Taishin Kin

Genome sequencing and analysis of Aspergillus oryzae

The genome of Aspergillus oryzae, a fungus important for the production of traditional fermented foods and beverages in Japan, has been sequenced. The ability to secrete large amounts of proteins and the development of a transformation system have facilitated the use of A. oryzae in modern biotechnology. Although both A. oryzae and Aspergillus flavus belong to the section Flavi of the subgenus Circumdati of Aspergillus, A. oryzae, unlike A. flavus, does not produce aflatoxin, and its long history of use in the food industry has proved its safety. Here we show that the 37-megabase (Mb) genome of A. oryzae contains 12,074 genes and is expanded by 7–9 Mb in comparison with the genomes of Aspergillus nidulans and Aspergillus fumigatus. Comparison of the three aspergilli species revealed the presence of syntenic blocks and A. oryzae-specific blocks (lacking synteny with A. nidulans and A. fumigatus) in a mosaic manner throughout the genome of A. oryzae. The blocks of A. oryzae-specific sequence are enriched for genes involved in metabolism, particularly those for the synthesis of secondary metabolites. Specific expansion of genes for secretory hydrolytic enzymes, amino acid metabolism and amino acid/sugar uptake transporters supports the idea that A. oryzae is an ideal microorganism for fermentation.

Drosophila endogenous small RNAs bind to Argonaute 2 in somatic cells.

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.

fRNAdb: a platform for mining/annotating functional RNA candidates from non-coding RNA sequences

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

A fast structural multiple alignment method for long RNA sequences

BackgroundAligning multiple RNA sequences is essential for analyzing non-coding RNAs. Although many alignment methods for non-coding RNAs, including Sankoffs 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 http://mxscarna.ncrna.org.

Robust prediction of consensus secondary structures using averaged base pairing probability matrices

MOTIVATION Recent transcriptomic studies have revealed the existence of a considerable number of non-protein-coding RNA transcripts in higher eukaryotic cells. To investigate the functional roles of these transcripts, it is of great interest to find conserved secondary structures from multiple alignments on a genomic scale. Since multiple alignments are often created using alignment programs that neglect the special conservation patterns of RNA secondary structures for computational efficiency, alignment failures can cause potential risks of overlooking conserved stem structures. RESULTS We investigated the dependence of the accuracy of secondary structure prediction on the quality of alignments. We compared three algorithms that maximize the expected accuracy of secondary structures as well as other frequently used algorithms. We found that one of our algorithms, called McCaskill-MEA, was more robust against alignment failures than others. The McCaskill-MEA method first computes the base pairing probability matrices for all the sequences in the alignment and then obtains the base pairing probability matrix of the alignment by averaging over these matrices. The consensus secondary structure is predicted from this matrix such that the expected accuracy of the prediction is maximized. We show that the McCaskill-MEA method performs better than other methods, particularly when the alignment quality is low and when the alignment consists of many sequences. Our model has a parameter that controls the sensitivity and specificity of predictions. We discussed the uses of that parameter for multi-step screening procedures to search for conserved secondary structures and for assigning confidence values to the predicted base pairs. AVAILABILITY The C++ source code that implements the McCaskill-MEA algorithm and the test dataset used in this paper are available at http://www.ncrna.org/papers/McCaskillMEA/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Idiographica: a general-purpose web application to build idiograms on-demand for human, mouse and rat

SUMMARY We have launched a web server, which serves as a general-purpose idiogram rendering service, and allows users to generate high-quality idiograms with custom annotation according to their own genome-wide mapping/annotation data through an easy-to-use interface. The generated idiograms are suitable not only for visualizing summaries of genome-wide analysis but also for many types of presentation material including web pages, conference posters, oral presentations, etc. AVAILABILITY Idiographica is freely available at http://www.ncrna.org/idiographica/

Mining frequent stem patterns from unaligned RNA sequences

MOTIVATION In detection of non-coding RNAs, it is often necessary to identify the secondary structure motifs from a set of putative RNA sequences. Most of the existing algorithms aim to provide the best motif or few good motifs, but biologists often need to inspect all the possible motifs thoroughly. RESULTS Our method RNAmine employs a graph theoretic representation of RNA sequences and detects all the possible motifs exhaustively using a graph mining algorithm. The motif detection problem boils down to finding frequently appearing patterns in a set of directed and labeled graphs. In the tasks of common secondary structure prediction and local motif detection from long sequences, our method performed favorably both in accuracy and in efficiency with the state-of-the-art methods such as CMFinder. AVAILABILITY The software is available upon request.

SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments

MOTIVATION The functions of non-coding RNAs are strongly related to their secondary structures, but it is known that a secondary structure prediction of a single sequence is not reliable. Therefore, we have to collect similar RNA sequences with a common secondary structure for the analyses of a new non-coding RNA without knowing the exact secondary structure itself. Therefore, the sequence comparison in searching similar RNAs should consider not only their sequence similarities but also their potential secondary structures. Sankoffs algorithm predicts the common secondary structures of the sequences, but it is computationally too expensive to apply to large-scale analyses. Because we often want to compare a large number of cDNA sequences or to search similar RNAs in the whole genome sequences, much faster algorithms are required. RESULTS We propose a new method of comparing RNA sequences based on the structural alignments of the fixed-length fragments of the stem candidates. The implemented software, SCARNA (Stem Candidate Aligner for RNAs), is fast enough to apply to the long sequences in the large-scale analyses. The accuracy of the alignments is better or comparable with the much slower existing algorithms. AVAILABILITY The web server of SCARNA with graphical structural alignment viewer is available at http://www.scarna.org/.

Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data

In biological data, it is often the case that objects are described in two or more representations. In order to perform classification based on such data, we have to combine them in a certain way. In the context of kernel machines, this task amounts to mix several kernel matrices into one. In this paper, we present two ways to mix kernel matrices, where the mixing weights are optimized to minimize the cross validation error. In bacteria classification and gene function prediction experiments, our methods significantly outperformed single kernel classifiers in most cases.