Biology subjects

Ohta, T.

Publications and source records attributed to Ohta, T..

At least 19 recordsLinked to original sources

bioRxiv2026

The GA4GH Workflow Execution Service: A Standard API for Workflow Execution and Management in Genomics

Rapid growth in life science data generation has led to large and increasingly distributed collections of sensitive genomic data. To analyse this data, researchers increasingly rely on computational workflows to enable scalable, portable and reproducible analysis. However, these workflows are executed by specialized workflow engines across heterogeneous compute environments, each with its own requirements. As a result, users often need engine- and compute-environment-specific knowledge to submit, monitor and manage workflow execution, increasing the barriers to interoperability. These challenges are compounded in large-scale projects, where workflow runs must be dispatched to local clusters, cloud platforms or data hosting infrastructures with different underlying technologies. We describe the Global Alliance for Genomics and Health (GA4GH) Workflow Execution Service (WES) API, an approved GA4GH standard first released in 2018, together with its data model and the endpoints that define a unified interface for workflow execution. By standardizing workflow invocation, the WES API allows a single client to submit, monitor and cancel workflow runs across diverse workflow engines and compute backends, such as scheduling clusters or cloud platforms. We surveyed thirteen adoptions of the standard, covering standalone services and platforms in four programming languages. In a two-site demonstration, a WES implementation from Germany and one from Japan executed the same federated allele-frequency analysis with site-specific adjustments and only aggregated results leaving either site, demonstrating that it is possible to run the same analysis across independently built and operated WES services in different countries. From the gaps identified, we propose extensions for community discussion.

bioinformatics↗

bioRxiv2026

CATaN maps gene regulatory programs that shape genetic risk across complex diseases

Causal variants of complex traits are enriched at transcription factor (TF) binding sites and are thought to contribute to pathology by disrupting TF activity and thereby causing transcriptome dysregulation. However, existing approaches typically address TF-mediated gene regulatory networks (TF-GRNs) and transcriptomes separately, and methods that jointly leverage both to systematically assess disease heritability remain limited. We aimed to develop a framework that jointly leverages TF-GRNs and transcriptomes to assess disease heritability. Here, we constructed a matrix encoding TF-GRNs and developed an unsupervised analytical pipeline, Canonical correlation Analysis of Transcriptome and TF-gene regulatory Networks (CATaN). CATaN applies canonical correlation analysis (CCA) to extract canonical correlation (CC) components, i.e., shared variation components between transcriptomes and TF-GRNs, and converts them into genome-wide functional annotation scores connected to stratified LD score regression (S-LDSC) for heritability analysis. We applied CATaN to eight datasets, including 19,198 bulk samples and 611,772 single cells from human and mouse sources, identifying 588 CC components that are significantly enriched for SNP heritability across 69 complex traits. Notably, functional annotation tracks based on these TF-GRNs are distinct from transcriptome signatures prioritized by LDSC-SEG, with greater heritability enrichment for a subset of traits. Finally, we suggest that CATaN may help prioritize candidate causal variants for experimental fine-mapping using genome editing. Together, integrating TF-GRNs with transcriptomes reveals disease-relevant regulatory programs that are not fully captured by transcriptome-based analyses alone.

genetics↗

bioRxiv2025

Freshwater degradation as a potential extinction factor of the Japanese river otters

Exploring the causes of species extirpation can provide insights into mitigating the ongoing extinction. The Japanese otter Lutra lutra whiteleyi had extincted around 1970s, but their extinction causes remain unknown. To elucidate the ecological relationship among their foraging ecology, habitat degradation, and the extinction, we assessed dependency of Japanese otters on marine- or freshwater-derived resources using sulfur stable isotopes ({delta}34S: 34S/32S) of the museum specimens which were collected on the brink of extinction. Our results clearly showed that the otters strongly depended on marine-derived resources, indicating anthropogenic activities led to depletion of freshwater prey and a potential extinction factor of the last population of otters in Japan. Protecting freshwater habitats was crucial for preventing otter extinction in Japan, and for safeguarding the extant otter species.

