No abstract

RNA silencing is a conserved mechanism in which small RNAs trigger various forms of sequence-specific gene silencing by guiding Argonaute complexes to target RNAs by means of base pairing. RNA silencing is thought to have evolved as a form of nucleic-acid-based immunity to inactivate viruses and transposable elements. Although the activity of transposable elements in animals has been thought largely to be restricted to the germ line, recent studies have shown that they may also actively transpose in somatic cells, creating somatic mosaicism in animals. In the Drosophila germ line, Piwi-interacting RNAs arise from repetitive intergenic elements including retrotransposons by a Dicer-independent pathway and function through the Piwi subfamily of Argonautes to ensure silencing of retrotransposons. Here we show that, in cultured Drosophila S2 cells, Argonaute 2 (AGO2), an AGO subfamily member of Argonautes, associates with endogenous small RNAs of 20-22 nucleotides in length, which we have collectively named endogenous short interfering RNAs (esiRNAs). esiRNAs can be divided into two groups: one that mainly corresponds to a subset of retrotransposons, and the other that arises from stem-loop structures. esiRNAs are produced in a Dicer-2-dependent manner from distinctive genomic loci, are modified at their 3' ends and can direct AGO2 to cleave target RNAs. Mutations in Dicer-2 caused an increase in retrotransposon transcripts. Together, our findings indicate that different types of small RNAs and Argonautes are used to repress retrotransposons in germline and somatic cells in Drosophila.

There are abundance of transcripts that code for no particular protein and that remain functionally uncharacterized. Some of these transcripts may have novel functions while others might be junk transcripts. Unfortunately, the experimental validation of such transcripts to find functional non-coding RNA candidates is very costly. Therefore, our primary interest is to computationally mine candidate functional transcripts from a pool of uncharacterized transcripts. We introduce fRNAdb: a novel database service that hosts a large collection of non-coding transcripts including annotated/non-annotated sequences from the H-inv database, NONCODE and RNAdb. A set of computational analyses have been performed on the included sequences. These analyses include RNA secondary structure motif discovery, EST support evaluation, cis-regulatory element search, protein homology search, etc. fRNAdb provides an efficient interface to help users filter out particular transcripts under their own criteria to sort out functional RNA candidates. fRNAdb is available at

We propose a reasonable way of designing a kernel when objects are generated from latent variable models (e.g., HMM). First of all, a joint kernel is designed for complete data which include both visible and hidden variables. Then a marginalized kernel for visible data is obtained by taking the expectation with respect to hidden variables. We will show that the Fisher kernel is a special case of marginalized kernels, which gives another viewpoint to the Fisher kernel theory. Although our approach can be applied to any object, we particularly derive several marginalized kernels useful for biological sequences (e.g., DNA and proteins). The effectiveness of marginalized kernels is illustrated in the task of classifying bacterial gyrase subunit B (gyrB) amino acid sequences.

BackgroundAligning multiple RNA sequences is essential for analyzing non-coding RNAs. Although many alignment methods for non-coding RNAs, including Sankoff's algorithm for strict structural alignments, have been proposed, they are either inaccurate or computationally too expensive. Faster methods with reasonable accuracies are required for genome-scale analyses.ResultsWe propose a fast algorithm for multiple structural alignments of RNA sequences that is an extension of our pairwise structural alignment method (implemented in SCARNA). The accuracies of the implemented software, MXSCARNA, are at least as favorable as those of state-of-art algorithms that are computationally much more expensive in time and memory.ConclusionThe proposed method for structural alignment of multiple RNA sequences is fast enough for large-scale analyses with accuracies at least comparable to those of existing algorithms. The source code of MXSCARNA and its web server are available at .