A classification approach for genotyping viral sequences based on multidimensional scaling and linear discriminant analysis

Background: Accurate classification into genotypes is critical in understanding evolution of divergent viruses. Here we report a new approach, MuLDAS, which classifies a query sequence based on the statistical genotype models learned from the known sequences. Thus, MuLDAS utilizes full spectra of well characterized sequences as references, typically of an order of hundreds, in order to estimate the significance of each genotype assignment. Results: MuLDAS starts by aligning the query sequence to the reference multiple sequence alignment and calculating the subsequent distance matrix among the sequences. They are then mapped to a principal coordinate space by multidimensional scaling, and the coordinates of the reference sequences are used as features in developing linear discriminant models that partition the space by genotype. The genotype of the query is then given as the maximum a posteriori estimate. MuLDAS tests the model confidence by leave-one-out cross-validation and also prov...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Ji Woong Kim, Yongju Ahn, Kichan Lee, Sung-Hee Park, Sangsoo Kim
• Keywords: BMCBI 2010, Genotype, Genotype Assignment, Sequences