The respiratory microbiome plays an important role in maintaining human health. Despite the rapid growth of literature and publicly accessible data on the respiratory microbiome, a large-scale, well-curated database is still lacking. Here, we introduced ResMicroDb, a comprehensive database and analysis platform for the human respiratory microbiome. ResMicroDb contains 106 464 samples from 514 projects, spanning 10 sample sites, 72 sample types, and 146 phenotypes. Notably, it includes ~7-fold more respiratory samples than existing multi-body-site resources. To improve the reusability and accessibility of data, a standardized bioinformatics pipeline was employed to generate taxonomic profiles, and 32 metadata fields were manually curated. ResMicroDb also provides 11 908 microbe-disease associations, identified from 132 case-control studies, to deepen the understanding of microbiome-disease relationships. Additionally, ResMicroDb offers three tools for in-depth analysis: "Microbiome Composition" for visualizing taxonomic profiles of user-selected samples; "Sample Similarity Search" for inferring the characteristics of new samples by comparing them to the database based on similarity; and "Cross-study Analysis" for identifying common and specific microbial characteristics across cohorts, phenotypes and sample sites. ResMicroDb serves as a versatile and valuable resource for advancing a broad spectrum of respiratory microbiome research and clinical relevance. ResMicroDb is freely accessed at https://resmicrodb.cncb.ac.cn.

Background Sickle cell disease (SCD) is an inherited blood disorder characterized by chronic hemolysis, inflammation, and vaso-occlusive crises (VOC), leading to multiple complications and reduced life expectancy in affected individuals. Limited effective treatment options are currently available; however, recent genomic findings from underrepresented populations (Saudi Arabians) have offered new hope for predicting molecularly guided treatments. This study aimed to identify approved drugs suitable for repurposing based on their interactions with SCD-associated genetic variants and to discover novel druggable targets within genetic pathways linked to disease severity by utilizing genome-wide association study (GWAS) data from Saudi SCD patients. Methods Bioinformatic pipelines were used to evaluate drug-gene interactions and identify potential therapeutic targets based on GWAS data derived from the Saudi population. Approved drugs were suggested for repurposing according to their interactions with genes known to impact SCD pathophysiology, using the Drug-Gene Interaction Database (DGIdb 5.0). New drug targets were also proposed by assessing the simulated binding pockets of gene products, using 3D protein structures from the Protein Data Bank (PDB) and the AlphaFold database. Molecules with higher druggability scores, as estimated by the DoGSiteScorer database, were predicted to have a higher success rate for new SCD treatment development. Results Our analysis identified 78 approved medications with potential for repurposing in SCD; this list was narrowed to 21 candidates based on safety profiles and interactions with key genetic pathways. Among these, simvastatin, allopurinol, omalizumab, canakinumab, and etanercept were suggested as the most promising agents. Furthermore, novel drug targets encoded by olfactory receptor (OR) gene clusters (OR51V1, OR52A1, OR52A5, OR51B5, and OR51S1), TRIM genes, SIDT2, and CADM3 displayed high druggability scores. Conclusion This study provides a robust framework for drug repurposing and novel drug discovery in SCD, particularly tailored to the Saudi population. The findings underscore the potential of leveraging genomic data to identify targeted therapies, offering a pathway to more personalized and effective treatments for SCD patients. Future clinical trials are essential to validate these findings and translate them into clinical practice.

Therapeutic resistance in glioblastoma (GBM) remains a critical clinical challenge. To better understand its molecular basis, we performed a multistage computational analysis on a resistant GBM data set derived from the Ivy Glioblastoma Atlas Project, filtered for unmethylated MGMT and EGFR amplification. A subtype-specificity analysis identified distinct expression patterns for established biomarkers, including PDGFRA, FAP, and CD163, providing refined context for their roles in specific GBM subtypes. Kaplan-Meier analysis confirmed that high PDGFRA expression correlates with poor survival in this resistant population. To explore its therapeutic tractability, we developed a Quantitative Structure-Activity Relationship (QSAR) model targeting PDGFRA. This led to the in silico identification of a novel lead compound with a distinct thiazolopyridine-based scaffold and high predicted potency. Our findings demonstrate how integrated bioinformatic pipelines can dissect the complex landscape of GBM resistance, contextualize the roles of key oncogenes, and guide the rational design of potential new inhibitors.

