The Bioinformatics Core provides data management, communications systems, statistical analysis, and support for functional interpretation of high-throughput data as part of the systems biology approach to immunogenetics. Existing platforms will be leveraged to develop and maintain a sophisticated laboratory information management system (LIMS) to facilitate the tracking of information, the processing of raw data through computational pipelines, and communication between all U19 investigators. In addition, the Core will support the prioritization of key immune genes and pathways derived from expression quantitative trait loci (eQTL) and QTL analyses.
The Specific Aims of the Core are as follows:
Aim 1 : Develop and maintain a dedicated data management system with a Systems Immunogenetics Web Portal. The Core will define and implement a program-wide data communication plan that ensures the capture of all grant-generated sample information, data, and resources. Public access to grant-generated data, resources, and mouse line infomnation will also be provided through the Web portal.
Aim 2; Assist in the development of data processing pipelines that ensure data is high-quality, conforms to technical specifications for modeling, and is accessible to all U19 investigators. In collaboration with the Systems Immunogenetics Core, an existing infrastructure will be leveraged to develop and implement data processing pipelines for all high-throughput profiling data, including quality control protocols for microarray, next-generation sequencing, and proteomics.
Aim 3 : Provide analytical support for high-throughput data and the computational predictions generated by the Research Projects and Cores. Statistical, computational, and functional analysis support will be provided for the interpretation of data and results. In addition, the Core will provide analysis of next-generation sequencing data, including in-depth analysis of noncoding RNAs, and processing and interpretation of targeted proteomics studies allowing for a systems level view of key immune genes and pathways across a select set of mouse lines.

Public Health Relevance

The analysis of genetic trait and gene expression data will provide a deeper understanding of the specific immune responses that contribute to viral disease. Communication of this information to the larger research community will help to foster the development of new antiviral therapies and improved vaccines.

National Institute of Health (NIH)
Research Program--Cooperative Agreements (U19)
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Special Emphasis Panel (ZAI1)
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University of North Carolina Chapel Hill
Chapel Hill
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Ferris, Martin T; Heise, Mark T (2014) Quantitative genetics in the study of virus-induced disease. Adv Virus Res 88:193-225
Menachery, Vineet D; Eisfeld, Amie J; Schäfer, Alexandra et al. (2014) Pathogenic influenza viruses and coronaviruses utilize similar and contrasting approaches to control interferon-stimulated gene responses. MBio 5:e01174-14
Schäfer, Alexandra; Baric, Ralph S; Ferris, Martin T (2014) Systems approaches to Coronavirus pathogenesis. Curr Opin Virol 6:61-9
Menachery, Vineet D; Yount Jr, Boyd L; Josset, Laurence et al. (2014) Attenuation and restoration of severe acute respiratory syndrome coronavirus mutant lacking 2'-o-methyltransferase activity. J Virol 88:4251-64
Menachery, Vineet D; Debbink, Kari; Baric, Ralph S (2014) Coronavirus non-structural protein 16: evasion, attenuation, and possible treatments. Virus Res 194:191-9
Rasmussen, Angela L; Okumura, Atsushi; Ferris, Martin T et al. (2014) Host genetic diversity enables Ebola hemorrhagic fever pathogenesis and resistance. Science 346:987-91
Gibbs, David L; Gralinski, Lisa; Baric, Ralph S et al. (2014) Multi-omic network signatures of disease. Front Genet 4:309
Josset, Laurence; Tchitchek, Nicolas; Gralinski, Lisa E et al. (2014) Annotation of long non-coding RNAs expressed in collaborative cross founder mice in response to respiratory virus infection reveals a new class of interferon-stimulated transcripts. RNA Biol 11:875-90
Scobey, Trevor; Yount, Boyd L; Sims, Amy C et al. (2013) Reverse genetics with a full-length infectious cDNA of the Middle East respiratory syndrome coronavirus. Proc Natl Acad Sci U S A 110:16157-62
Chen, Yaoqing; Rajashankar, Kanagalaghatta R; Yang, Yang et al. (2013) Crystal structure of the receptor-binding domain from newly emerged Middle East respiratory syndrome coronavirus. J Virol 87:10777-83

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