In a paper published online in advance in Nucleic Acids Research this week, investigators in France present "genome-wide evidence for local DNA methylation spreading from small RNA-targeted sequences in Arabidopsis." By re-annotating transposable element sequences in the plant with an eye toward analyzing genome-wide DNA methylation at single-nucleotide resolution, the team found that "although the majority of TE sequences are methylated, [about] 26 percent are not." Further, as "a significant fraction of TE sequences densely methylated … have no or few matching small interfering RNA," they team says that they are "unlikely to be targeted by the RNA-directed DNA methylation machinery." Rather, the researchers present evidence to suggest that the TE sequences "acquire DNA methylation through spreading from adjacent siRNA-targeted regions."
Wolfgang Gerlach and Jens Stoye at Bielefeld University in Germany this week report on a "new method for the taxonomic classification of assembled and unassembled metagenomic sequences that has been adapted to work with both BLAST and HMMER3 homology searches," called CARMA3. Gerlach and Stoye say that CARMA3 makes fewer incorrect taxonomic predictions than other BLAST-based methods at the same sensitivity.
An international team led by investigators at the University of Hong Kong present in Nucleic Acids Research ChIP-Array, a "Web server that integrates ChIP-X and expression data from human, mouse, yeast, fruit fly and Arabidopsis." The authors say that ChIP-Array is useful for detecting direct and indirect target genes regulated by transcription factor of interest as well as for the functional characterization of particular transcription factors.
Researchers at Shanghai Jiao Tong University, along with their collaborators at the US Food and Drug Administration and elsewhere, report online in advance in Nucleic Acids Research on a "server for identifying drug repositioning potential and adverse drug reactions via the chemical-protein interactome," called DRAR-CPI. With a representative collection of drug molecules and targetable human proteins, DRAR-CPI provides users a positive or negative association score for the interaction between his or her molecule and the library drugs. In addition, the team has matched its "predictions of drug-drug associations with those predicted via gene-expression profiles, achieving a matching rate as high as 74 percent," it says.