Metadata-driven comparative analysis tool for sequences (meta-CATS): An automated process for identifying significant sequence variations that correlate with virus attributes
Author(s)
Pickett, B. E.; Liu, M.; Sadat, E. L.; Squires, R. B.; Noronha, J. M.; He, S.; Jen, W.; Zaremba, S.; Gu, Z.; Zhou, L.; Larsen, C. N.; McGee, M.; Klem, E. B.; Scheuermann, R. H.; Bosch, Irene; Gehrke, Lee; ... Show more Show less
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The Virus Pathogen Resource (ViPR; www.viprbrc.org) and Influenza Research Database (IRD; www.fludb.org) have developed a metadata-driven Comparative Analysis Tool for Sequences (meta-CATS), which performs statistical comparative analyses of nucleotide and amino acid sequence data to identify correlations between sequence variations and virus attributes (metadata). Meta-CATS guides users through: selecting a set of nucleotide or protein sequences; dividing them into multiple groups based on any associated metadata attribute (e.g. isolation location, host species); performing a statistical test at each aligned position; and identifying all residues that significantly differ between the groups. As proofs of concept, we have used meta-CATS to identify sequence biomarkers associated with dengue viruses isolated from different hemispheres, and to identify variations in the NS1 protein that are unique to each of the 4 dengue serotypes. Meta-CATS is made freely available to virology researchers to identify genotype-phenotype correlations for development of improved vaccines, diagnostics, and therapeutics.
Date issued
2013-12Department
Massachusetts Institute of Technology. Institute for Medical Engineering & ScienceJournal
Virology
Publisher
Elsevier
Citation
Pickett, B.E., M. Liu, E.L. Sadat, R.B. Squires, J.M. Noronha, S. He, W. Jen, et al. “Metadata-Driven Comparative Analysis Tool for Sequences (meta-CATS): An Automated Process for Identifying Significant Sequence Variations That Correlate with Virus Attributes.” Virology 447, no. 1–2 (December 2013): 45–51. © 2013 Elsevier Inc.
Version: Final published version
ISSN
00426822
1096-0341