ecology↗

bioRxiv2025

Uncertainty-aware transcription factor activity and perturbation inference without additional training

Understanding how transcription factors (TFs) control gene expression is essential for deciphering cellular regulatory programs. However, estimating TF activity remains challenging. Current methods rely on either broad TF-target databases, which increase false positives, or on curated sets with limited coverage. Most methods lack uncertainty quantification. Here, we introduce TFActProfiler, an integrated resource and computational framework that learns signed TF-mRNA effects by pretraining on prior TF-mRNA interaction knowledge and large RNA-seq atlases. Given an expression profile as input, TFActProfiler infers per-sample TF activity together with a confidence score that quantifies agreement between predicted and observed mRNA levels. Across TF knockdown datasets spanning multiple human cell types, TFActProfiler consistently improves TF activity inference over approaches based on unfiltered priors or narrowly curated target sets while retaining broad TF and mRNA coverage. The learned effect map further enables training-free prediction of transcriptome responses to TF perturbations. By coupling TF activity inference with training-free perturbation modeling and confidence estimates, TFActProfiler offers a tool for dissecting regulatory programs across diverse cellular states.

bioinformatics↗

bioRxiv2025

Chromosome-scale genomes of two wild flowering cherrys (Cerasus itosakura and C. jamasakura) provide insights into structural evolution in Prunus

Flowering cherries (genus Cerasus) are iconic trees in Japan, celebrated for their cultural and ecological significance. Despite their prominence, high-quality genomic resources for wild Cerasus species have been limited. Here, we report chromosome-level genome assemblies of two representative Japanese cherries: Cerasus itosakura, a progenitor of the widely cultivated C. xyedoensis Somei-yoshino, and Cerasus jamasakura, a traditional popular wild species endemic to Japan. Using deep PacBio long-read and Illumina short-read sequencing, combined with reference-guided scaffolding based on near-complete C. speciosa genome, we generated assemblies of 259.1 Mbp (C. itosakura) and 312.6 Mbp (C. jamasakura), with both >98% BUSCO completeness. Consistent with their natural histories, C. itosakura showed low heterozygosity, while C. jamasakura displayed high genomic diversity. Comparative genomic analyses revealed structural variations, including large chromosomal inversions. Notably, the availability of both the previously published C. speciosa genome and our new C. itosakura genome enabled the reconstruction of proxy haplotypes for both parental lineages of Somei-yoshino. Comparison with the phased genome of Somei-yoshino revealed genomic discrepancies, suggesting that the cultivar may have arisen from genetically distinct or admixed individuals, and may also reflect intraspecific diversity. Our results offer genomic foundations for evolutionary and breeding studies in Cerasus and Prunus.

genomics↗

bioRxiv2025

Extraction of biological terms using large language models enhances the usability of metadata in the BioSample database

BioSample is a comprehensive repository of experimental sample metadata, playing a crucial role in providing a comprehensive archive and enabling experiment searches regardless of type. However, the difficulty in comprehensively defining the rules for describing metadata and limited user awareness of best practices for metadata have resulted in substantial variability depending on the submitter. This inconsistency poses significant challenges to the findability and reusability of the data. Given the vast scale of BioSample, which hosts over 40 million records, manual curation is impractical. Rule-based automatic ontology mapping methods have been proposed to address this issue, but their effectiveness is limited by the heterogeneity of BioSample metadata. Recently, large language models (LLMs) have gained attention in natural language processing and have been expected as promising tools for automating metadata curation. In this study, we evaluated the performance of LLMs in extracting cell line names from BioSample descriptions using a gold-standard dataset derived from ChIP-Atlas, a secondary database of epigenomics experiment data, which manually curates samples. Our results demonstrated that LLM-assisted methods outperformed traditional approaches, achieving higher accuracy and coverage. We further extended this approach to extraction of information about experimentally manipulated genes from metadata where manual curation had not yet been applied in ChIP-Atlas. This also yielded successful results for the usage of the database, which facilitates more precise filtering of data and prevents misinterpretation caused by inclusion of unintended data. These findings underscore the potential of LLMs to improve the findability and reusability of experimental data in general, significantly reducing user workload and enabling more effective scientific data management.

