Biology subjects

Fukunaga, T.

Publications and source records attributed to Fukunaga, T..

16 recordsLinked to original sources

bioRxiv2026

BWR-finder: BWT-based de novo interspersed repeat detection for gigantic genomes

We developed BWR-finder (Burrows-Wheeler transform-based Repeat finder), a new software tool for database-free detection of interspersed repeats in large genomes of tens of gigabases. BWR-finder employs a BWT-based seed-and-extend repeat detection algorithm and parallelized extension computation, improving both runtime and memory usage. In benchmarks using the rice and human genomes, BWR-finder reduced runtime compared with RepeatModeler2, HiTE, and REPrise, and reduced memory usage compared with HiTE and REPrise, while maintaining high nucleotide-level repeat detection sensitivity. BWR-finder also enabled whole-genome repeat detection in genomes larger than 10 Gb and identified candidate repeat regions and repeat consensus sequences in the 20.3-Gb Pleurodeles waltl genome that were not associated with existing repeat annotations or libraries.

bioinformatics↗

bioRxiv2025

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

MotivationRNA 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. ResultsWe 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. ConclusionsLinearCapR 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. AvailabilityThe source code of LinearCapR is available at https://github.com/hoget157/LinearCapR.

bioinformatics↗

bioRxiv2025

MitoNGS: an online platform to analyze fish metabarcoding data in high-resolution

Environmental DNA (eDNA) metabarcoding has become a powerful tool for assessing fish biodiversity in aquatic ecosystems. However, accurate species-level identification remains challenging due to incomplete and contaminated reference databases, as well as ambiguous taxa sharing identical barcode sequences. Here, we present MitoNGS, a next-generation platform that succeeds the widely used MiFish pipeline, designed for high-resolution analysis of fish metabarcoding data. MitoNGS addresses these challenges by incorporating more comprehensive references including non-fish species and detailed annotations of heterospecific regions. Additionally, it introduces the "species group" strategy in conjunction with environmental habitat and geographic occurrence data to resolve ambiguous taxa. Furthermore, MitoNGS expands the functionalities of the legacy MiFish pipeline. It can analyze data from any mitochondrial markers and from Nanopore sequencing platforms. MitoNGS demonstrated excellent performance on our testing datasets from diverse locations, markers and sequencing platforms. MitoNGS offers a user-friendly, web-based solution for fish detection, biodiversity monitoring, conservation research, and bioresource management. MitoNGS is freely available via https://mitofish.aori.u-tokyo.ac.jp/mito-ngs.

bioinformatics↗

bioRxiv2024

CoLaML: Inferring latent evolutionary modes from heterogeneous gene content

MotivationEstimating the history of gene content evolution provides insights into genome evolution on a macroevolutionary timescale. Previous models did not consider heterogeneity in evolutionary patterns among gene families across different periods and/or clades. ResultsWe introduce CoLaML (joint inference of gene COntent evolution and its LA-tent modes using Maximum Likelihood), which considers heterogeneity using a Markov-modulated Markov chain. This model assumes that internal states determine evolutionary patterns (i.e., latent evolutionary modes) and attributes heterogeneity to their switchover during the evolutionary timeline. We developed a practical algorithm for model inference and validated its performance through simulations. CoLaML outperformed previous models in fitting empirical datasets and estimated plausible evolutionary histories, capturing heterogeneity among clades and gene families without prior knowledge. AvailabilityCoLaML is freely available at https://github.com/mtnouchi/colaml. Contactyamanouchi@bs.s.u-tokyo.ac.jp

bioinformatics↗

bioRxiv2024

Protective effects of PDGF-AB/BB against cellular senescence in human intervertebral disc

