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Identification of novel viruses using VirusHunter--an automated data analysis pipeline.

ABSTRACT: Quick and accurate identification of microbial pathogens is essential for both diagnosis and response to emerging infectious diseases. The advent of next-generation sequencing technology offers an unprecedented platform for rapid sequencing-based identification of novel viruses. We have developed a customized bioinformatics data analysis pipeline, VirusHunter, for the analysis of Roche/454 and other long read Next generation sequencing platform data. To illustrate the utility of VirusHunter, we performed Roche/454 GS FLX titanium sequencing on two unclassified virus isolates from the World Reference Center for Emerging Viruses and Arboviruses (WRCEVA). VirusHunter identified sequences derived from a novel bunyavirus and a novel reovirus in the two samples respectively. Further sequence analysis demonstrated that the viruses were novel members of the Phlebovirus and Orbivirus genera. Both Phlebovirus and Orbivirus genera include many economic important viruses or serious human pathogens.

SUBMITTER: Zhao G

PROVIDER: S-EPMC3805514 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Identification of novel viruses using VirusHunter--an automated data analysis pipeline.

Zhao Guoyan G Krishnamurthy Siddharth S Cai Zhengqiu Z Popov Vsevolod L VL Travassos da Rosa Amelia P AP Guzman Hilda H Cao Song S Virgin Herbert W HW Tesh Robert B RB Wang David D

PloS one 20131022 10

Quick and accurate identification of microbial pathogens is essential for both diagnosis and response to emerging infectious diseases. The advent of next-generation sequencing technology offers an unprecedented platform for rapid sequencing-based identification of novel viruses. We have developed a customized bioinformatics data analysis pipeline, VirusHunter, for the analysis of Roche/454 and other long read Next generation sequencing platform data. To illustrate the utility of VirusHunter, we ...[more]

PMID: 24167629

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Project description:BACKGROUNDResearch involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification.RESULTHere, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples.CONCLUSIONMetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies.

Project description:Recent advances in human induced pluripotent stem cell (hiPSC)-derived cardiac microtissues provide a unique opportunity for cardiotoxic assessment of pharmaceutical and environmental compounds. Here, we developed a series of automated data processing algorithms to assess changes in action potential (AP) properties for cardiotoxicity testing in 3D engineered cardiac microtissues generated from hiPSC-derived cardiomyocytes (hiPSC-CMs). Purified hiPSC-CMs were mixed with 5-25% human cardiac fibroblasts (hCFs) under scaffold-free conditions and allowed to self-assemble into 3D spherical microtissues in 35-microwell agarose gels. Optical mapping was performed to quantify electrophysiological changes. To increase throughput, AP traces from 4x4 cardiac microtissues were simultaneously acquired with a voltage sensitive dye and a CMOS camera. Individual microtissues showing APs were identified using automated thresholding after Fourier transforming traces. An asymmetric least squares method was used to correct non-uniform background and baseline drift, and the fluorescence was normalized (ΔF/F0). Bilateral filtering was applied to preserve the sharpness of the AP upstroke. AP shape changes under selective ion channel block were characterized using AP metrics including stimulation delay, rise time of AP upstroke, APD30, APD50, APD80, APDmxr (maximum rate change of repolarization), and AP triangulation (APDtri = APDmxr-APD50). We also characterized changes in AP metrics under various ion channel block conditions with multi-class logistic regression and feature extraction using principal component analysis of human AP computer simulations. Simulation results were validated experimentally with selective pharmacological ion channel blockers. In conclusion, this simple and robust automated data analysis pipeline for evaluating key AP metrics provides an excellent in vitro cardiotoxicity testing platform for a wide range of environmental and pharmaceutical compounds.

Dataset Information

Identification of novel viruses using VirusHunter--an automated data analysis pipeline.

Publications

Identification of novel viruses using VirusHunter--an automated data analysis pipeline.

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