Yasubumi Sakakibara

Stochastic context-free grammars (SCFGs) are applied to the problems of folding, aligning and modeling families of tRNA sequences. SCFGs capture the sequences' common primary and secondary structure and generalize the hidden Markov models (HMMs) used in related work on protein and DNA. Results show that after having been trained on as few as 20 tRNA sequences from only two tRNA subfamilies (mitochondrial and cytoplasmic), the model can discern general tRNA from similar-length RNA sequences of ot

Effective embedding is actively conducted by applying deep learning to biomolecular information. Obtaining better embeddings enhances the quality of downstream analyses, such as DNA sequence motif detection and protein function prediction. In this study, we adopt a pre-training algorithm for the effective embedding of RNA bases to acquire semantically rich representations and apply this algorithm to two fundamental RNA sequence problems: structural alignment and clustering. By using the pre-trai

Long reads obtained from third-generation sequencing platforms can help overcome the long-standing challenge of the de novo assembly of sequences for the genomic analysis of non-model eukaryotic organisms. Numerous long-read-aided de novo assemblies have been published recently, which exhibited superior quality of the assembled genomes in comparison with those achieved using earlier second-generation sequencing technologies. Evaluating assemblies is important in guiding the appropriate choice fo

The assembly of multiple genomes from mixed sequence reads is a bottleneck in metagenomic analysis. A single-genome assembly program (assembler) is not capable of resolving metagenome sequences, so assemblers designed specifically for metagenomics have been developed. MetaVelvet is an extension of the single-genome assembler Velvet. It has been proved to generate assemblies with higher N50 scores and higher quality than single-genome assemblers such as Velvet and SOAPdenovo when applied to metag

The source code of our CNN software in the deep-learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/cnn/, and the dataset used for performance evaluation in this work is available at the same URL.

The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this study, we

Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden in biological sequences. In this article, we give an overview of a series of our grammatical approa

Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we p

We propose the pair HMMs on tree structures (PHMMTSs), which is an extension of PHMMs defined on alignments of trees and provides a unifying framework and an automata-theoretic model for alignments of trees, structural alignments and pair stochastic context-free grammars. By structural alignment, we mean a pairwise alignment to align an unfolded RNA sequence into an RNA sequence of known secondary structure. First, we extend the notion of PHMMs defined on alignments of 'linear' sequences to pair

Bacillus subtilis is the main component in the fermentation of soybeans. To investigate the genetics of the soybean-fermenting B. subtilis strains and its relationship with the productivity of extracellular poly-γ-glutamic acid (γPGA), we sequenced the whole genome of eight B. subtilis stains isolated from non-salted fermented soybean foods in Southeast Asia. Assembled nucleotide sequences were compared with those of a natto (fermented soybean food) starter strain B. subtilis BEST195 and the lab