PMID- 22495748
OWN - NLM
STAT- MEDLINE
DCOM- 20130131
LR  - 20181201
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Linking)
VI  - 28
IP  - 12
DP  - 2012 Jun 15
TI  - Ranking viruses: measures of positional importance within networks define core 
      viruses for rational polyvalent vaccine development.
PG  - 1624-32
LID - 10.1093/bioinformatics/bts181 [doi]
AB  - MOTIVATION: The extraordinary genetic and antigenic variability of RNA viruses is 
      arguably the greatest challenge to the development of broadly effective vaccines. 
      No single viral variant can induce sufficiently broad immunity, and incorporating 
      all known naturally circulating variants into one multivalent vaccine is not 
      feasible. Furthermore, no objective strategies currently exist to select actual 
      viral variants that should be included or excluded in polyvalent vaccines. 
      RESULTS: To address this problem, we demonstrate a method based on graph theory 
      that quantifies the relative importance of viral variants. We demonstrate our 
      method through application to the envelope glycoprotein gene of a particularly 
      diverse RNA virus of pigs: porcine reproductive and respiratory syndrome virus 
      (PRRSV). Using distance matrices derived from sequence nucleotide difference, 
      amino acid difference and evolutionary distance, we constructed viral networks 
      and used common network statistics to assign each sequence an objective ranking 
      of relative 'importance'. To validate our approach, we use an independent 
      published algorithm to score our top-ranked wild-type variants for coverage of 
      putative T-cell epitopes across the 9383 sequences in our dataset. Top-ranked 
      viruses achieve significantly higher coverage than low-ranked viruses, and 
      top-ranked viruses achieve nearly equal coverage as a synthetic mosaic protein 
      constructed in silico from the same set of 9383 sequences. CONCLUSION: Our 
      approach relies on the network structure of PRRSV but applies to any diverse RNA 
      virus because it identifies subsets of viral variants that are most important to 
      overall viral diversity. We suggest that this method, through the objective 
      quantification of variant importance, provides criteria for choosing viral 
      variants for further characterization, diagnostics, surveillance and ultimately 
      polyvalent vaccine development.
FAU - Anderson, Tavis K
AU  - Anderson TK
AD  - Department of Pathobiological Sciences, School of Veterinary Medicine, University 
      of Wisconsin-Madison, Madison, WI 53706, USA.
FAU - Laegreid, William W
AU  - Laegreid WW
FAU - Cerutti, Francesco
AU  - Cerutti F
FAU - Osorio, Fernando A
AU  - Osorio FA
FAU - Nelson, Eric A
AU  - Nelson EA
FAU - Christopher-Hennings, Jane
AU  - Christopher-Hennings J
FAU - Goldberg, Tony L
AU  - Goldberg TL
LA  - eng
PT  - Journal Article
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20120411
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
RN  - 0 (Epitopes, T-Lymphocyte)
RN  - 0 (Viral Envelope Proteins)
SB  - IM
MH  - Algorithms
MH  - *Antigenic Variation
MH  - Computational Biology/methods
MH  - Epitopes, T-Lymphocyte/*immunology
MH  - Porcine respiratory and reproductive syndrome virus/genetics/*immunology
MH  - RNA Viruses/genetics/immunology
MH  - Viral Envelope Proteins/genetics/*immunology
EDAT- 2012/04/13 06:00
MHDA- 2013/02/01 06:00
CRDT- 2012/04/13 06:00
PHST- 2012/04/13 06:00 [entrez]
PHST- 2012/04/13 06:00 [pubmed]
PHST- 2013/02/01 06:00 [medline]
AID - bts181 [pii]
AID - 10.1093/bioinformatics/bts181 [doi]
PST - ppublish
SO  - Bioinformatics. 2012 Jun 15;28(12):1624-32. doi: 10.1093/bioinformatics/bts181. 
      Epub 2012 Apr 11.