bioinformatics↗

bioRxiv2024

RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature

MotivationIn the field of biomedical text mining, the ability to extract relations from literature is crucial for advancing both theoretical research and practical applications. There is a notable shortage of corpora designed to enhance the extraction of multiple types of relations, particularly focusing on proteins and protein-containing entities such as complexes and families, as well as chemicals. ResultsIn this work we present RegulaTome, a corpus that overcomes the limitations of several existing biomedical relation extraction (RE) corpora, many of which concentrate on single-type relations at the sentence level. RegulaTome stands out by offering 16,962 relations annotated in over 2,500 documents, making it the most extensive dataset of its kind to date. This corpus is specifically designed to cover a broader spectrum of over 40 relation types beyond those traditionally explored, setting a new benchmark in the complexity and depth of biomedical RE tasks. Our corpus both broadens the scope of detected relations and allows for achieving noteworthy accuracy in RE. A Transformer-based model trained on this corpus has demonstrated a promising F1-score (66.6%) for a task of this complexity, underscoring the effectiveness of our approach in accurately identifying and categorizing a wide array of biological relations. This achievement highlights RegulaTomes potential to significantly contribute to the development of more sophisticated, efficient, and accurate RE systems to tackle biomedical tasks. Finally, a run of the trained relation extraction system on all PubMed abstracts and PMC Open Access full-text documents resulted in over 18 million relations, extracted from the entire biomedical literature. AvailabilityThe corpus and all introduced resources are openly accessible via Zenodo (https://zenodo.org/doi/10.5281/zenodo.10808330) and GitHub (https://github.com/farmeh/RegulaTome_extraction).

bioinformatics↗

bioRxiv2023

STRING-ing together protein complexes: extracting physical protein interactions from the literature

Understanding biological processes relies heavily on curated knowledge of physical interactions between proteins. Yet, a notable gap remains between the information stored in databases of curated knowledge and the plethora of interactions documented in the scientific literature. To bridge this gap, we introduce ComplexTome, a manually annotated corpus designed to facilitate the development of text-mining methods for the extraction of complex formation relationships among biomedical entities. This corpus comprises 1,287 documents with [~]3, 500 relationships. We train a novel relation extraction model on this corpus and find that it can highly reliably identify physical protein interactions (F1-score=82.8%). We additionally enhance the models capabilities through unsupervised trigger word detection and apply it to extract relations and trigger words for these relations from all open publications in the domain literature. This information has been fully integrated into the latest version of the STRING database, and all introduced resources are openly accessible via Zenodo and GitHub.

bioinformatics↗

bioRxiv2023

S1000: A better taxonomic name corpus for biomedical information extraction

MotivationThe recognition of mentions of species names in text is a critically important task for biomedical text mining. While deep learning-based methods have made great advances in many named entity recognition tasks, results for species name recognition remain poor. We hypothesize that this is primarily due to the lack of appropriate corpora. ResultsWe introduce the S1000 corpus, a comprehensive manual re-annotation and extension of the S800 corpus. We demonstrate that S1000 makes highly accurate recognition of species names possible (F-score=93.1%), both for deep learning and dictionary-based methods. AvailabilityAll resources introduced in this study are available under open licenses from https://jensenlab.org/resources/s1000/. The webpage contains links to a Zenodo project and two GitHub repositories associated with the study. Contactsampo.pyysalo@utu.fi, lars.juhl.jensen@cpr.ku.dk Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

A workflow reproducibility scale for automatic validation of biological interpretation results.