Cellular senescence, characterized by a permanent state of cell cycle arrest and a secretory phenotype contributing to inflammation and tissue deterioration, has emerged as a target for age-related interventions. Accumulation of senescent cells is closely linked with intervertebral disc (IVD) degeneration, a prevalent age-dependent chronic disorder causing low back pain. Previous studies have highlighted that platelet-derived growth factor (PDGF) mitigated IVD degeneration through anti-apoptosis, anti-inflammation, and pro-anabolism. However, its impact on IVD cell senescence remains elusive. In this study, human NP and AF cells derived from aged, degenerated IVDs were treated with recombinant human (rh) PDGF-AB/BB for 5 days and changes of transcriptome profiling were examined through mRNA sequencing. NP and AF cells demonstrated similar but distinct responses to the treatment. However, the effects of PDGF-AB and BB on human IVD cells were comparable. Specifically, PDGF-AB/BB treatment resulted in downregulation of gene clusters related to neurogenesis and response to mechanical stimulus in AF cells while the downregulated genes in NP cells were mainly associated with metabolic pathways. In both NP and AF cells, PDGF-AB and BB treatment upregulated the expression of genes involved in cell cycle regulation, mesenchymal cell differentiation, and response to reduced oxygen levels, while downregulating the expression of genes related to senescence associated phenotype, including oxidative stress, reactive oxygen species (ROS), and mitochondria dysfunction. Network analysis revealed that PDGFRA and IL6 were the top hub genes in treated NP cells. Furthermore, in irradiation-induced senescent NP cells, PDGFRA gene expression was significantly reduced compared to non-irradiated cells. However, rhPDGF-AB/BB treatment increased PDGFRA expression and mitigated the senescence progression through increased cell population in the S phase, reduced SA-{beta}-Gal activity, and decreased expression of senescence related regulators including P21, P16, IL6, and NF-{kappa}B. Our findings reveal a novel anti-senescence role of PDGF in the IVD, making it a promising potential candidate to delay aging-induced IVD degeneration. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/617862v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1f151d0org.highwire.dtl.DTLVardef@324747org.highwire.dtl.DTLVardef@e58c92org.highwire.dtl.DTLVardef@6fe26c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

REPrise: de novo interspersed repeat detection using inexact seeding

MotivationInterspersed repeats occupy a large part of many eukaryotic genomes, and thus their accurate annotation is essential for various genome analyses. Database-free de novo repeat detection approaches are powerful for annotating genomes that lack well-curated repeat databases. However, existing tools do not yet have sufficient repeat detection performance. ResultsIn this study, we developed REPrise, a de novo interspersed repeat detection software program based on a seed-and-extension method. Although the algorithm of REPrise is similar to that of RepeatScout, which is currently the de facto standard tool, we incorporated three unique techniques into REPrise: inexact seeding, affine gap scoring and loose masking. Analyses of rice and simulation genome datasets showed that REPrise outperformed RepeatScout in terms of sensitivity, especially when the repeat sequences contained many mutations. Furthermore, when applied to the complete human genome dataset T2T-CHM13, REPrise demonstrated the potential to detect novel repeat sequence families. AvailabilityThe source code of REPrise is freely available at https://github.com/hmdlab/REPrise. Repeat annotations predicted for the T2T genome using REPrise are also available at https://waseda.box.com/v/REPrise-data. Contactfukunaga@aoni.waseda.jp and mhamada@waseda.jp

bioinformatics↗

bioRxiv2023

DeepRaccess: High-speed RNA accessibility prediction using deep learning

RNA accessibility is a useful RNA secondary structural feature for predicting RNA-RNA interactions and translation efficiency in prokaryotes. However, conventional accessibility calculation tools, such as Raccess, are computationally expensive and require considerable computational time to perform transcriptome-scale analyses. In this study, we developed DeepRaccess, which predicts RNA accessibility based on deep learning methods. DeepRaccess was trained to take artificial RNA sequences as input and to predict the accessibility of these sequences as calculated by Raccess. Simulation and empirical dataset analyses showed that the accessibility predicted by DeepRaccess was highly correlated with the accessibility calculated by Raccess. In addition, we confirmed that DeepRaccess can predict protein abundance in E.coli with moderate accuracy from the sequences around the start codon. We also demonstrated that DeepRaccess achieved tens to hundreds of times software speed-up in a GPU environment. The source codes and the trained models of DeepRaccess are freely available at https://github.com/hmdlab/DeepRaccess.

bioinformatics↗

bioRxiv2022

LinAliFold and CentroidLinAliFold: Fast RNA consensus secondary structure prediction for aligned sequences using beam search methods

RNA consensus secondary structure prediction from aligned sequences is a powerful approach for improving the secondary structure prediction accuracy. However, because the computational complexities of conventional prediction tools scale with the cube of the alignment lengths, their application to long RNA sequences, such as viral RNAs or long non-coding RNAs, requires significant computational time. In this study, we developed LinAliFold and CentroidLinAliFold, fast RNA consensus secondary structure prediction tools based on minimum free energy and maximum expected accuracy principles, respectively. We achieved software acceleration using beam search methods that were successfully used for fast secondary structure prediction from a single RNA sequence. Benchmark analyses showed that LinAliFold and CentroidLinAliFold were much faster than the existing methods while preserving the prediction accuracy. As an empirical application, we predicted the consensus secondary structure of coronaviruses with approximately 30,000 nt in 5 and 76 minutes by LinAliFold and CentroidLinAliFold, respectively. We confirmed that the predicted consensus secondary structure of coronaviruses was consistent with the experimental results. The source code is freely available at https://github.com/fukunagatsu/LinAliFold-CentroidLinAliFold.

