IDseq – An Open Source Cloud-based Pipeline and Analysis Service for Metagenomic Pathogen Detection and Monitoring

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 which 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.

A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis

“…Viral transcripts were identified from raw sequencing reads using IDseq (v2.11) as previously described ( 42 – 44 ). To distinguish potential viruses from laboratory reagent and skin commensal flora contaminants, a Z score was calculated for the value observed for each genus relative to water (“no template”) and uninfected CSF controls ( 45 ).…”

Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens

“…Demand grew for older resources such as BioNumerics (Applied Maths, Sint-Martens-Latem, Belgium), used by the PulseNet community [21], to allow users to submit their WGS data to NCBI, as well as the capability to download NCBI data into BioNumerics to perform local, customized analyses. As a result of building interoperable systems, researchers can now use public data from the INSDC in conjunction with private data from individual or industry labs, using platforms such as Integrated Rapid Infectious Disease Analysis (IRIDA) [22], INNUENDO [23], PathogenWatch [24] NextStrain [13], IDseq [25], and CGE Evergreen [26]. And finally, for FDA-specific missions, open source tools to support genomic epidemiology (GalaxyTrakr) [27], AMR surveillance (Resistome Tracker) [28], and risk assessment (GenomeGraphR) [29] have been created.…”

Complete Genome Sequence of a Novel Coronavirus (SARS-CoV-2) Isolate from Bangladesh

“…for host subtraction (4). SARS-CoV-2 reads were recovered by mapping the raw reads against the reference sequence (GenBank accession no.…”