The influenza virus is endemic in the human population and causes significant annual morbidity and mortality. One of the primary mechanisms for generating such antigenically novel influenza strains is re- assortment, in which viral segments from two distinct strains combine. Reassortment occurs frequently among human and avian isolates and is an important feature of the evolution of virus. The recent explosion in available influenza sequence data has made it a pressing need to be able to computationally detect reassortments quickly and accurately. By doing so, we will be able identify new, potentially harmful strains quickly. We will also gain a better understanding of how reassortment occurs and why certain reassortments are more evolutionarily successful than others. We propose to (Aim 1) validate and improve a new computational approach for the accurate detection of influenza reassortments. The method takes into account uncertainty in the estimated evolutionary histories of the influenza segments by comparing two distributions of phylogenetic trees, rather than a pair of possibly unreliable or uninformative consensus trees. The proposed method permits the assignment of a confidence score to each reassortment event, something that is not possible with other approaches. We propose to validate the method on collections of human and avian genomes and also on extensive simulated data. In order to further improve the methods accuracy, we propose several extensions based on novel statistical methods that assess the changes in evolutionary distances between isolates. One outcome will be a stand-alone software package. We also propose to (Aim 2) computationally construct a large catalog of reassortments involving the sequenced isolates and to use this catalog to estimate the frequency and characteristics of reassortments. In particular, we will look for sequence mutations that tend to occur contemporaneously with reassortments. By more accurately detecting these reassortment events, we will gain a better understanding of influenza evolution. This will help plan vaccination strategies and design effective surveillance protocols.

Public Health Relevance

We propose to study new computational methods for predicting reassortments, a key event in the evolution of influenza virus, an important human pathogen. By more accurately detecting these reassortment events, we will gain a better understanding of influenza evolution. This will help plan vaccination strategies and design effective surveillance protocols.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21AI085376-01
Application #
7772829
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Hauguel, Teresa M
Project Start
2010-06-01
Project End
2012-05-31
Budget Start
2010-06-01
Budget End
2011-05-31
Support Year
1
Fiscal Year
2010
Total Cost
$185,500
Indirect Cost
Name
University of Maryland College Park
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
790934285
City
College Park
State
MD
Country
United States
Zip Code
20742
Patro, Rob; Norel, Raquel; Prill, Robert J et al. (2016) A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin. BMC Bioinformatics 17:155
Patro, Rob; Mount, Stephen M; Kingsford, Carl (2014) Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol 32:462-4
Duggal, Geet; Wang, Hao; Kingsford, Carl (2014) Higher-order chromatin domains link eQTLs with the expression of far-away genes. Nucleic Acids Res 42:87-96
Patro, Rob; Kingsford, Carl (2013) Predicting protein interactions via parsimonious network history inference. Bioinformatics 29:i237-46
Patro, Rob; Sefer, Emre; Malin, Justin et al. (2012) Parsimonious reconstruction of network evolution. Algorithms Mol Biol 7:25
Duggal, Geet; Kingsford, Carl (2012) Graph rigidity reveals well-constrained regions of chromosome conformation embeddings. BMC Bioinformatics 13:241
Filippova, Darya; Gadani, Aashish; Kingsford, Carl (2012) Coral: an integrated suite of visualizations for comparing clusterings. BMC Bioinformatics 13:276
Patro, Rob; Kingsford, Carl (2012) Global network alignment using multiscale spectral signatures. Bioinformatics 28:3105-14
Nagarajan, Niranjan; Kingsford, Carl (2011) GiRaF: robust, computational identification of influenza reassortments via graph mining. Nucleic Acids Res 39:e34
Marcais, Guillaume; Kingsford, Carl (2011) A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27:764-70

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