High-throughput Sequencing in the Diagnosis of Inherited Bleeding and Clotting Disorders: Technical and Interpretive Considerations Necessary for Accurate Reporting and Clinical Application.

Some assembly required: Comparison of bioinformatic pipelines for analysis of viral metagenomic sequencing from nosocomial respiratory virus outbreaks.

Advances in molecular tools for parasitic nematodes of animals - From genetic markers to metabarcoding and genomics.

Tracking polyhydroxyalkanoate biosynthesis in thermophilic microorganisms.

Advancements and challenges in bioinformatics tools for microbial genomics in the last decade: Toward the smart integration of bioinformatics tools, digital resources, and emerging technologies for the analysis of complex biological data.

Microbiome and pathogen identification, and associated antimicrobial resistance genes and virulence factors in seafood revealed by 16S rRNA amplicon and metagenomic sequencing.

A long-amplicon nanopore sequencing and analysis method for human whole mitochondrial genome.

PURPOSE: Exercise–microbiome research is expanding rapidly, but methodological heterogeneity and technical limitations still hinder reproducibility and mechanistic interpretation. This review provides a comprehensive methodological roadmap to overcome these barriers.METHODS: We conducted a structured literature search in PubMed, Web of Science, and Scopus for records published between 1998 and 2025 using predefined combinations of exercise and gut-microbiome terms. After deduplication, titles/abstracts and full texts were screened according to prespecified criteria, yielding 99 eligible studies on aerobic and resistance/anaerobic exercise and gut microbiota. Evidence is critically appraised across standard short-read 16S rRNA protocols, shotgun metagenomics, and emerging long-read sequencing, as well as metatranscriptomics, metabolomics, and associated bioinformatics pipelines. A PRISMA-style flow diagram summarizes the study-selection process.RESULTS: Exercise across diverse modalities reshapes gut microbial diversity and community structure, frequently enriching taxa such as <i>Akkermansia muciniphila</i> and <i>Veillonella</i> and enhancing production of short-chain fatty acids (SCFAs). SCFAs strengthen the intestinal barrier, activate anti-inflammatory pathways, and supply energy substrates for colonocytes and exercising muscle. Long-read sequencing now enables species- and strain-level resolution beyond short V-region amplicons, while inclusion of the gut mycobiome and virome expands ecological scope. Multi-omics designs integrating metagenomics, metatranscriptomics, and metabolomics connect microbial composition with functional outputs and host metabolic adaptations.CONCLUSIONS: The future of exercise–microbiome science lies not in enlarging static catalogs of responsive microorganisms but in constructing individual-level predictive models. Integrating long-read sequencing and multi-omics with standardized training metadata will enable precision exercise prescriptions and microbiome-targeted interventions, including tailored probiotics, synbiotics, and nutrition strategies. Adoption of these advanced methodologies can accelerate mechanistic insight and promote translation of exercise–microbiome research into athletic performance optimization and clinical practice.