bioinformatics↗

bioRxiv2021

Inverse Potts model improves accuracy of phylogenetic profiling

Phylogenetic profiling is a powerful computational method for revealing the functions of function-unknown genes. Although conventional similarity evaluation measures in phylogenetic profiling showed high prediction accuracy, they have two estimation biases: an evolutionary bias and a spurious correlation bias. Existing studies have focused on the evolutionary bias, but the spurious correlation bias has not been analyzed. To eliminate the spurious correlation bias, we applied an evaluation measure based on the inverse Potts model (IPM) to phylogenetic profiling. We also proposed an evaluation measure to remove both the evolutionary and spurious correlation biases using the IPM. In an empirical dataset analysis, we demonstrated that these IPM-based evaluation measures improved the prediction performance of phylogenetic profiling. In addition, we found that the integration of several evaluation measures, including the IPM-based evaluation measures, had superior performance to a single evaluation measure. The source code is freely available at https://github.com/fukunagatsu/Ipm.

bioinformatics↗

bioRxiv2021

Representation learning applications in biological sequence analysis

Remarkable advances in high-throughput sequencing have resulted in rapid data accumulation, and analyzing biological (DNA/RNA/protein) sequences to discover new insights in biology has become more critical and challenging. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention, because biological sequences are regarded as sentences and k-mers in these sequences as words. Embedding is an essential step in NLP, which converts words into vectors. This transformation is called representation learning and can be applied to biological sequences. Vectorized biological sequences can be used for function and structure estimation, or as inputs for other probabilistic models. Given the importance and growing trend in the application of representation learning in biology, here, we review the existing knowledge in representation learning for biological sequence analysis.

bioinformatics↗

bioRxiv2021

Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model

MotivationAccumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka-Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. ResultsIn this study, we developed unsupervised learning based microbial interaction inference method using Bayesian estimation (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota. AvailabilityThe C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato Contactshion_hosoda@asagi.waseda.jp, mhamada@waseda.jp

microbiology↗

bioRxiv2020

Mirage: A phylogenetic mixture model to reconstruct gene-content evolutionary history using a realistic evolutionary rate model

Reconstruction of gene-content evolutionary history is an essential approach for understanding how complex biological systems have been organized. However, the existing gene-content evolutionary models cannot formulate complex and heterogeneous gene gain/loss processes, which reflect diverse evolutionary events and greatly depend on gene families. In this study, we developed Mirage (MIxture model with a Realistic evolutionary rate model for Ancestral Genome Estimation), which allows different gene families to have flexible gene gain/loss rates, but reasonably limits the number of parameters to be estimated by the expectation-maximization algorithm. Simulation analysis showed that Mirage can accurately estimate complex and heterogeneous gene gain/loss rates and reconstruct gene-content evolutionary history. Application to empirical datasets demonstrated that our evolutionary model better fits genome data from various taxonomic groups than other models. Using Mirage, we revealed that gene families of metabolic function-related gene families displayed frequent gene gains and losses in all taxa investigated. The source code of Mirage is freely available at https://github.com/fukunagatsu/Mirage.

bioinformatics↗

bioRxiv2020

Novel metric for hyperbolic phylogenetic tree embeddings

Advances in experimental technologies such as DNA sequencing have opened up new avenues for the applications of phylogenetic methods to various fields beyond their traditional application in evolutionary investigations, extending to the fields of development, differentiation, cancer genomics, and immunogenomics. Thus, the importance of phylogenetic methods is increasingly being recognized, and the development of a novel phylogenetic approach can contribute to several areas of research. Recently, the use of hyperbolic geometry has attracted attention in artificial intelligence research. Hyperbolic space can better represent a hierarchical structure compared to Euclidean space, and can therefore be useful for describing and analyzing a phylogenetic tree. In this study, we developed a novel metric that considers the characteristics of a phylogenetic tree for representation in hyperbolic space. We compared the performance of the proposed hyperbolic embeddings, general hyperbolic embeddings, and Euclidean embeddings, and confirmed that our method could be used to more precisely reconstruct evolutionary distance. We also demonstrate that our approach is useful for predicting the nearest-neighbor node in a partial phylogenetic tree with missing nodes. This study highlights the utility of adopting a geometric approach for further advancing the applications of phylogenetic methods. The demo code is attached as a supplementary file in a compiled jupyter notebook. The code used for analyses is available on GitHub at https://github.com/hmatsu1226/HyPhyTree.

bioinformatics↗

bioRxiv2020

MotiMul: A significant discriminative sequence motif discovery algorithm with multiple testing correction

Sequence motifs play essential roles in intermolecular interactions such as DNA-protein interactions. The discovery of novel sequence motifs is therefore crucial for revealing gene functions. Various bioinformatics tools have been developed for finding sequence motifs, but until now there has been no software based on statistical hypothesis testing with statistically sound multiple testing correction. Existing software therefore could not control for the type-1 error rates. This is because, in the sequence motif discovery problem, conventional multiple testing correction methods produce very low statistical power due to overly-strict correction. We developed MotiMul, which comprehensively finds significant sequence motifs using statistically sound multiple testing correction. Our key idea is the application of Tarones correction, which improves the statistical power of the hypothesis test by ignoring hypotheses that never become statistically significant. For the efficient enumeration of the significant sequence motifs, we integrated a variant of the PrefixSpan algorithm with Tarones correction. Simulation and empirical dataset analysis showed that MotiMul is a powerful method for finding biologically meaningful sequence motifs. The source code of MotiMul is freely available at https://github.com/ko-ichimo-ri/MotiMul.

bioinformatics↗

bioRxiv2019

Revealing microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation

Recent research has revealed that there are various microbial species in the human gut microbiome. To clarify the structure of the human gut microbiome, many data mining methods have been applied to microbial composition data. Cluster analysis, one of the key data mining methods that have been used in human gut microbiome research, can classify the human gut microbiome into three clusters, called enterotypes. The human gut microbiome has been suggested to be composed of the microbial assemblages or groups of co-occurring microbes, and one human gut microbiome can contain several microbial assemblages. However, cluster analysis can cluster samples into groups without capturing minor assemblages. In addition, a reliable method of assemblage detection has not been established, and little is known about the distributions of microbial assemblages at a population-level scale. Accordingly, the purpose of this study was to clarify the microbial assemblages in the human gut microbiome. In this study, we detected gut microbiome assemblages using a latent Dirichlet allocation (LDA) method, which was first proposed for the classification of documents in natural language processing. We applied LDA to a large-scale human gut metagenome dataset and found that a four-assemblage LDA model can represent relationships between enterotypes and assemblages with high interpretability. This model indicates that each individual tends to have several assemblages, and each of three assemblages corresponded to each enterotype. However, the C-assemblage can exist in all enterotypes. Interestingly, the dominant genera of the C-assemblage (Clostridium, Eubacterium, Faecalibacterium, Roseburia, Coprococcus, and Butyrivibrio) included butyrate-producing species such as Faecalibacterium prausnitzii. Finally, we revealed that genera mainly appearing in the same assemblage were correlated to each other. We conducted an assemblage analysis on a large-scale human gut metagenome dataset using LDA, a powerful method for detection of microbial assemblages. This approach has the potential to reveal the structure of the human gut microbiome.

microbiology↗

bioRxiv2019

Dear Editorial manager PLoS ONEA survey of vaccine-induced measles IgG antibody titer and the verification of changes in temporal differences of measles vaccination in young adults

In Japan, sporadic measles cases increased rapidly in 2019 compared to that in past 6 years. To clarify the persistence of immunity against measles in young adult, this study explored the prevalence of IgG antibody titer against measles virus in 18-to 24-year-old young adult participants. Measles-specific IgG antibody titers determined by enzyme immunoassay in serum samples collected from 506 participants between 18 to 24 years were assessed through statistical analyses. Multiple regression analysis revealed that the distribution of measles IgG antibody titers was significantly correlated with medical history (P < 0.05), while there was no significant difference among the number of vaccinations related to measles IgG titers. Further, measles IgG titers were significantly different, which was determined by the temporal change that elapsed period after last vaccination (P < 0.05). These results indicate that periodic vaccination against measles is required for young and older adults to prevent even sporadic measles infection.

microbiology↗