Functional involvement of Tudor and dPRMT5 in the piRNA processing pathway in Drosophila germlines

In Drosophila, the PIWI proteins, Aubergine (Aub), AGO3, and Piwi are expressed in germlines and function in silencing transposons by associating with PIWI‐interacting RNAs (piRNAs). Recent studies show that PIWI proteins contain symmetric dimethyl‐arginines (sDMAs) and that dPRMT5/Capsuleen/DART5 is the modifying enzyme. Here, we show that Tudor (Tud), one of Tud domain‐containing proteins, associates with Aub and AGO3, specifically through their sDMA modifications and that these three proteins form heteromeric complexes. piRNA precursor‐like molecules are detected in these complexes. The expression levels of Aub and AGO3, along with their degree of sDMA modification, were not changed by tud mutations. However, the population of transposon‐derived piRNAs associated with Aub and AGO3 was altered by tud mutations, whereas the total amounts of small RNAs on Aub and AGO3 was increased. Loss of dprmt5 did not change the stability of Aub, but impaired its association with Tud and lowered piRNA association with Aub. Thus, in germline cells, piRNAs are quality‐controlled by dPRMT5 that modifies PIWI proteins, in tight association with Tud.     

Bisulfighter: accurate detection of methylated cytosines and differentially methylated regions

Analysis of bisulfite sequencing data usually requires two tasks: to call methylated cytosines (mCs) in a sample, and to detect differentially methylated regions (DMRs) between paired samples. Although numerous tools have been proposed for mC calling, methods for DMR detection have been largely limited. Here, we present Bisulfighter, a new software package for detecting mCs and DMRs from bisulfite sequencing data. Bisulfighter combines the LAST alignment tool for mC calling, and a novel framework for DMR detection based on hidden Markov models (HMMs). Unlike previous attempts that depend on empirical parameters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for each data set. We conduct extensive experiments in which accuracy of mC calling and DMR detection is evaluated on simulated data with various mC contexts, read qualities, sequencing depths and DMR lengths, as well as on real data from a wide range of biological processes. We demonstrate that Bisulfighter consistently achieves better accuracy than other published tools, providing greater sensitivity for mCs with fewer false positives, more precise estimates of mC levels, more exact locations of DMRs and better agreement of DMRs with gene expression and DNase I hypersensitivity. The source code is available at http://epigenome.cbrc.jp/bisulfighter.     

Prediction of Conserved Precursors of miRNAs and Their Mature Forms by Integrating Position-Specific Structural Features

MicroRNA (miRNA) precursor hairpins have a unique secondary structure, nucleotide length, and nucleotide content that are in most cases evolutionarily conserved. The aim of this study was to utilize position-specific features of miRNA hairpins to improve their identification. To this end, we defined the evolutionary and structurally conserved features in each position of miRNA hairpins with heuristically derived values, which were successfully integrated using a probabilistic framework. Our method, miRRim2, can not only accurately detect miRNA hairpins, but infer the location of a mature miRNA sequence. To evaluate the accuracy of miRRim2, we designed a cross validation test in which the whole human genome was used for evaluation. miRRim2 could more accurately detect miRNA hairpins than the other computational predictions that had been performed on the human genome, and detect the position of the 5'-end of mature miRNAs with sensitivity and positive predictive value (PPV) above 0.4. To further evaluate miRRim2 on independent data, we applied it to the Ciona intestinalis genome. Our method detected 47 known miRNA hairpins among top 115 candidates, and pinpointed the 5'-end of mature miRNAs with sensitivity and PPV about 0.4. When our results were compared with deep-sequencing reads of small RNA libraries from Ciona intestinalis cells, we found several candidates in which the predicted mature miRNAs were in good accordance with deep-sequencing results.     

Directed acyclic graph kernels for structural RNA analysis

Background  

Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity.