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Comprehensive human virus screening using high-throughput sequencing with a user-friendly representation of bioinformatics analysis: a pilot study.

ABSTRACT: High-throughput sequencing (HTS) provides the means to analyze clinical specimens in unprecedented molecular detail. While this technology has been successfully applied to virus discovery and other related areas of research, HTS methodology has yet to be exploited for use in a clinical setting for routine diagnostics. Here, a bioinformatics pipeline (ezVIR) was designed to process HTS data from any of the standard platforms and to evaluate the entire spectrum of known human viruses at once, providing results that are easy to interpret and customizable. The pipeline works by identifying the most likely viruses present in the specimen given the sequencing data. Additionally, ezVIR can generate optional reports for strain typing, can create genome coverage histograms, and can perform cross-contamination analysis for specimens prepared in series. In this pilot study, the pipeline was challenged using HTS data from 20 clinical specimens representative of those most often collected and analyzed in daily practice. The specimens (5 cerebrospinal fluid, 7 bronchoalveolar lavage fluid, 5 plasma, 2 serum, and 1 nasopharyngeal aspirate) were originally found to be positive for a diverse range of DNA or RNA viruses by routine molecular diagnostics. The ezVIR pipeline correctly identified 14 of 14 specimens containing viruses with genomes of <40,000 bp, and 4 of 6 specimens positive for large-genome viruses. Although further validation is needed to evaluate sensitivity and to define detection cutoffs, results obtained in this pilot study indicate that the overall detection success rate, coupled with the ease of interpreting the analysis reports, makes it worth considering using HTS for clinical diagnostics.

SUBMITTER: Petty TJ

PROVIDER: S-EPMC4313162 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Comprehensive human virus screening using high-throughput sequencing with a user-friendly representation of bioinformatics analysis: a pilot study.

Petty Tom J TJ Cordey Samuel S Padioleau Ismael I Docquier Mylène M Turin Lara L Preynat-Seauve Olivier O Zdobnov Evgeny M EM Kaiser Laurent L

Journal of clinical microbiology 20140709 9

High-throughput sequencing (HTS) provides the means to analyze clinical specimens in unprecedented molecular detail. While this technology has been successfully applied to virus discovery and other related areas of research, HTS methodology has yet to be exploited for use in a clinical setting for routine diagnostics. Here, a bioinformatics pipeline (ezVIR) was designed to process HTS data from any of the standard platforms and to evaluate the entire spectrum of known human viruses at once, prov ...[more]

PMID: 25009045

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Project description:BackgroundExplosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. Using a MapReduce framework, data and workload can be distributed via a network to computers in the cloud to substantially reduce computational latency. Hadoop/MapReduce has been successfully adopted in bioinformatics for genome assembly, mapping reads to genomes, and finding single nucleotide polymorphisms. Major cloud providers offer Hadoop cloud services to their users. However, it remains technically challenging to deploy a Hadoop cloud for those who prefer to run MapReduce programs in a cluster without built-in Hadoop/MapReduce.ResultsWe present CloudDOE, a platform-independent software package implemented in Java. CloudDOE encapsulates technical details behind a user-friendly graphical interface, thus liberating scientists from having to perform complicated operational procedures. Users are guided through the user interface to deploy a Hadoop cloud within in-house computing environments and to run applications specifically targeted for bioinformatics, including CloudBurst, CloudBrush, and CloudRS. One may also use CloudDOE on top of a public cloud. CloudDOE consists of three wizards, i.e., Deploy, Operate, and Extend wizards. Deploy wizard is designed to aid the system administrator to deploy a Hadoop cloud. It installs Java runtime environment version 1.6 and Hadoop version 0.20.203, and initiates the service automatically. Operate wizard allows the user to run a MapReduce application on the dashboard list. To extend the dashboard list, the administrator may install a new MapReduce application using Extend wizard.ConclusionsCloudDOE is a user-friendly tool for deploying a Hadoop cloud. Its smart wizards substantially reduce the complexity and costs of deployment, execution, enhancement, and management. Interested users may collaborate to improve the source code of CloudDOE to further incorporate more MapReduce bioinformatics tools into CloudDOE and support next-generation big data open source tools, e.g., Hadoop BigTop and Spark.AvailabilityCloudDOE is distributed under Apache License 2.0 and is freely available at http://clouddoe.iis.sinica.edu.tw/.

Project description:Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

Project description:BackgroundHigh-throughput sequencing has increased the number of available microbial genomes recovered from isolates, single cells, and metagenomes. Accordingly, fast and comprehensive functional gene annotation pipelines are needed to analyze and compare these genomes. Although several approaches exist for genome annotation, these are typically not designed for easy incorporation into analysis pipelines, do not combine results from different annotation databases or offer easy-to-use summaries of metabolic reconstructions, and typically require large amounts of computing power for high-throughput analysis not available to the average user.ResultsHere, we introduce MicrobeAnnotator, a fully automated, easy-to-use pipeline for the comprehensive functional annotation of microbial genomes that combines results from several reference protein databases and returns the matching annotations together with key metadata such as the interlinked identifiers of matching reference proteins from multiple databases [KEGG Orthology (KO), Enzyme Commission (E.C.), Gene Ontology (GO), Pfam, and InterPro]. Further, the functional annotations are summarized into Kyoto Encyclopedia of Genes and Genomes (KEGG) modules as part of a graphical output (heatmap) that allows the user to quickly detect differences among (multiple) query genomes and cluster the genomes based on their metabolic similarity. MicrobeAnnotator is implemented in Python 3 and is freely available under an open-source Artistic License 2.0 from https://github.com/cruizperez/MicrobeAnnotator .ConclusionsWe demonstrated the capabilities of MicrobeAnnotator by annotating 100 Escherichia coli and 78 environmental Candidate Phyla Radiation (CPR) bacterial genomes and comparing the results to those of other popular tools. We showed that the use of multiple annotation databases allows MicrobeAnnotator to recover more annotations per genome compared to faster tools that use reduced databases and is computationally efficient for use in personal computers. The output of MicrobeAnnotator can be easily incorporated into other analysis pipelines while the results of other annotation tools can be seemingly incorporated into MicrobeAnnotator to generate summary plots.

Dataset Information

Comprehensive human virus screening using high-throughput sequencing with a user-friendly representation of bioinformatics analysis: a pilot study.

Publications

Comprehensive human virus screening using high-throughput sequencing with a user-friendly representation of bioinformatics analysis: a pilot study.

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