Biomedical subjects

Kiyoshi Asai

Publications and source records attributed to Kiyoshi Asai.

11 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↗

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↗

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↗

PubMed2002

Modeling splicing sites with pairwise correlations.

MOTIVATION: A new method for finding subtle patterns in sequences is introduced. It approximates the multiple correlations among residuals with pair-wise correlations, with the learning cost O(m(2)n) where n is the number of training sequences, each of length m. The method suits to model splicing sites in human DNA, which are reported to have higher-order dependencies. RESULTS: By computational experiments, the prediction accuracy of our model was shown to surpass that of previously reported Markov models for the prediction of acceptor sites in human. AVAILABILITY: The C++ source code is available on request from the authors.

Algorithms↗

PubMed2002

Marginalized kernels for RNA sequence data analysis.

We present novel kernels that measure similarity of two RNA sequences, taking account of their secondary structures. Two types of kernels are presented. One is for RNA sequences with known secondary structures, the other for those without known secondary structures. The latter employs stochastic context-free grammar (SCFG) for estimating the secondary structure. We call the latter the marginalized count kernel (MCK). We show computational experiments for MCK using 74 sets of human tRNA sequence data: (i) kernel principal component analysis (PCA) for visualizing tRNA similarities, (ii) supervised classification with support vector machines (SVMs). Both types of experiment show promising results for MCKs.

Computational Biology↗

PubMed2002

Inference of Euler angles for single particle analysis by using Genetic Algorithms.

Single particle analysis is one of the methods for structural studies of protein and macromolecules developed in image analysis on electron microscopy. Reconstructing 3D structure from microscope images is not an easy analysis because of the low 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 is automatically devised. In this paper, we propose a new method for determining Euler angles of projections by applying Genetic Algorithms (i.e., GAs). We empirically show that the proposed approach has improved the previous one in terms of computational time and acquired precision.

Algorithms↗