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Browsing by Author "Snyder, E. E."

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    PATRIC: The VBI PathoSystems Resource Integration Center
    Snyder, E. E.; Kampanya, N.; Lu, J.; Nordberg, E. K.; Karur, H. R.; Shukla, Maulik; Soneja, J.; Tian, Y.; Xue, T.; Yoo, H.; Zhang, F.; Dharmanolla, C.; Dongre, N. V.; Gillespie, J. J.; Hamelius, J.; Hance, M.; Huntington, K. I.; Jukneliene, D.; Koziski, J.; Mackasmiel, L.; Mane, S. P.; Nguyen, V.; Purkayastha, A.; Shallom, J.; Yu, G.; Guo, Y.; Gabbard, Joseph L.; Hix, D.; Azad, A. F.; Baker, S. C.; Boyle, Stephen M.; Khudyakov, Y.; Meng, Xiang-Jin; Rupprecht, C.; Vinje, J.; Crasta, Oswald R.; Czar, M. J.; Dickerman, Allan W.; Eckart, J. D.; Kenyon, R.; Will, R.; Setubal, Joao C.; Sobral, Bruno (2007-01)
    The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: 'breadth first' beginning with whole-genome and proteome curation using standardized protocols, a 'targeted' approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website (https://patric.vbi.vt.edu) will continually grow with the addition of data, analysis and functionality over the course of the project.
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    Reproducibility, bioinformatic analysis and power of the SAGE method to evaluate changes in transcriptome
    Dinel, S.; Bolduc, C.; Belleau, P.; Boivin, A.; Yoshioka, M.; Calvo, E.; Piedboeuf, B.; Snyder, E. E.; Labrie, F.; St-Amand, J. (2005-01-01)
    The serial analysis of gene expression (SAGE) method is used to study global gene expression in cells or tissues in various experimental conditions. However, its reproducibility has not yet been definitively assessed. In this study, we have evaluated the reproducibility of the SAGE method and identified the factors that affect it. The determination coefficient (R-2 ) for the reproducibility of SAGE is 0.96. However, there are some factors that can affect the reproducibility of SAGE, such as the replication of concatemers and ditags, the number of sequenced tags and double PCR amplification of ditags. Thus, corrections for these factors must be made to ensure the reproducibility and accuracy of SAGE results. A bioinformatic analysis of SAGE data is also presented in order to eliminate these artifacts. Finally, the current study shows that increasing the number of sequenced tags improves the power of the method to detect transcripts and their regulation by experimental conditions.
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    A versatile computational pipeline for bacterial genome annotation improvement and comparative analysis, with Brucella as a use case
    Yu, G. X.; Snyder, E. E.; Boyle, Stephen M.; Crasta, Oswald R.; Czar, M. J.; Mane, S. P.; Purkayastha, A.; Sobral, Bruno; Setubal, Joao C. (2007-06)
    We present a bacterial genome computational analysis pipeline, called GenVar. The pipeline, based on the program GeneWise, is designed to analyze an annotated genome and automatically identify missed gene calls and sequence variants such as genes with disrupted reading frames (split genes) and those with insertions and deletions (indels). For a given genome to be analyzed, GenVar relies on a database containing closely related genomes (such as other species or strains) as well as a few additional reference genomes. GenVar also helps identify gene disruptions probably caused by sequencing errors. We exemplify GenVar's capabilities by presenting results from the analysis of four Brucella genomes. Brucella is an important human pathogen and zoonotic agent. The analysis revealed hundreds of missed gene calls, new split genes and indels, several of which are species specific and hence provide valuable clues to the understanding of the genome basis of Brucella pathogenicity and host specificity.