Abstract Motivation The rapid evolution of bioinformatics tools and multi-step analytic procedure presents a challenge for building effective pipelines, particularly for researchers without extensive programming expertise. This study demonstrates that large language models (LLMs) hold strong potential for generating end-to-end bioinformatic pipelines through carefully crafted prompts, using a multi-step metaviral workflow as a representative example. Multiple LLMs were tested for their effectiveness, including OpenAI ChatGPT series, Anthropic Claude series, Google Gemini, Meta Llama, and DeepSeek. Results Our results show that ChatGPT-4, ChatGPT-5, Claude 4.5, and Gemini 2.5 consistently outperform other LLMs in generating complete bioinformatic pipelines, with statistically significant success rates. These models also handle tool substitutions effectively. Simple prompt engineering and the inclusion of official documentation further enhance performance, especially for newer bioinformatic tools. While capabilities vary, all LLMs tested show potential for both pipeline generation and updates with our designed prompts and strategies. Availability and implementation All prompts are available in the paper. The examples are available in GitHub https://github.com/mpckkk/pBio.

Hulun Lake, a UNESCO Biosphere Reserve, faces mounting threats from extreme climate events and anthropogenic pressures, highlighting the need for robust biodiversity monitoring. Environmental DNA (eDNA) has emerged as a promising tool for aquatic biomonitoring, yet different bioinformatic pipelines—such as Amplicon Sequence Variant (ASV) and Operational Taxonomic Unit (OTU) clustering—may yield divergent results. This study compares ASV and OTU clustering approaches in eDNA metabarcoding alongside traditional capture-based surveys to assess fish diversity in Hulun Lake. Across all methods, we identified 43 taxa (40 species), including the critically endangered Acheilognathus hypselonotus and vulnerable Choi’s spiny loach (Cobitis choii). While eDNA methods detected 2~3 times more species than in nets (13 species), strong methodological correlations (p < 0.001) were observed between net frequencies and eDNA-derived relative abundances (based on both ASV and OTU datasets using 4th-root and log transformations). Clustering analysis of capture-based data revealed four distinct ecological zones: the areas near tourist facilities, Wuerxun River inflow region, Wulan Nuoer Lake (connected via the Wuerxun River), and the Lake Centre. Significant spatial variation (p < 0.05) between these four zones was found in eDNA datasets, whereas nets captured more heterogeneous patterns, consistent with previous studies. Community structures were shaped by both generalists (e.g., Cyprinus carpio, Hemiculter bleekeri) and habitat specialists such as Amur catfish (Silurus asotus). The Lake Centre hosted a unique assemblage, likely due to reduced human disturbance. Overall, both eDNA clustering methods outperformed capture-based survey in detecting species richness and offered semi-quantitative insights. However, discrepancies between ASV and OTU approaches were evident in resolving fine-scale community differences. We recommend an integrated monitoring strategy that combines the sensitivity of eDNA with the abundance resolution of net captured to inform spatially targeted conservation and habitat protection in this vulnerable ecosystem.

Exome sequencing (ES) and genome sequencing (GS) are essential for diagnosing rare genetic disorders, yet a significant number of patients remain without a definitive diagnosis after the initial analysis. Reanalysis of existing ES and GS data has emerged as a clinically indispensable practice, offering the potential to solve previously unresolved cases by leveraging rapid advances in genomic knowledge and technology. This review comprehensively addresses the growing importance, clinical utility, methodologies, and challenges of ES and GS data reanalysis. The increase in diagnostic yield from reanalysis is driven by several key factors: the continuous discovery of new gene-disease associations, ongoing updates to clinical and population genetic databases like ClinVar and gnomAD, the refinement of bioinformatic pipelines, and the application of advanced analytical techniques. Reanalysis has been shown to provide an additional diagnostic yield ranging from 3% to 15% across various disease cohorts, including neurodevelopmental, renal, and cardiovascular disorders. A significant portion of these new diagnoses stems from the reclassification of variants of uncertain significance, which often leads to direct and meaningful changes in clinical management, including targeted surveillance and tailored therapies. The reanalysis of ES and GS data is no longer a supplementary activity but a fundamental component of modern genomic medicine, transforming genomic testing from a one-time event into a continuous diagnostic process. To fully realize its potential, the development of standardized guidelines is crucial to address financial, logistical, and ethical barriers and to facilitate the equitable integration of reanalysis into routine clinical care.

