Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Background: Bioinformatics pipelines spanning genomics, transcriptomics, proteomics, and metagenomics face a pervasive reproducibility crisis driven by software dependency drift, resource exhaustion, and non-deterministic tool behaviour. Despite substantial investment in workflow management systems such as Nextflow, Snakemake, and Galaxy, pipeline failure responses remain predominantly manual. Advances in large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration offer technically credible pathways to automated pipeline failure detection and autonomous remediation, yet their systematic application to bioinformatics-specific infrastructure has not been reviewed. Results: This PRISMA 2020-compliant systematic review (PROSPERO: CRD420261361756) synthesised 26 studies from six databases covering January 2019 to April 2026, addressing five research questions spanning LLM-based log parsing, multi-agent workflow orchestration, human-in-the-loop governance, fault-tolerant infrastructure patterns, and benchmarking gaps. LLM-RAG frameworks achieved up to 80% workflow step recall and reduced manual curation time by over 90%. Multi-agent systems including BioMaster and MARWA demonstrated superior error recovery across 18 omics modalities and 102 bioinformatics tools, consistently outperforming single-agent baselines. Infrastructure foundations are technically mature, but no included study demonstrated an end-to-end integrated self-healing pipeline combining monitoring, anomaly detection, remediation, and governance-compliant audit trail generation. Evidence quality was moderate (mean 6.2/10; range 5–9). Conclusions: Governance frameworks, bioinformatics-specific benchmarks, and regulatory alignment for clinical contexts remain critically absent and represent the field’s primary bottleneck. We propose a four-layer conceptual model (Infrastructure, Observability, Orchestration, Governance) to organise current evidence and identify research priorities. Three directions warrant priority investment: standardised failure-injection benchmarks, end-to-end integrated pipeline validation in production bioinformatics environments, and LLM fine-tuning on bioinformatics-specific log formats....
Background: Hydatid cyst disease is caused by the parasite Echinococcus and poses significant health concerns worldwide. Due to the lack of early symptoms and limited diagnostic tools, researchers aim to design a more specific and sensitive antigen. The study focuses on developing a recombinant multi‐epitope antigen using two parasite proteins (EgTeg and EgFABP1) and the IH4 nanobody. Methods: Protein sequences were analyzed and validated using bioinformatics tools, and B‐cell epitopes were identified. The resulting antigen, confirmed by UniProt, is 266 amino acids long. Results: The multi‐epitope antigen lacks a signal peptide and contains 46 phosphorylation sites associated with serine and tyrosine. Structural predictions showed both alpha helices and beta sheets in the secondary structure, with a spherical tertiary structure. Both linear and discontinuous epitopes were predicted, indicating regions with potential to stimulate immune responses. The antigen's physicochemical properties—molecular weight, isoelectric point stability index, and hydrophilicity— indicate that it is stable and suitable for diagnostic use. Conclusions: The study introduces the EgFABP1‐EgTeg‐IH4 recombinant protein as a promising candidate for diagnosing HC disease. By integrating multiple antigenic regions and the IH4 nanobody, this approach significantly improves diagnostic specificity and sensitivity, offering the potential for more accurate, earlier detection....
While proteomics is being increasingly applied to investigate extracellular vesicles (EVs), there remains no consensus on how to address the inevitable missing values within proteomics datasets. Here, we devised a three-step approach that prioritized retaining biologically-relevant information in EV samples using a dataset containing two populations of EVs analysed by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). Firstly, to avoid overfitting, we excluded proteins in which more than half of the values were missing. Next, we statistically tested for low protein abundance in a single population: when the number of potential “missing not at random” (MNAR) values was significantly enriched, these values were replaced with the lowest possible value of “1”. Finally, all remaining missing values were then imputed using the Random Forest machine learning algorithm. Our final dataset included 49.9 % of proteins that originally contained at least one missing value, from which 84.7 % were listed in the ExoCarta database, significantly more than would be expected by chance, strongly indicating biologically relevant imputation. To enable other EV researchers to analyse proteomics data in a robust, easy-to-use and peer-reviewed manner, we provide BioProEV, a bioinformatics pipeline to impute missing values with biological relevance in EV datasets....
The escalating demand for adaptable Artificial Intelligence (AI) systems presents a critical hurdle: generating efficient text embeddings tailored to specific problems. While Large Language Models (LLMs) excel in general contexts, they struggle in specialized domains due to their massive data requirements, opaque embedding strategies, and high computational costs. We introduce Biotext, featuring SWeePtex, a novel framework that adapts successful Bioinformatics techniques for text embedding. By converting text to the Biological Sequence-Like (BSL) format, our Python package enables the application of SWeeP, a tool originally developed for biological sequences, to create content-addressable vectors in natural language, employing the random projection paradigm. Using unsupervised machine learning, we validated this finding by analyzing data from 14,984 MEDLINE abstracts on the thioredoxin theme. Biotext, through SWeePtex, constructs a unified vector space for words and documents from scratch, capturing rich contextual relationships and offering scalable processing. Our usage example demonstrates that this Bioinformatics-inspired method effectively addresses key challenges in Natural Language Processing (NLP), providing interpretable, computationally efficient, and content-addressable linguistic representations for document exploration. Ultimately, Biotext demonstrates that bridging Bioinformatics and NLP yields powerful, efficient, and accessible text analysis tools that balance analytical power with interpretability, particularly valuable in specialized domains and resource-constrained environments. Biotext Python package is freely available at the PyPI repository....
Diabetic nephropathy (DN) stands as a primary contributor to end-stage renal disease. Podocyte injury is a key factor underlying proteinuria in DN. Metadherin (MTDH) participates in podocyte apoptosis and promotes renal tubular injury in DN. However, its role in podocyte damage and podocyte cytoskeleton remodeling requires further investigation. PTEN plays a crucial role in maintaining podocyte integrity; however, the mechanisms governing PTEN stability in DN remain poorly understood. This study is aimed at investigating the specific functional role of MTDH in PTEN regulation using db∕db diabetic mice, human kidney biopsy samples from DN patients, and cultured mouse podocytes exposed to high glucose (HG). MTDH expression was markedly increased in DN kidneys and HG-stimulated podocytes. Elevated MTDH resulted in decreased PTEN protein expression levels without altering PTEN mRNA expression, suggesting a posttranscriptional regulatory mechanism. Further assays demonstrated that MTDH promoted PTEN degradation through the ubiquitin–proteasome pathway. Through bioinformatics analysis of the GSE96804 dataset from the Gene Expression Omnibus (GEO) database, obtaining 13,289 differentially expressed genes and comparing them with the known ubiquitin ligase–encoding genes obtained from the Genecards database, we identified candidate hub genes involved in PTEN ubiquitination-mediated degradation. RNA sequencing identified ubiquitin-conjugating enzyme E2N (UBE2N) as a critical downstream mediator positively regulated by MTDH. Subsequent coimmunoprecipitation experiments confirmed direct interactions between PTEN and UBE2N, enhancing PTEN ubiquitination. Knockdown of UBE2N attenuated MTDH-induced PTEN degradation and podocyte cytoskeletal remodeling. Collectively, our findings reveal a novel regulatory axis wherein MTDH accelerates PTEN ubiquitination and proteasomal degradation via UBE2N, contributing to podocyte injury in DN. Targeting MTDH-driven PTEN ubiquitination degradation presents a promising therapeutic strategy to protect podocytes and mitigate diabetic kidney injury....
Loading....