scholarly journals Fast Virome Explorer: A Pipeline for Virus and Phage Identification and Abundance Profiling in Metagenomics Data

2017 ◽  
Author(s):  
Saima Sultana Tithi ◽  
Roderick V. Jensen ◽  
Liqing Zhang
Keyword(s):  
Next Generation Sequencing ◽  
Important Implication ◽  
Simulated Data ◽  
Large Data ◽  
Rapid Identification ◽  
Metagenomic Data ◽  
Efficient Manner ◽  
Metagenomics Data ◽  
Data Files ◽  
Generation Sequencing

AbstractIdentifying viruses and phages in a metagenomics sample has important implication in improving human health, preventing viral outbreaks, and developing personalized medicine. With the rapid increase in data files generated by next generation sequencing, existing tools for identifying and annotating viruses and phages in metagenomics samples suffer from expensive running time. In this paper, we developed a stand-alone pipeline, FastViromeExplorer, for rapid identification and abundance quantification of viruses and phages in big metagenomic data. Both real and simulated data validated FastViromeExplorer as a reliable tool to accurately identify viruses and their abundances in large data, as well as in a time efficient manner.

scholarly journals FastViromeExplorer: a pipeline for virus and phage identification and abundance profiling in metagenomics data

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4227 ◽  
Author(s):  
Saima Sultana Tithi ◽  
Frank O. Aylward ◽  
Roderick V. Jensen ◽  
Liqing Zhang
Keyword(s):  
Next Generation Sequencing ◽  
Environmental Samples ◽  
Human Microbiome ◽  
Real Data ◽  
Large Datasets ◽  
Metagenomic Data ◽  
Reliable Tool ◽  
Metagenomics Data ◽  
Generation Sequencing ◽  
Phage Sequence

With the increase in the availability of metagenomic data generated by next generation sequencing, there is an urgent need for fast and accurate tools for identifying viruses in host-associated and environmental samples. In this paper, we developed a stand-alone pipeline called FastViromeExplorer for the detection and abundance quantification of viruses and phages in large metagenomic datasets by performing rapid searches of virus and phage sequence databases. Both simulated and real data from human microbiome and ocean environmental samples are used to validate FastViromeExplorer as a reliable tool to quickly and accurately identify viruses and their abundances in large datasets.

scholarly journals Teaching NeuroImage: Rapid Identification of Infectious Optic Neuritis by Next-Generation Sequencing

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000013213
Author(s):  
Ying Huang ◽  
Yulu Liu ◽  
Yongguang Liu ◽  
Qiang Li ◽  
Xuejun Fu ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Optic Neuritis ◽  
Rapid Identification ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals A Comparison of Two DNA Metagenomic Bioinformatic Pipelines While Evaluating the Microbial Diversity in Feces of Tanzanian Small Holder Dairy Cattle

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Felix M. Kibegwa ◽  
Rawlynce C. Bett ◽  
Charles K. Gachuiri ◽  
Francesca Stomeo ◽  
Fidalis D. Mujibi
Keyword(s):  
Next Generation Sequencing ◽  
Metagenomic Data ◽  
Diversity Analysis ◽  
Taxonomic Identification ◽  
Sequencing Platforms ◽  
High Level ◽  
Small Holder ◽  
Generation Sequencing ◽  
Bacterial Domain

Analysis of shotgun metagenomic data generated from next generation sequencing platforms can be done through a variety of bioinformatic pipelines. These pipelines employ different sets of sophisticated bioinformatics algorithms which may affect the results of this analysis. In this study, we compared two commonly used pipelines for shotgun metagenomic analysis: MG-RAST and Kraken 2, in terms of taxonomic classification, diversity analysis, and usability using their primarily default parameters. Overall, the two pipelines detected similar abundance distributions in the three most abundant taxa Proteobacteria, Firmicutes, and Bacteroidetes. Within bacterial domain, 497 genera were identified by both pipelines, while an additional 694 and 98 genera were solely identified by Kraken 2 and MG-RAST, respectively. 933 species were detected by the two algorithms. Kraken 2 solely detected 3550 species, while MG-RAST identified 557 species uniquely. For archaea, Kraken 2 generated 105 and 236 genera and species, respectively, while MG-RAST detected 60 genera and 88 species. 54 genera and 72 species were commonly detected by the two methods. Kraken 2 had a quicker analysis time (~4 hours) while MG-RAST took approximately 2 days per sample. This study revealed that Kraken 2 and MG-RAST generate comparable results and that a reliable high-level overview of sample is generated irrespective of the pipeline selected. However, Kraken 2 generated a more accurate taxonomic identification given the higher number of “Unclassified” reads in MG-RAST. The observed variations at the genus level show that a main restriction is using different databases for classification of the metagenomic data. The results of this research indicate that a more inclusive and representative classification of microbiomes may be achieved through creation of the combined pipelines.

