Genomic classification using an information-based similarity index: application to the SARS coronavirus.
Measures of genetic distance based on alignment methods are confined to studying sequences that are conserved and identifiable in all organisms under study. A number of alignment-free techniques based on either statistical linguistics or information theory have been developed to overcome the limitations of alignment methods. We present a novel alignment-free approach to measuring the similarity among genetic sequences that incorporates elements from both word rank order-frequency statistics and information theory. We first validate this method on the human influenza A viral genomes as well as on the human mitochondrial DNA database. We then apply the method to study the origin of the SARS coronavirus. We find that the majority of the SARS genome is most closely related to group 1 coronaviruses, with smaller regions of matches to sequences from groups 2 and 3. The information based similarity index provides a new tool to measure the similarity between datasets based on their information content and may have a wide range of applications in the large-scale analysis of genomic databases.
Yang
Albert C-C
AC
Cardiovascular Division and Margret and H.A. Rey Institute for Nonlinear Dynamics in Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts 02215, USA.
Goldberger
Ary L
AL
Peng
C-K
CK
D001483:Base Sequence; D016000:Cluster Analysis; D004272:DNA, Mitochondrial; D030561:Databases, Nucleic Acid; D019143:Evolution, Molecular; D006801:Humans; D009980:Influenza A virus / Q000235:genetics*; D008969:Molecular Sequence Data; D010802:Phylogeny; D045473:Severe acute respiratory syndrome-related coronavirus / Q000145:classification / Q000235:genetics*; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*
D004272:DNA, Mitochondrial