BACKGROUND: Prokaryotic 16S ribosomal RNA (rRNA) sequences are widely used in environmental microbiology and molecular evolution as reliable markers for the taxonomic classification and phylogenetic analysis of microbes. Restricted by current sequencing techniques, the massive sequencing of 16S rRNA gene amplicons encompassing the full length of genes is not yet feasible. Thus, the selection of the most efficient hypervariable regions for phylogenetic analysis and taxonomic classification is still debated. In the present study, several bioinformatics tools were integrated to build an in silico pipeline to evaluate the phylogenetic sensitivity of the hypervariable regions compared with the corresponding full-length sequences. RESULTS: The correlation of seven sub-regions was inferred from the geodesic distance, a parameter that is applied to quantitatively compare the topology of different phylogenetic trees constructed using the sequences from different sub-regions. The relationship between different sub-regions based on the geodesic distance indicated that V4-V6 were the most reliable regions for representing the full-length 16S rRNA sequences in the phylogenetic analysis of most bacterial phyla, while V2 and V8 were the least reliable regions. CONCLUSIONS: Our results suggest that V4-V6 might be optimal sub-regions for the design of universal primers with superior phylogenetic resolution for bacterial phyla. A potential relationship between function and the evolution of 16S rRNA is also discussed.

MOTIVATION: Recently, more stringent criteria for CpG islands have been introduced to exclude Alu repeats, thereby enabling a higher proportion of CpG islands associating with genes to be identified. Using these new criteria, several types of associations between CpG islands and genes were investigated to further establish the importance of CpG islands as gene markers. RESULTS: The CpG islands were searched by CpGIE, a java software program developed for CpG island identification. CpGIE was advanced in identification accuracy compared with other tools. According to our results, about 70% of the identified CpG islands were associating with the human genes and over half of them are in the promoters. Furthermore, the investigation of genes in the confirmed gene model showed that 56% of them had a CpG island overlapping the transcription start sites. In comparison, the new criteria were found capable of filtering a large fraction of Alu repeats that was identified as CpG islands by the generally accepted criteria within the genes, but very few CpG islands associating with the promoters were affected. The genes in the predicted gene model were not obviously associated with CpG islands, suggesting that CpG islands can be used to evaluate the accuracy of gene annotation. AVAILABILITY: http://bioinfo.hku.hk/cpgieintro

MOTIVATION: It has been speculated that CpG dinucleotide deficiency in genomes is a consequence of DNA methylation. However, this hypothesis does not adequately explain CpG deficiency in bacteria. The hypothesis based on DNA structure constraint as an alternative explanation was therefore examined. RESULTS: By comparing real bacterial genomes and Markov artificial genomes in the second order, we found that the core structure of a restricted pattern, the TTCGAA pattern, was under represented in low GC content bacterial genomes regardless of CpG dinucleotide level. This is in contrast to the AACGTT pattern, indicating that the counterselection is context-dependent. Further study discovered nine underrepresented patterns that were supposed to be capable of inducing DNA structure constraint. In summary, most of them are in TTCGNA and TTCGAN patterns in both DNA strands. An explanation is also proposed for the strong correlation between GC content and CpG deficiency. The result of random sequence simulation showed that the occurrences of these patterns were correlated with GC content, as well as the percentage of CpG dinucleotides being trapped in these patterns. Finally, we suggest that the degree of counter-selection against these restricted patterns could be influenced by global GC content of a genome.