BackgroundReproducibility of data analysis workflow is a key issue in the field of bioinformatics. Recent computing technologies, such as virtualization, have made it possible to reproduce workflow execution with ease. However, the reproducibility of results is not well discussed; that is, there is no standard way to verify whether the biological interpretation of reproduced results are the same. Therefore, it still remains a challenge to automatically evaluate the reproducibility of results. ResultsWe propose a new metric, a reproducibility scale of workflow execution results, to evaluate the reproducibility of results. This metric is based on the idea of evaluating the reproducibility of results using biological feature values (e.g., number of reads, mapping rate, and variant frequency) representing their biological interpretation. We also implemented a prototype system that automatically evaluates the reproducibility of results using the proposed metric. To demonstrate our approach, we conducted an experiment using workflows used by researchers in real research projects and the use cases that are frequently encountered in the field of bioinformatics. ConclusionsOur approach enables automatic evaluation of the reproducibility of results using a fine-grained scale. By introducing our approach, it is possible to evolve from a binary view of whether the results are superficially identical or not to a more graduated view. We believe that our approach will contribute to more informed discussion on reproducibility in bioinformatics.

bioinformatics↗

bioRxiv2022

Workflow sharing with automated metadata validation and test execution to improve the reusability of published workflows

BackgroundMany open-source workflow systems have made bioinformatics data analysis procedures portable. Sharing these workflows provides researchers easy access to high-quality analysis methods without the requirement of computational expertise. However, published workflows are not always guaranteed to be reliably reusable. Therefore, a system is needed to lower the cost of sharing workflows in a reusable form. ResultsWe introduce Yevis, a system to build a workflow registry that automatically validates and tests workflows to be published. The validation and test are based on the requirements we defined for a workflow being reusable with confidence. Yevis runs on GitHub and Zenodo and allows workflow hosting without the need of dedicated computing resources. A Yevis registry accepts workflow registration via a GitHub pull request, followed by an automatic validation and test process for the submitted workflow. As a proof of concept, we built a registry using Yevis to host workflows from a community to demonstrate how a workflow can be shared while fulfilling the defined requirements. ConclusionsYevis helps in the building of a workflow registry to share reusable workflows without requiring extensive human resources. By following Yeviss workflow-sharing procedure, one can operate a registry while satisfying the reusable work-flow criteria. This system is particularly useful to individuals or communities that want to share workflows but lacks the specific technical expertise to build and maintain a workflow registry from scratch.

bioinformatics↗

bioRxiv2022

A very long chain fatty acid responsive transcription factor, MYB93, regulates lateral root development in Arabidopsis

Lateral roots (LRs) are critical to rhizosphere development in plants. Although the molecular mechanisms by which auxin regulates LR development has been studied extensively, many additional regulatory systems are thought to be involved. Based on the expression analysis of LTPG1 and 2, we found that they were specifically expressed at the developing LR primordium (LRP), and the number of LRs were reduced in these mutants. Because LTPG is a protein that transports Very Long Chain Fatty Acids (VLCFAs), we hypothesized that VLCFAs regulated LR development. We revealed that late LRP development was stunted when VLCFA levels were reduced in the synthetic mutant, kcs1-5. In this study, we established a method to analyze the LRP development stages with high temporal resolution using a deep neural network. We identified a VLCFA responsible transcription factor, MYB93, from transcriptome analysis of kcs1-5. Interestingly, MYB93 showed a carbon chain length-specific expression response after treatment of VLCFA. Furthermore, myb93 transcriptome analysis suggested that MYB93 regulated the expression of cell wall organization genes. Our results indicated that VLCFA is a regulator of LRP development through transcription factor-mediated regulation of gene expression.

plant biology↗

bioRxiv2022

AT-hook DNA-binding motif-containing protein one knockdown downregulates EWS-FLI1 transcriptional activity in Ewings sarcoma cells

Ewings sarcoma is the second most common bone malignancy in children or young adults and is caused by an oncogenic transcription factor by a chromosomal translocation between the EWSR1 gene and the ETS transcription factor family. However, the transcriptional mechanism of EWS-ETS fusion proteins is still unclear. To identify the transcriptional complexes of EWS-ETS fusion transcription factors, we applied a proximal labeling system called BioID in Ewings sarcoma cells. We identified AHDC1 as a proximal protein of EWS-ETS fusion proteins. AHDC1 knockdown showed a reduced cell growth and transcriptional activity of EWS-FLI1. AHDC1 knockdown also reduced BRD4 and BRG1 protein levels, both known as interacting proteins of EWS-FLI1. In addition, AHDC1 co-localized with BRD4. Our results suggest that AHDC1 supports cell growth through EWS-FLI1.

