Relationship of SARS-CoV to other pathogenic RNA viruses explored by tetranucleotide usage profiling

Relationship of SARS-CoV to other pathogenic RNA viruses explored by tetranucleotide usage profiling

Danchin Antoine, ZHANG Xue, Yap Yee

Abstract Background The exact origin of the cause of the Severe Acute Respiratory Syndrome (SARS) is still an open question. The genomic sequence relationship of SARS-CoV with 30 different single-stranded RNA (ssRNA) viruses of various families was studied using two non-standard approaches. Both approaches began with the vectorial profiling of the tetra-nucleotide usage pattern V for each virus. In approach one, a distance measure of a vector V, based on correlation coefficient was devised to construct a relationship tree by the neighbor-joining algorithm. In approach two, a multivariate factor analysis was performed to derive the embedded tetra-nucleotide usage patterns. These patterns were subsequently used to classify the selected viruses. Results Both approaches yielded relationship outcomes that are consistent with the known virus classification. They also indicated that the genome of RNA viruses from the same family conform to a specific pattern of word usage. Based on the correlation of the overall tetra-nucleotide usage patterns, the Transmissible Gastroenteritis Virus (TGV) and the Feline CoronaVirus (FCoV) are closest to SARS-CoV. Surprisingly also, the RNA viruses that do not go through a DNA stage displayed a remarkable discrimination against the CpG and UpA di-nucleotide (z = -77.31, -52.48 respectively) and selection for UpG and CpA (z = 65.79,49.99 respectively). Potential factors influencing these biases are discussed. Conclusion The study of genomic word usage is a powerful method to classify RNA viruses. The congruence of the relationship outcomes with the known classification indicates that there exist phylogenetic signals in the tetra-nucleotide usage patterns, that is most prominent in the replicase open reading frames.

2003

RNA virus, convergent evolution, horizontal gene transfer, Factor analysis, SARS

DOI: 10.1186/1471-2105-4-43

BMC Bioinformatics

BMC

Biology (General), Computer applications to medicine. Medical informatics

EN