Zhong, Emily, Ahyong, Vida, Lay, Sreyngim, Chea, Sophana, Bohl, Jennifer A, Manning, Jessica E, Tato, Cristina M, DeRisi, Joseph L·2020·GigaScience
DOI 10.1093/gigascience/giaa111
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Abstract

Background: Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments. Findings: We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2. Conclusion: The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.

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Metadata

Authors
Kalantar, K. L., Carvalho, T., de Bourcy, C. F. A., Dimitrov, B., Dingle, G., Egger, R., Han, J., Holmes, O. B., Juan, Y.-F., King, R., Kislyuk, A., Lin, M. F., Mariano, M., Morse, T., Reynoso, L. V., Cruz, D. R., Sheu, J., Tang, J., Wang, J., Zhang, M. A., Zhong, E., Ahyong, V., Lay, S., Chea, S., Bohl, J. A., Manning, J. E., Tato, C. M., & DeRisi, J. L.
Venue
GigaScience
Publisher
Oxford University Press (OUP)
Published
2020-10
Volume
9
Issue
10
ISSN
2047-217X (ISSN-L)
DOI
10.1093/gigascience/giaa111
OpenAlex
W3093218166
Cited by
406
References
11
FWCI
13.28
Type
journal article
Language
en
Open access
gold

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