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Kiyoshi Asai

Publications and source records attributed to Kiyoshi Asai.

At least 19 recordsLinked to original sources
PubMed2026

LinearCapR: linear-time computation of per-nucleotide structural-context probabilities of RNA without base-pair span limits.

MOTIVATION: RNA molecules adopt dynamic ensembles of secondary structures, where the local structural context of each nucleotide-such as whether it resides in a stem or a specific type of loop-strongly shapes molecular interactions and regulatory function. Structural-context probabilities therefore provide a more functionally informative view of RNA folding than the minimum free energy structures or base-pairing probabilities. However, existing tools either require O(N3) time or employ span-restricted approximations that omit long-range base-pairs, limiting their applicability to large and biologically important RNAs. RESULTS: We introduce LinearCapR, enabling linear-time, span-unrestricted computation of structural-context marginalized probabilities, using beam-pruned Stochastic Context Free Grammar-based computation. LinearCapR retains global ensemble features lost by span-limited methods and yields superior predictive power on bpRNA-1m(90) dataset, especially for multiloops and exterior regions, as well as long-distance stems. LinearCapR supports analysis of long RNAs, demonstrated on the full genome of SARS-CoV-2. LinearCapR provides the first base-pair-span-unrestricted, linear-time framework for RNA structural-context analysis, retaining key thermodynamic ensemble features essential for functional interpretation. It enables large-scale studies of viral genomes, long non-coding RNAs, and downstream analyses such as RNA-binding protein site prediction. AVAILABILITY AND IMPLEMENTATION: The source code of LinearCapR is available at https://github.com/hoget157/LinearCapR. The archived software release used in this work is available at Zenodo: https://doi.org/10.5281/zenodo.19450645.

Nucleic Acid Conformation↗
PubMed2006

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.

Algorithms↗
PubMed2006

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 http://www.ncrna.org/

Base Sequence↗
PubMed2006

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.

Algorithms↗
PubMed2006

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. Sankoff's 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/.

Algorithms↗
PubMed2006

Network-based de-noising improves prediction from microarray data.

BACKGROUND: Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. RESULTS: We devised an extended version of the off-subspace noise-reduction (de-noising) method to incorporate heterogeneous network data such as sequence similarity or protein-protein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data. Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out 12-fold cross-validation for assessment of the prediction performance. We use the Pearson's correlation coefficient between the true and predicted response values as the prediction performance. De-noising improves the prediction performance for 65% of drugs. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data. CONCLUSION: We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer drug responses from microarray data.

Algorithms↗
PubMed2005

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.

Aspartic Acid Endopeptidases↗
PubMed2005

Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus.

Aspergillus fumigatus is exceptional among microorganisms in being both a primary and opportunistic pathogen as well as a major allergen. Its conidia production is prolific, and so human respiratory tract exposure is almost constant. A. fumigatus is isolated from human habitats and vegetable compost heaps. In immunocompromised individuals, the incidence of invasive infection can be as high as 50% and the mortality rate is often about 50% (ref. 2). The interaction of A. fumigatus and other airborne fungi with the immune system is increasingly linked to severe asthma and sinusitis. Although the burden of invasive disease caused by A. fumigatus is substantial, the basic biology of the organism is mostly obscure. Here we show the complete 29.4-megabase genome sequence of the clinical isolate Af293, which consists of eight chromosomes containing 9,926 predicted genes. Microarray analysis revealed temperature-dependent expression of distinct sets of genes, as well as 700 A. fumigatus genes not present or significantly diverged in the closely related sexual species Neosartorya fischeri, many of which may have roles in the pathogenicity phenotype. The Af293 genome sequence provides an unparalleled resource for the future understanding of this remarkable fungus.

Allergens↗
PubMed2005

Sequencing of Aspergillus nidulans and comparative analysis with A. fumigatus and A. oryzae.

The aspergilli comprise a diverse group of filamentous fungi spanning over 200 million years of evolution. Here we report the genome sequence of the model organism Aspergillus nidulans, and a comparative study with Aspergillus fumigatus, a serious human pathogen, and Aspergillus oryzae, used in the production of sake, miso and soy sauce. Our analysis of genome structure provided a quantitative evaluation of forces driving long-term eukaryotic genome evolution. It also led to an experimentally validated model of mating-type locus evolution, suggesting the potential for sexual reproduction in A. fumigatus and A. oryzae. Our analysis of sequence conservation revealed over 5,000 non-coding regions actively conserved across all three species. Within these regions, we identified potential functional elements including a previously uncharacterized TPP riboswitch and motifs suggesting regulation in filamentous fungi by Puf family genes. We further obtained comparative and experimental evidence indicating widespread translational regulation by upstream open reading frames. These results enhance our understanding of these widely studied fungi as well as provide new insight into eukaryotic genome evolution and gene regulation.

Aspergillus fumigatus↗
PubMed2005

Selective integration of multiple biological data for supervised network inference.

MOTIVATION: Inferring networks of proteins from biological data is a central issue of computational biology. Most network inference methods, including Bayesian networks, take unsupervised approaches in which the network is totally unknown in the beginning, and all the edges have to be predicted. A more realistic supervised framework, proposed recently, assumes that a substantial part of the network is known. We propose a new kernel-based method for supervised graph inference based on multiple types of biological datasets such as gene expression, phylogenetic profiles and amino acid sequences. Notably, our method assigns a weight to each type of dataset and thereby selects informative ones. Data selection is useful for reducing data collection costs. For example, when a similar network inference problem must be solved for other organisms, the dataset excluded by our algorithm need not be collected. RESULTS: First, we formulate supervised network inference as a kernel matrix completion problem, where the inference of edges boils down to estimation of missing entries of a kernel matrix. Then, an expectation-maximization algorithm is proposed to simultaneously infer the missing entries of the kernel matrix and the weights of multiple datasets. By introducing the weights, we can integrate multiple datasets selectively and thereby exclude irrelevant and noisy datasets. Our approach is favorably tested in two biological networks: a metabolic network and a protein interaction network. AVAILABILITY: Software is available on request.