Balamuthia mandrillaris-Related Primary Amoebic Encephalitis in China Diagnosed by Next Generation Sequencing and a Review of the Literature

2019 ◽  
Author(s):  
Yinan Yang ◽  
Xiaobin Hu ◽  
Li Min ◽  
Xiangyu Dong ◽  
Yuanlin Guan
Keyword(s):  
Next Generation Sequencing ◽  
Inflammatory Diseases ◽  
Clinical Manifestations ◽  
Infectious Agent ◽  
Disease Diagnosis ◽  
Rapid Identification ◽  
Next Generation ◽  
Balamuthia Mandrillaris ◽  
Granulomatous Amoebic Encephalitis ◽  
Generation Sequencing

Abstract Background Encephalitis is caused by infection, immune mediated diseases, or primary inflammatory diseases. Of all the causative infectious pathogens, 90% are viruses or bacteria. Granulomatous amoebic encephalitis (GAE), caused by Balamuthia mandrillaris, is a rare but life-threatening disease. Diagnosis and therapy are frequently delayed due to the lack of specific clinical manifestations. Method A healthy 2 year old Chinese male patient initially presented with a nearly 2 month history of irregular fever. We present this case of granulomatous amoebic encephalitis caused by B. mandrillaris. Next generation sequencing of the patient’s cerebrospinal fluid (CSF) was performed to identify an infectious agent. Result The results of next generation sequencing of the CSF showed that most of the mapped reads belonged to Balamuthia mandrillaris. Conclusion Next generation sequencing (NGS) is an unbiased and rapid diagnostic tool. The NGS method can be used for the rapid identification of causative pathogens. The NGS method should be widely applied in clinical practice and help clinicians provide direction for the diagnosis of diseases, especially for rare and difficult cases.

scholarly journals Developing Genome and Exome Sequencing for Candidate Gene Identification in Inherited Disorders: An Integrated Technical and Bioinformatics Approach

2013 ◽  
Vol 137 (3) ◽  
pp. 415-433 ◽  
Author(s):  
Emily M. Coonrod ◽  
Jacob D. Durtschi ◽  
Rebecca L. Margraf ◽  
Karl V. Voelkerding
Keyword(s):  
Next Generation Sequencing ◽  
Large Data ◽  
Data Sets ◽  
Next Generation ◽  
Inherited Disorders ◽  
Disease Gene Discovery ◽  
Concurrent Development ◽  
Effective Use ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Context.—Advances in sequencing technology with the commercialization of next-generation sequencing (NGS) has substantially increased the feasibility of sequencing human genomes and exomes. Next-generation sequencing has been successfully applied to the discovery of disease-causing genes in rare, inherited disorders. By necessity, the advent of NGS has fostered the concurrent development of bioinformatics approaches to expeditiously analyze the large data sets generated. Next-generation sequencing has been used for important discoveries in the research setting and is now being implemented into the clinical diagnostic arena. Objective.—To review the current literature on technical and bioinformatics approaches for exome and genome sequencing and highlight examples of successful disease gene discovery in inherited disorders. To discuss the challenges for implementing NGS in the clinical research and diagnostic arenas. Data Sources.—Literature review and authors' experience. Conclusions.—Next-generation sequencing approaches are powerful and require an investment in infrastructure and personnel expertise for effective use; however, the potential for improvement of patient care through faster and more accurate molecular diagnoses is high.

scholarly journals Analysis Comparison for Rapid Identification of Pathogenic Virus from Infected Tissue Samples

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 196
Author(s):  
Junji Hosokawa-Muto ◽  
Yukiko Sassa-O’Brien ◽  
Yoshihito Fujinami ◽  
Hiroaki Nakahara
Keyword(s):  
Influenza Virus ◽  
Next Generation Sequencing ◽  
Rapid Determination ◽  
Rapid Identification ◽  
Infected Tissue ◽  
Mouse Lung ◽  
Next Generation ◽  
Tissue Samples ◽  
Time Required ◽  
Generation Sequencing