cancer biology↗

bioRxiv2021

m6A demethylase ALKBH5 promotes tumor cell proliferation by destabilizing IGF2BPs target genes and worsens the prognosis of patients with non-small cell lung cancer

The modification of N6-methyladenosine (m6A) in RNA and its eraser ALKBH5, an m6A demethylase, play important roles across various steps of human carcinogenesis. However, the involvement of ALKBH5 in non-small cell lung cancer (NSCLC) development remains to be completely elucidated. The current study revealed that the expression of ALKBH5 were increased in NSCLC and increased expression of ALKBH5 worsened the prognosis of patients with NSCLC. In vitro study revealed that ALKBH5 knockdown suppressed cell proliferation ability of PC9 and A549 cells as well as promoted G1 arrest and increased the number of apoptotic cells. Furthermore, ALKBH5 overexpression increased the cell proliferation ability of the immortalized cell lines. Microarray analysis and western blotting revealed that the expression of CDKN1A or TIMP3 were increased by ALKBH5 knockdown. These alterations were offset by a double knockdown of both ALKBH5 and one of the IGF2BPs. The decline of mRNAs was, at least partly, owing to the destabilization of these mRNAs by one of the IGF2BPs. In conclusions, the ALKBH5-IGF2BPs axis promotes cell proliferation and tumorigenicity, which in turn causes the unfavorable prognosis of NSCLC.

cancer biology↗

bioRxiv2020

MCM10 compensates for Myc-induced DNA replication stress in breast cancer stem-like cells

Cancer stem-like cells (CSCs) are responsible for the drug resistance of tumors and recurrence while they experience DNA replication stress. However, the underlying mechanisms that cause DNA replication stress in CSCs and how they compensate for this stress remain unclear. Here we provide evidence that upregulated c-Myc expression induces stronger DNA replication stress in patient-derived breast CSCs than in differentiated cancer cells. Our results suggest critical roles for mini-chromosome maintenance protein 10 (MCM10), which is a firing (activating) factor of the DNA replication origins, to compensate for the DNA replication stress. Expression levels of MCM10 are upregulated in CSCs and maintained by c-Myc. c-Myc-dependent collisions may take place between RNA transcription and DNA replication machinery in nuclei, thereby causing DNA replication stress. MCM10 may activate dormant replication origins close to the collisions to ensure replication progression. Moreover, patient-derived breast CSCs were dependent on MCM10 for their maintenance even after enrichment for CSCs that were resistant to paclitaxel, the standard chemotherapeutic agent. In addition, MCM10 depletion decreased the growth of cancer cells but not normal cells. Therefore, MCM10 is likely to robustly compensate for DNA replication stress and facilitate genome duplication in the S-phase in cancer cells, which is more pronounced in CSCs. We provide a preclinical rationale to target the c-Myc-MCM10 axis to prevent drug resistance and recurrence.

cancer biology↗

bioRxiv2020

In silico integration of thousands of epigenetic datasets into 707 cell type regulatory annotations improves the trans-ethnic portability of polygenic risk scores

