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

Katrina Kalantar | Zendy; Tiago Rodrigues De Carvalho | Zendy; Charles F. A. de Bourcy | Zendy; Boris Dimitrov | Zendy; Greg Dingle | Zendy; Rebecca Egger | Zendy; Julie Han | Zendy; Olivia Holmes | Zendy; Yun-Fang Juan | Zendy; Ryan J. King | Zendy; Andrey Kislyuk | Zendy; Michael F. Lin | Zendy; Maria Mariano | Zendy; Todd Morse | Zendy; Lucia Reynoso | Zendy; David Rissato Cruz | Zendy; Jonathan Sheu | Zendy; Jennifer Tang | Zendy; James Z. Wang | Zendy; Mark A. Zhang | Zendy; Emily Zhong | Zendy; Vida Ahyong | Zendy; Sreyngim Lay | Zendy; Sophana Chea | Zendy; Jennifer A. Bohl | Zendy; Jessica E. Manning | Zendy; Cristina M. Tato | Zendy; Joseph L. DeRisi | Zendy

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

Author(s) -

Katrina Kalantar,

Tiago Rodrigues De Carvalho,

Charles F. A. de Bourcy,

Boris Dimitrov,

Greg Dingle,

Rebecca Egger,

Julie Han,

Olivia Holmes,

Yun-Fang Juan,

Ryan J. King,

Andrey Kislyuk,

Michael F. Lin,

Maria Mariano,

Todd Morse,

Lucia Reynoso,

David Rissato Cruz,

Jonathan Sheu,

Jennifer Tang,

James Z. Wang,

Mark A. Zhang,

Emily Zhong,

Vida Ahyong,

Sreyngim Lay,

Sophana Chea,

Jennifer A. Bohl,

Jessica E. Manning,

Cristina M. Tato,

Joseph L. DeRisi

Publication year - 2020

Publication title -

gigascience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.947

H-Index - 54

ISSN - 2047-217X

DOI - 10.1093/gigascience/giaa111

Subject(s) - metagenomics , pipeline (software) , cloud computing , open source , computer science , data science , pathogen , service (business) , computational biology , biology , microbiology and biotechnology , operating system , business , gene , software , genetics , marketing

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.

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