Algorithms↗
PubMed2005

Accurate extraction of functional associations between proteins based on common interaction partners and common domains.

MOTIVATION: Genomic and proteomic approaches have accumulated a huge amount of data which provide clues to protein function. However, interpreting single omic data for predicting uncharacterized protein functions has been a challenging task, because the data contain a lot of false positives. To overcome this problem, methods for integrating data from various omic approaches are needed for more accurate function prediction. RESULT: In this paper, we have developed a method which extracts functionally similar proteins with high confidence by integrating protein-protein interaction data and domain information. We used this method to analyze publicly available data from Saccharomyces cerevisiae. We identified 1042 functional associations, involving 765 proteins of which 98 (12.8%) had no previously ascribed function. Our method extracts functionally similar protein pairs more accurately than conventional methods, and predicting function for previously uncharacterized proteins can be achieved. Our method can of course be applied to protein-protein interaction data for any species.

Algorithms↗
PubMed2005

Statistical evaluation of a bottom-up clustering for single particle molecular images.

We examined the statistical performance of clustering single particle molecular images by bottom-up clustering, a hierarchical algorithm, using simulated protein images with a low signal-to-noise ratio. Using covariance for the measure of similarity together with the iterative alignment, our method was found to be fairly robust against noise. Clustering tests of four known protein structures were performed at three levels of noise and with three levels of smoothing. A significant effect of smoothing was confirmed in our results for images with noise suggesting an effective degree of smoothing depending on the noise and structural features of the target molecule. The consistency of clustering results was evaluated by the average solid angle of projection, and the precision of our clustering results was checked by the average image correlation between the obtained cluster image and the true projection. Once image features are extracted appropriately, the average solid angle also represents the degree of clustering precision.

Algorithms↗
PubMed2004

Extracting relations between promoter sequences and their strengths from microarray data.

MOTIVATION: The relations between the promoter sequences and their strengths were extensively studied in the 1980s. Although these studies uncovered strong sequence-strength correlations, the cost of their elaborate experimental methods have been too high to be applied to a large number of promoters. On the contrary, a recent increase in the microarray data allows us to compare thousands of gene expressions with their DNA sequences. RESULTS: We studied the relations between the promoter sequences and their strengths using the Escherichia coli microarray data. We modeled those relations using a simple weight matrix, which was optimized with a novel support vector regression method. It was observed that several non-consensus bases in the '-35' and '-10' regions of promoter sequences act positively on the promoter strength and that certain consensus bases have a minor effect on the strength. We analyzed outliers for which the observed gene expressions deviate from the promoter strength predictions, and identified several genes with enhanced expressions due to multiple promoters and genes under strong regulation by transcription factors. Our method is applicable to other procaryotes for which both the promoter sequences and the microarray data are available.

Algorithms↗
PubMed2003

Inference of Euler angles for single-particle analysis by means of evolutionary algorithms.

Single-particle analysis is one of the methods for structural studies of protein and macromolecules; it requires advanced image analysis of electron micrographics. Reconstructing three-dimensional (3D) structure from microscope images is not an easy analysis because of the low image resolution of images and lack of the directional information of images in 3D structure. To improve the resolution, different projections are aligned, classified, and averaged. Inferring the orientations of these images is so difficult that the task of reconstructing 3D structures depends upon the experience of researchers. But recently, a method to reconstruct 3D structures was automatically devised. In this paper, we propose a new method for determining Euler angles of projections by applying genetic algorithms. We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.

Algorithms↗
PubMed2003

Processing sequence annotation data using the Lua programming language.

The data processing language in a graphical software tool that manages sequence annotation data from genome databases should provide flexible functions for the tasks in molecular biology research. Among currently available languages we adopted the Lua programming language. It fulfills our requirements to perform computational tasks for sequence map layouts, i.e. the handling of data containers, symbolic reference to data, and a simple programming syntax. Upon importing a foreign file, the original data are first decomposed in the Lua language while maintaining the original data schema. The converted data are parsed by the Lua interpreter and the contents are stored in our data warehouse. Then, portions of annotations are selected and arranged into our catalog format to be depicted on the sequence map. Our sequence visualization program was successfully implemented, embedding the Lua language for processing of annotation data and layout script. The program is available at http://staff.aist.go.jp/yutaka.ueno/guppy/.

Computational Biology↗
PubMed2002

MOSBY: a molecular structure viewer program with portability and extensibility.

A molecular structure viewer program, MOSBY has been developed for studies that use atomic coordinates to understand the structures of protein molecules. The program is designed to be portable with a comprehensive user interface by our high-throughput graphics library. In addition, it cooperates with extension modules customized for individual research topics and analysis. For example, an electron density module loads and displays electron density maps derived in X-ray crystallographic analysis superimposed to an atomic model. A molecular dynamics module reads a trajectory file of the results of molecular dynamics calculations and animates the structure. These plug-in modules are devised to function without modification to the MOSBY program. For variations of analysis and calculations with atomic coordinates, the portability and extensibility illustrated by MOSBY play an important rule in scientific computational tools with active software development.

Computer Graphics↗
PubMed2002

Marginalized kernels for biological sequences.

MOTIVATION: Kernel methods such as support vector machines require a kernel function between objects to be defined a priori. Several works have been done to derive kernels from probability distributions, e.g., the Fisher kernel. However, a general methodology to design a kernel is not fully developed. RESULTS: 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.

Algorithms↗