Principal Component Analysis
Principal Component Analysis (PCA) is a general tool to discern
correlated changes of variables in a multivariate space yielding new
orthogonal vectors which are linear combinations of variables in the
original multivariate space. PCA has been applied to cartesian
coordinate space (31), electrostatic potential space spread over a
molecular frame (32) or sequence space (33). PCA was performed in the
sequence space, following methodology described earlier (33) on aligned
GluRS sequences, aligned using ClustalW (34). A curated sequence
database (GlxRS and tRNAGlx), compiled from complete
bacterial genomes and obtained from KEGG genome database (35) was used
in the present study.