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.