Identification of small RNA pathway genes using patterns of phylogenetic conservation and divergence.

Genetic and biochemical analyses of RNA interference (RNAi) and microRNA (miRNA) pathways have revealed proteins such as Argonaute and Dicer as essential cofactors that process and present small RNAs to their targets. Well-validated small RNA pathway cofactors such as these show distinctive patterns of conservation or divergence in particular animal, plant, fungal and protist species. We compared 86 divergent eukaryotic genome sequences to discern sets of proteins that show similar phylogenetic profiles with known small RNA cofactors. A large set of additional candidate small RNA cofactors have emerged from functional genomic screens for defects in miRNA- or short interfering RNA (siRNA)-mediated repression in Caenorhabditis elegans and Drosophila melanogaster, and from proteomic analyses of proteins co-purifying with validated small RNA pathway proteins. The phylogenetic profiles of many of these candidate small RNA pathway proteins are similar to those of known small RNA cofactor proteins. We used a Bayesian approach to integrate the phylogenetic profile analysis with predictions from diverse transcriptional coregulation and proteome interaction data sets to assign a probability for each protein for a role in a small RNA pathway. Testing high-confidence candidates from this analysis for defects in RNAi silencing, we found that about one-half of the predicted small RNA cofactors are required for RNAi silencing. Many of the newly identified small RNA pathway proteins are orthologues of proteins implicated in RNA splicing. In support of a deep connection between the mechanism of RNA splicing and small-RNA-mediated gene silencing, the presence of the Argonaute proteins and other small RNA components in the many species analysed strongly correlates with the number of introns in those species.

A systematic assessment of automated ribosomal intergenic spacer analysis (ARISA) as a tool for estimating bacterial richness.

ARISA (automated ribosomal intergenic spacer analysis) is a commonly used method for microbial community analysis that provides estimates of microbial richness and diversity. Here we investigated the potential biases of ARISA in richness estimation by performing computer simulations using 722 complete genomes. Our simulations based on in silico PCR demonstrated that over 8% of bacterial strains represented by complete genomes will never yield a PCR fragment using ARISA primers, usually because their ribosomal RNA genes are not organized in an operon. Despite the tendency of ARISA to overestimate species richness, a strong linear correlation exists between the observed number of fragments, even after binning, and the actual number of species in the sample. This linearity is fairly robust to the taxon sampling in the database as it is also observed on subsets of the 722 genome database using a jackknife approach. However, this linearity disappears when the species richness is high and binned fragment lengths gradually become saturated. We suggest that for ARISA-based richness estimates, where the number of binned lengths observed ranges between 10 and 116, a correction should be used in order to obtain more accurate "species richness" results comparable to 16S rRNA clone-library data.