statistical testing for differential expression (limma/deseq2 workflows)

To address inflated false discovery rates (FDR) in high-dimensional RNA-seq differential expression analysis arising from multiple testing, this study develops a robust statistical inference framework. Methodologically, we systematically compare three FDR control procedures—Benjamini–Hochberg (BH), Benjamini–Yekutieli (BY), and Storey’s q-value—and introduce an adaptive q-value approach to enhance statistical power under sparsity and high dimensionality. Visualization and performance evaluation integrate PCA, volcano plots, MA plots, and confusion matrices. Our key contribution is the first systematic quantification—within transcriptomic contexts—of how inter-gene correlation and batch effects compromise FDR control; we demonstrate that Storey’s method maintains strict FDR control while substantially improving detection sensitivity and cross-dataset reproducibility of differentially expressed genes. This workflow provides a standardized, statistically rigorous, and practically applicable analytical pipeline for large-scale transcriptomic studies.

This work addresses the pervasive challenges in bioinformatics tooling—such as fragmentation, complex dependencies, inconsistent documentation, and irreproducible environments—that severely hinder method reuse and adaptation. To overcome these limitations, the authors propose PoSyMed, an open modular platform that integrates biomedical workflows through formalized tool descriptions, containerized execution, a persistent workflow engine, and a conversational interface. Innovatively, a large language model is incorporated as a semantic assistant within a typed, validated, and human-supervised framework to support tool discovery, pipeline construction, and parameter configuration. This design significantly enhances analytical transparency and reproducibility. The platform’s efficacy is demonstrated in representative biomedical use cases, and it has been released as open-source software.

Current evaluations of bioinformatics agents overemphasize answer correctness while neglecting workflow auditability and scientific credibility. This work proposes a Function–Evidence–Validation (FEV) tri-dimensional evaluation framework centered on inspectable workflow trajectories, shifting the primary focus to workflow correctness for the first time. Through systematic literature review, trajectory analysis, and cross-domain benchmark mapping, the study comprehensively analyzes 109 agent systems and 28 evaluation resources across subfields including genomics, single-cell and spatial omics, and protein science. The findings reveal that while agents perform adequately in planning and execution, they exhibit significant deficiencies in reproducibility, traceability, external validation, and prospective experimental design. This research provides both theoretical grounding and practical guidance for developing transparent, auditable next-generation bioinformatics agents.

To address the challenges of parallel scheduling, opaque execution states, poor result reproducibility, and inadequate auditability when managing hundreds to thousands of Snakemake/Nextflow pipelines in large-scale bioinformatics analyses, this paper proposes a lightweight command-line orchestration framework. Built in Python and integrated with SQLite or PostgreSQL, it enables unified pipeline launching across heterogeneous workflows, real-time status monitoring, fine-grained log collection, automated result ingestion into databases, and comprehensive lifecycle metric logging—including runtime, resource consumption, and failure points. It introduces a novel CLI paradigm that supports cross-pipeline collaborative monitoring and reproducibility assurance without modifying existing workflow code. Experimental evaluation demonstrates a 42% improvement in multi-project throughput, significantly enhancing observability, auditability, and reproducibility in large-scale bioinformatics analysis.

This work addresses the heavy reliance on expert knowledge in designing and debugging scientific workflows, a challenge exacerbated by existing large language model approaches that directly generate code without ensuring transparency, reproducibility, or seamless system integration. To overcome these limitations, we propose an AI-assisted scientific workflow management framework that decouples user intent from implementation through a structured specification phase, enabling specification-driven workflow generation and validation. We further introduce a multi-layer debugging agent powered by large language models to automate error diagnosis and correction. By deeply integrating with the Pegasus workflow system via the Model Context Protocol (MCP), our approach supports end-to-end workflow lifecycle management. Empirical evaluation demonstrates successful generation and execution of federated learning medical imaging workflows comprising thousands of tasks, substantially reducing debugging effort and empowering non-expert users to construct complex workflows adhering to expert-level design patterns.

This study addresses the scarcity of structured, context-rich experimental data in targeted protein degradation (TPD), which has hindered the development of computational models. To overcome this limitation, the authors propose the first expert-in-the-loop large language model (LLM) agent framework tailored for TPD. By integrating lightweight prompt optimization, terminology-aware transfer, and a triangulation-based validation mechanism, the framework automatically extracts multidimensional information—including compounds, targets, recruiters, and critical experimental conditions—from scientific literature. Requiring only minimal annotated data, it achieves high-accuracy cross-task transfer. The resulting molecular glue and PROTAC databases are expanded by 81% and 92%, respectively, with expert-validated accuracy rates of 92% and 82.5%, substantially enhancing condition-aware modeling of degrader activity.

Existing large language model (LLM)-driven data analysis tools are often confined to isolated subtasks and struggle to support end-to-end executable analytical workflows. This work proposes an autonomous, sandboxed, and auditable end-to-end system that leverages LLMs for action planning, iteratively generating structured operations, executing code in a secure environment, and integrating streaming traceability with intermediate result previews. By unifying a structured action backend, sandboxed execution, and an interactive visual interface—features integrated here for the first time—the system enables users to drive complete analytical workflows using only natural language. Users can inspect, modify, and export the entire process and its outputs directly within a web browser, ensuring full reproducibility, editability, and transparency throughout the analytical pipeline.

Developing cross-platform graphical user interfaces (GUIs) and plugins for command-line tools in structural bioinformatics is often costly and complex. This work proposes a three-stage automated workflow that leverages a platform-agnostic formal GUI specification, decouples model, view, and presenter components through the Model–View–Presenter (MVP) architectural pattern, and employs a dedicated code generator to automatically produce plugins for target platforms—namely VMD, PyMOL, and the web. To the best of our knowledge, this is the first systematic application of the MVP pattern to the automatic GUI generation for CLI tools, substantially enhancing logic reusability, cross-platform portability, and development efficiency. The framework’s generality, extensibility, and practical utility are demonstrated by successfully generating plugins for multiple tools from the Structural Bioinformatics Library across all three platforms.

In enterprise microservice regression testing, QA engineers often lack up-to-date documentation and must rely on real user traffic to reconstruct business scenarios; however, transforming such traffic into replayable test cases with stable assertions is labor-intensive and error-prone. This work proposes NL2Test, a method that combines the semantic understanding of large language models with deterministic algorithms to generate executable API test cases end-to-end from natural language scenario descriptions and captured execution traces. NL2Test automatically slices request sequences, reconstructs data dependencies, masks non-deterministic fields, and produces business-aligned, reliable assertions. Evaluated on 51 industrial scenarios, it achieves an exact match rate of 82.4%, with 98.0% of generated test cases becoming functional after minor tuning. During a nine-month production deployment, it produced 3,196 test cases, of which 85.4% were accepted and integrated into the codebase.

This work addresses the lack of systematic evaluation benchmarks for AI agents in multi-step bioinformatics workflows, which hinders reliable assessment of their performance and robustness. We propose the first standardized evaluation framework encompassing end-to-end tasks such as RNA-seq analysis and variant calling. The framework integrates structured prompting, an automated LLM-based scoring mechanism, and perturbation tests—including corrupted inputs and decoy files—to holistically evaluate agents on workflow completeness, output correctness, and robustness, while also considering the applicability of open-source models in privacy-sensitive settings. Experimental results demonstrate that leading closed-source agents can reliably execute complex pipelines but exhibit reasoning vulnerabilities under perturbations, whereas open-source models, despite lower task completion rates, offer greater practical utility when data privacy constraints limit access to external systems.