Poor trans-ethnic portability of polygenic risk score (PRS) models is a critical issue that may be partially due to limited knowledge of causal variants shared among populations. Hence, leveraging noncoding regulatory annotations that capture genetic variation across populations has the potential to enhance the trans-ethnic portability of PRS. To this end, we constructed a unique resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 public epigenetic datasets to predict binding patterns of 142 cell-type-regulating transcription factors across 245 cell types. With this resource, we partitioned the common SNP heritability of diverse polygenic traits and diseases from 111 GWAS summary statistics of European (EUR, average N=180K) and East Asian (EAS, average N=157K) origin. For 95 traits, we were able to identify a single IMPACT annotation most strongly enriched for trait heritability. Across traits, these annotations captured an average of 43.3% of heritability (se = 13.8%) with the top 5% of SNPs. Strikingly, we observed highly concordant polygenic trait regulation between populations: the same regulatory annotations captured statistically indistinguishable SNP heritability (fitted slope = 0.98, se = 0.04). Since IMPACT annotations capture both large and consistent proportions of heritability across populations, prioritizing variants in IMPACT regulatory elements may improve the trans-ethnic portability of PRS. Indeed, we observed that EUR PRS models more accurately predicted 21 tested phenotypes of EAS individuals when variants were prioritized by key IMPACT tracks (49.9% mean relative increase in R2). Notably, the improvement afforded by IMPACT was greater in the trans-ethnic EUR-to-EAS PRS application than in the EAS-to-EAS application (47.3% vs 20.9%, P < 1.7e-4). Overall, our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ethnic portability of genetic data, and this has important implications for future risk prediction models that work across populations.

genetics↗

bioRxiv2020

Shikonin induces odontoblastic differentiation of dental pulp stem cells via AKT-mTOR signaling in the presence of CD44

In our previous study, we demonstrated that hyaluronan induces odontoblastic differentiation of dental pulp stem cells via interactions with CD44. However, it remains unclear whether CD44 expression by dental pulp stem cells is required for odontoblastic differentiation. Therefore, we searched for a compound that induces odontoblastic differentiation of dental pulp stem cells, regardless of the chemical structure and function of hyaluronan, and examined whether CD44 is involved in the induction of odontoblastic differentiation by the compound. Because vitamin K analogues can promote bone formation and tissue calcification, we focused on derivatives of naphthoquinone, the skeleton of vitamin K; we verified whether those compounds could induce odontoblastic differentiation of dental pulp stem cells. We found that dentin sialophosphoprotein, a marker of odontoblasts, was expressed in dental pulp stem cells after treatment with shikonin. The shikonin-induced expression of dentin sialophosphoprotein was inhibited by PI3K, AKT, and mTOR inhibitors. In addition, in dental pulp stem cells transfected with siRNA against CD44, the shikonin-induced expression of dentin sialophosphoprotein was inhibited. Thus, shikonin can stimulate dental pulp stem cells to undergo odontoblastic differentiation through a mechanism involving the AKT-mTOR signaling pathway and CD44. Hyaluronan stimulated dental pulp stem cells to undergo CD44-mediated odontoblastic differentiation in our previous study; the present study indicated that CD44 is necessary for dental pulp stem cells to undergo odontoblastic differentiation. Although expression of CD44 is important for inducing odontoblastic differentiation of dental pulp stem cells, the relationship between the AKT-mTOR signaling pathway and CD44 expression, in the context of shikonin stimulation, has not yet been elucidated. This study suggested that shikonin may be useful for inducing odontoblastic differentiation of dental pulp stem cells, and that it may have clinical applications, including protection of dental pulp.

cell biology↗

bioRxiv2018

Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection

BackgroundContainer virtualization technologies such as Docker became popular in the bioinformatics domain as they improve portability and reproducibility of software deployment. Along with software packaged in containers, the workflow description standards Common Workflow Language also enabled to perform data analysis on multiple different computing environments with ease. These technologies accelerate the use of on-demand cloud computing platform which can scale out according to the amount of data. However, to optimize the time and the budget on a use of cloud, users need to select a suitable instance type corresponding to the resource requirements of their workflows.\n\nResultsWe developed CWL-metrics, a system to collect runtime metrics of Docker containers and workflow metadata to analyze resource requirement of workflows. We demonstrated the analysis by using seven transcriptome quantification workflows on six instance types. The result showed instance type options of lower financial cost and faster execution time with required amount of computational resources.\n\nConclusionsThe summary of resource requirements of workflow executions provided by CWL-metrics can help users to optimize the selection of cloud computing instance. The runtime metrics data also accelerate to share workflows among different workflow management frameworks.

bioinformatics↗