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

Naccache, Samia N.; Federman, Scot; Veeraraghavan, Narayanan; Zaharia, Matei; Lee, Deanna; Samayoa, Erik; Bouquet, Jérôme; Greninger, Alexander L.; Luk, Ka Cheung; Enge, Barryett; Wadford, Debra A.; Messenger, Sharon; Genrich, Gillian L.; Pellegrino, Kristen; Grard, Gilda; Leroy, Eric M.; Schneider, Bradley S.; Fair, Joseph N.; Martı́nez, Miguel Ángel; Iša, Pavel; Crump, John A.; DeRisi, Joseph L.; Sittler, Taylor; Hackett, John; Miller, Steve; Chiu, Charles Y.

doi:10.1101/gr.171934.113

Cited by 443 publications

(414 citation statements)

References 54 publications

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“…Libraries were sequenced as 100 nt paired-end reads on 4 lanes of an Illumina HiSeq2000 instrument, generating a total of 754 million raw reads with an average of 11 million paired-end reads per barcode. Reads were processed using the SURPI bioinformatics pipeline [18]. Briefly, using the SNAP nucleotide aligner, human reads were first computationally subtracted, followed by alignment of remaining reads to all sequences in the comprehensive NCBI nt database.…”

Section: Identification and Genome Sequencing Of Human Pegivirus 2 (Hmentioning

confidence: 99%

“…Here we employed NGS and the sequencebased ultra-rapid pathogen identification (SURPI) bioinformatics pipeline for pathogen detection [18] to identify a novel pegivirus, provisionally named HPgV-2, in an HCV-infected patient who died from sepsis and multi-organ failure of unknown etiology. Subsequent alignment to the human hg38 reference database [45].…”

Section: Introductionmentioning

confidence: 99%

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Discovery of a Novel Human Pegivirus in Blood Associated with Hepatitis C Virus Co-Infection

Berg¹,

Lee²,

Coller³

et al. 2016

Journal of Hepatology

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Hepatitis C virus (HCV) and human pegivirus (HPgV), formerly GBV-C, are the only known human viruses in the Hepacivirus and Pegivirus genera, respectively, of the family Flaviviridae. We present the discovery of a second pegivirus, provisionally designated human pegivirus 2 (HPgV-2), by next-generation sequencing of plasma from an HCV-infected patient with multiple bloodborne exposures who died from sepsis of unknown etiology. HPgV-2 is highly divergent, situated on a deep phylogenetic branch in a clade that includes rodent and bat pegiviruses, with which it shares <32% amino acid identity. Molecular and serological tools were developed and validated for high-throughput screening of plasma samples, and a panel of 3 independent serological markers strongly correlated antibody responses with viral RNA positivity (99.9% negative predictive value). Discovery of 11 additional RNA-positive samples from a total of 2440 screened (0.45%) revealed 93-94% nucleotide identity between HPgV-2 strains. All 12 HPgV-2 RNA-positive cases were identified in individuals also testing positive for HCV RNA (12 of 983; 1.22%), including 2 samples co-infected with HIV, but HPgV-2 RNA was not detected in non-HCV-infected individuals (p<0.0001), including those singly infected by HIV (p = 0.0075) or HBV (p = 0.0077), nor in volunteer blood donors (p = 0.0082). Nine of the 12 (75%) HPgV-2 RNA positive samples were reactive for antibodies to viral serologic markers, whereas only 28 of 2,429 (1.15%) HPgV-2 RNA negative samples were seropositive. Longitudinal sampling in two individuals revealed that active HPgV-2 infection can persist in blood for at least 7 weeks, despite the presence of virus-specific antibodies. One individual harboring both HPgV-2 and HCV RNA was found to be seronegative for both viruses, suggesting a high likelihood of simultaneous acquisition of HCV and HPgV-2 infection from an acute co-transmission event. Taken together, our PLOS Pathogens |

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Section: Identification and Genome Sequencing Of Human Pegivirus 2 (Hmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discovery of a Novel Human Pegivirus in Blood Associated with Hepatitis C Virus Co-Infection

Berg¹,

Lee²,

Coller³

et al. 2016

Journal of Hepatology

View full text Add to dashboard Cite

show abstract

“…Unbiased NGS approaches enable comprehensive pathogen detection in the clinical microbiology laboratory [63] and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. Sequence-based ultra rapid pathogen identification (SURPI™) [53] is a computational pipeline for pathogen identification from complex metagenomic NGS data generated.…”

Section: Open-source Toolsmentioning

confidence: 99%

Cloud Computing for Next-Generation Sequencing Data Analysis

Zhao¹,

Watrous²,

Zhang³

et al. 2017

Cloud Computing - Architecture and Applications

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High-throughput next-generation sequencing (NGS) technologies have evolved rapidly and are reshaping the scope of genomics research. The substantial decrease in the cost of NGS techniques in the past decade has led to its rapid adoption in biological research and drug development. Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis.

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“…DecontaMiner differs from other tools like Taxonomer [3] or SURPI [4], because it is not aimed to address metagenomics studies but it is focused on the detection and identification of upstream/downstream contamination among the unmapped reads. DecontaMiner does not require complicated installation like in RNA-COMPASS [5], since it exploits several tools that are widely used by the sequencing community.…”

Section: Introductionmentioning

confidence: 99%

From trash to treasure: detecting unexpected contamination in unmapped NGS data

Granata

Sangiovanni

Thind

et al. 2017

Preprint

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Standard procedures for NGS data analysis involve a pre-processing step of reads quality assessment, followed by the alignment of the filtered reads to a reference genome. Typically the amount of reads that correctly maps to the specific reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. Investigating the reasons of this discrepancy may provide relevant information about the source of the so called unmapped reads. It is not unusual that genetic material of microorganisms is present in biological samples undergoing sequencing. These exogenous sequences can derive from the normal or altered tissues microbiome (upstream contamination) or from a contamination occurring during the samples processing (downstream contamination). Here we propose DecontaMiner, a tool to unravel the presence of contaminating sequences among the unmapped reads. It uses a subtraction approach in which the sequences are first filtered according to quality parameters and then mapped to ribosomal, mithocondrial and foreign organism's databases. The reads that do not map on human genome are then mapped, through a local alignment algorithm (MegaBlast), to bacteria, fungi and viruses genome. DecontaMiner generates several output files to track all the processed reads, and to provide a complete report of their characteristics. The good quality matches on microorganism genomes are counted and compared among samples. The main novelty of DecontaMiner is the versatility of its use together with a complete, easy to use, and automatic pipeline. DecontaMiner has been mainly used to detect contamination in human RNA-seq data, but the pipeline can be easily tailored using the configuration files and flags to process DNA-seq data, and unmapped data coming from non-human species.

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

Cited by 443 publications

References 54 publications

Discovery of a Novel Human Pegivirus in Blood Associated with Hepatitis C Virus Co-Infection

Discovery of a Novel Human Pegivirus in Blood Associated with Hepatitis C Virus Co-Infection

Cloud Computing for Next-Generation Sequencing Data Analysis

From trash to treasure: detecting unexpected contamination in unmapped NGS data

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