A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples

By S. Naccache, S. Federman, N. Veeraraghavan, Matei A. Zaharia, Deanna Lee, Erik Samayoa, J. Bouquet, A. Greninger, K. Luk, B. Enge and 16 more. Genome Research, 2014

464 citations recorded by the source corpus.

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (“sequence-based ultrarapid pathogen identification”), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7–500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.

Subfields: Ecology, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/d4d6bef7e3df07197a9882a89f140a394e3c6a5c

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Citations in the corpus

• NCBI Viral Genomes Resource (2014, background): “Whereas primary databases are archival repositories of sequence data, reference databases provide curated datasets that enable a number of activities, among them are transfer annotation to related genomes (11) (12) (13) , sequence assembly and virus discovery (14) (15) (16) (17) , viral dynamics and”
• Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics (2015, unknown): “, 2012), and for specific pathogens, we also used SURPI (Naccache et al., 2014) and the BWA (see below) (Li and Durbin, 2010).”
• VFDB 2016: hierarchical and refined dataset for big data analysis—10 years on (2015, background): “However, given the vast amount of sequencing data generated from NGS-based studies and the limited computing resources available for regular bioinformatic analysis, the reliable and timely analysis of big data remains a challenging task (12,13).”
• Genome Detective: an automated system for virus identification from high-throughput sequencing data (2018, methodology): “These were the same datasets that the authors of drVM used to compare with three other similar tools, SURPI (Naccache et al., 2014), VIP (Li et al., 2016) and VirusTap (Yamashita et al., 2016).”
• Understanding the Promises and Hurdles of Metagenomic Next-Generation Sequencing as a Diagnostic Tool for Infectious Diseases (2017, methodology): “Both of these pipelines rapidly align the sequencing reads to the National Center for Biotechnology Information (NCBI) nt reference database and use taxonomic classification for more accurate read assignments [36, 37].”