When examining infectious samples, rapid identification of the pathogenic agent is required for diagnosis and treatment or for investigating the cause of death. In our previous study, we applied exhaustive amplification using non-specific primers (the rapid determination system of viral genome sequences, the RDV method) to identify the causative virus via swab samples from a cat with a suspected viral infection. The purpose of the current study is to investigate suitable methods for the rapid identification of causative pathogens from infected tissue samples. First, the influenza virus was inoculated into mice to prepare infected tissue samples. RNA extracted from the mouse lung homogenates was transcribed into cDNA and then analyzed using the RDV method and next-generation sequencing, using MiSeq and MinION sequencers. The RDV method was unable to detect the influenza virus in the infected tissue samples. However, influenza virus reads were detected using next-generation sequencing. Comparing MiSeq and MinION, the time required for library and sequence preparation was shorter for MinION sequencing than for MiSeq sequencing. We conclude that when a causative virus needs to be rapidly identified from an infectious sample, MinION sequencing is currently the method of choice.

scholarly journals 663. Two (Plus) Birds, One Stone: The Rapid, Comprehensive, Non-invasive Detection of Co-Pathogens of Critical Importance Using A Plasma-based Microbial Cell-free DNA Next-generation Sequencing Test

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S433-S434
Author(s):  
Matthew Smollin ◽  
Martin S Lindner ◽  
Nicholas R Degner ◽  
Ricardo Castillo-Galvan ◽  
Jose Alexander ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Rapid Identification ◽  
Microbial Cell ◽  
Next Generation ◽  
Critical Importance ◽  
Cell Free Dna ◽  
Non Invasive ◽  
Free Dna ◽  
Aspergillus Sp ◽  
Generation Sequencing

Abstract Background Immunocompromised (IC) patients are at risk for infections by a spectrum of invasive pathogens. The overlap in presentation makes it challenging to differentiate among infectious etiologies and critical co-infections (CI) may remain undiagnosed. Open-ended, comprehensive assessment of infection through microbial cell-free DNA (mcfDNA) next-generation sequencing (NGS) of plasma offers the potential for the rapid identification of multiple co-infecting pathogens of critical importance (CI-POCI) with one test. Methods Karius TestTM (KT) results from patients who underwent clinical testing from December 2016 to April 2021 were reviewed for detections of two or more CI-POCI including parasites, fungi (Pneumocystis jirovecii, Trichosporon sp, endemic mycoses, Aspergillus sp., Mucorales, Non-Aspergillus/Non-Mucorales molds), mycobacteria, Legionella sp., Nocardia sp. and Listeria. KT, developed and validated in Karius’ CLIA certified/CAP accredited lab, detects mcfDNA from plasma. McfDNA is extracted, NGS performed, human sequences removed and remaining sequences aligned to a curated pathogen database of > 1500 organisms. Organisms present above a statistical threshold are reported and quantified. For > 85% of tests the time to result reporting is the next day from sample receipt. Results KT detected CI of two or more POCI in 59 samples (75% adults, 25% children). The most common partnering co-pathogens of critical importance were Aspergillus sp (38), Mucorales (17) and PJP (14); the most common combinations were two or more distinct Aspergillus sp (14) followed by an Aspergillus sp and a Mucorales (12). There were 17 samples with the detection of three or more CI-POCI (29%). Figure 1. Chord Plot of Co-infections with Pathogens of Critical Importance The outer circle sections represent Karius Test detections belonging to different taxonomic groups. The length of each circle section is proportional to the total number of detections of a taxon belonging to that group. The chords connecting a pair of circle sections are proportional to the number of times two taxa from those groups were observed together, weighted by the total number of taxa detected. Conclusion Plasma mcfDNA NGS offers a rapid, comprehensive non-invasive means of detecting CI-POCI in IC patients with one test. Although rare, co-infections with POCI can greatly increase mortality. The KT can provide important insights into pathogen-pathogen interactions in complex hosts and help optimize therapy. Disclosures Matthew Smollin, PharmD, Karius, Inc. (Employee) Martin S. Lindner, PhD, Karius, Inc. (Consultant) Nicholas R. Degner, MD, MPH, MS, Karius Inc. (Employee, Shareholder) Ricardo Castillo-Galvan, MD MPH, Karius Inc. (Consultant) Jose Alexander, MD, D(ABMM), FCCM, CIC, SM, MB(ASCP), BCMAS, Karius (Employee) Ann Macintyre, DO, Karius, Inc. (Employee) Bradley Perkins, MD, Karius, Inc. (Employee) Asim A. Ahmed, MD, Karius, Inc. (Employee) Aparna Arun, MD, Karius (Employee)

Next-Generation Sequencing for Metagenomic Data: Assembling and Binning

2015 ◽  
pp. 539-544
Author(s):  
Henry C. M. Leung ◽  
Yi Wang ◽  
S. M. Yiu ◽  
Francis Y. L. Chin
Keyword(s):  
Next Generation Sequencing ◽  
Metagenomic Data ◽  
Next Generation ◽  
Generation Sequencing
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