Biology subjects

Fukuda, B.

Publications and source records attributed to Fukuda, B..

3 recordsLinked to original sources

bioRxiv2025

User-friendly scheduler Using a hybrid architecture and supercomputing for big data processing

The exponential growth of omics data requires novel strategies for storage, transfer, and processing of said data. We present a scheduler based on the Temporal.io workflow framework which enables two key optimizations of bioinformatics workflows. Firstly, we enable users to transparently map workflow steps to diverse execution environments, including high-performance computing (HPC) resources managed by the SLURM resource manager through an easy-to-use graphical user interface. Secondly, we enable asynchronous execution of workflows, a feature which guarantees that workflows will achieve reasonable resource utilization even when the scheduler cannot make use of a systems full RAM and CPU resources. Thirdly, we propose a universal, platform agnostic JSON representation of workflows that allows platform-specific execution details to be abstracted away from the core scientific logic. Our work includes a custom executor plugin that supports translation of workflows from an external language, such as Nextflow, to our universal JSON format. Finally, we develop a graphical user interface to make our scheduler easy-to-use for non-technical users. When benchmarked on a bulk RNA sequencing workflow, these features reduced the cost and time requirements. We illustrated the merits of our cross-platform method using credit allocations from federally funded supercomputers.

bioinformatics↗

bioRxiv2025

Uncovering the Pathophysiological Pattern of Expression from Integrated Analysis across Uniformly Processed RNA Sequencing COVID-19 Datasets

BackgroundPost-acute sequelae of SARS-CoV-2 infection (PASC) affects millions globally, yet the molecular mechanisms underlying acute COVID-19 and its chronic sequelae remain poorly understood. MethodsWe performed an integrative transcriptomic analysis of three independent RNA-seq datasets, capturing the complete COVID-19 pathophysiology from health through acute severe infection to post-acute sequelae and mortality (n=142 total samples). We implemented a containerized analytical pipeline from data download, quantification, differential gene expression to uniformly process these three RNA-seq datasets. ResultsOur analysis reveals striking molecular dichotomies contrasting disease phases with profound clinical implications. Acute severe/critical COVID-19 reveals predominant enrichment of TNF- signaling via NF-{kappa}B pathways (normalized enrichment score >2.5, FDR <0.001), reflecting a cytokine storm pathophysiology characterized by rapid inflammatory developments involving IL-6, TNF-, and anti-apoptotic responses. In contrast, PASC patients exhibit dominant enrichment of Myc Targets V1 and Oxidative Phosphorylation pathways (NES >2.2, FDR <0.005), indicating important shifts toward cellular adaptation. Pathway signature analysis identifies core differentially expressed genes that reliably distinguish disease phases, thereby offering objective biomarkers for precision diagnosis and monitoring. ConclusionsThese findings establish a comprehensive molecular framework distinguishing acute inflammatory from chronic metabolic COVID-19 phases, with potential clinical applicability. TNF-/NF-{kappa}B pathway signatures identify patients at risk for severe disease progression, while Myc/OXPHOS signatures allow objective PASC diagnosis, addressing current reliance on subjective and eliminative diagnosis. This integrative analytical framework has utility beyond COVID-19, offering an applicable approach for precision medicine implementation across other diseases processes. Clinical SignificanceThis study transforms COVID-19 from a symptom-based to a molecularly-defined disease spectrum, enabling precision diagnosis, prognostic monitoring, classification, and targeted therapeutic possibilities based on pathway-specific biomarkers rather than subjective clinical assessments.

genomics↗

bioRxiv2022

Accessible, interactive and cloud-enabled genomic workflows integrated with the NCI Genomic Data Commons

Cancer data is widely available in repositories such as the National Cancer Institute (NCI) Genomic Data Commons (GDC). These datasets could serve as controls or comparisons in compendium analyses with user data, avoiding the expense and time of generating additional datasets. However, the user must be able to process their new data in the same manner for these comparisons to be useful. This can be non-trivial. Although the executables themselves are usually available in repositories, the GDC pipelines that describe that entire analysis workflow are currently published as text-based standard operating procedures (SOPs). It is difficult to document a computational workflow to the level of detail and accuracy required to reproduce the results. Discrepancies between versions and exclusions of details accumulate as the documentation inevitably lags behind code revisions. We address this problem by converting the SOPs into a downloadable and executable format. Specifically, we converted the GDC DNA sequencing (DNA-Seq) and the GDC mRNA sequencing (mRNA-Seq) SOPs into reproducible, self-installing, containerized, and interactive graphical workflows. These can be applied to reproducibly process user data and to harmonize datasets across repositories. Using our publicly available graphical workflows, we harmonize raw RNA-Seq datasets from the GDC and the Genotype-Tissue Expression (GTEx) project that were originally processed using different methodologies to illustrate the importance of uniform processing of control and treatment data for accurate inference of differentially expressed genes. By disseminating the analytical methodology in a reproducible and easily executed form, we greatly increase the utility of the GDC by enabling researchers to uniformly process custom data and datasets across multiple repositories to enhance data interpretation. Our approach and open-source executable workflows of making the analytical process as readily available as the data can be applied to other data repositories to increase their impact on scientific research.

bioinformatics↗