The increasing prevalence of type 2 diabetes mellitus (T2DM), largely attributable to poor dietary habits and nutritional imbalances, highlights the need for novel diagnostic and therapeutic approaches. Emerging evidence emphasizes the critical role of trace elements in the pathogenesis and management of T2DM. However, the molecular mechanisms through which zinc and chromium deficiencies contribute to disease progression remain largely unexplored. This study aimed to investigate the role of zinc and chromium in T2DM pathogenesis through a combination of bioinformatics analysis, molecular docking, trace element profiling, and gene expression validation. Relevant genes identified from the literature were analyzed using a bioinformatics pipeline to uncover hub gene networks and regulatory pathways associated with trace element deficiencies. Molecular docking of HUB genes with zinc and chromium enriched compounds were employed to uncover the new therapeutic approach. For experimental validation, serum zinc and chromium levels were measured in fifty T2DM patients and fifteen healthy controls using atomic absorption spectrophotometry. Gene expression profiling of GCK (zinc associated) and GLUT4 (chromium associated) was performed using real time PCR. Bioinformatics analysis revealed that HUB genes affected due to zinc and chromium deficiency were linked to an elevated risk of T2DM. Crucial metabolic pathways was altered due to zinc and chromium deficiency in T2DM.The serum concentration of zinc and chromium were markedly lower in T2DM patients as compared to controls. Level of zinc was significantly lower in T2DM patients with nephropathy and chromium level was significantly were lower in T2DM patients with CVD. Expression of GCK and GLUT4 was reduced by approximately 4-6 folds and 3-4 folds, respectively, in T2DM and its associated complications. The study provide novel insights into the gene-environment interaction driving T2DM and highlight the therapeutic approach of zinc and chromium supplementation in mitigating diseases progression. This study paves the way for future research into personalized nutritional interventions targeting gene expression abnormalities in diabetes management.

Abstract Motivation Bioinformatics pipelines should meet the FAIR criteria to enable reproducible analysis. FAIR describes four key requirements for reproducible research: findability, accessibility, interoperability and reusability. Software containers such as Singularity are widely used tools that facilitate the reuse of software across different computing environments. However, many biologists and other researchers find command line tools such as Singularity unfamiliar and do not feel productive when using software via the command line. We present a graphical user interface that allows biologists without programming experience to interact with containerized software. We evaluate the feasibility of our approach with software used at the TRR156. Supplementary information Colony can be freely downloaded on its project page: https://clipc-jpg.github.io/ColonyWebsite/. The Colony launcher’s code is MIT-licensed and freely available at: https://github.com/clipc-jpg/Colony. All related assets can be found at: https://doi.org/10.7910/DVN/Z3OTWY.

The AlphaFoldDB Structure Extractor (https://project.iith.ac.in/sharmaglab/alphafoldextractor/) is an open-access web server and API toolkit designed to facilitate the bulk download of predicted protein structures from the AlphaFold Database using well-known accession formats. Addressing the current limitations in extracting structures beyond a restricted list of model organisms and a threshold number, this tool accepts diverse sequence and structure input identifiers, such as NCBI Taxonomy ID, RefSeq accessions, locus tags (old and new), and UniProt or AlphaFold accessions for structure retrieval. Users can download structure files in PDB, mmCIF, bCIF, or/and PAE JSON formats using any of the above-mentioned input accessions as input. The tool also generates an accompanying ID mapping file to trace input identifiers back to standard accession numbers and reports unmapped IDs separately. Users can also perform just the ID mapping in case they do not require the structure coordinate files. An API methodology is also provided for programmatic access, enabling integration into bioinformatics pipelines. We have tested the tool using several randomly selected accessions (individual inputs and up to 5000 input accessions) of each type from NCBI RefSeq and Taxonomy Databases, UniProt Database and AlphaFold Database. Overall, AlphaFoldDB Structure Extractor streamlines the structure procurement process from AlphaFold database, empowering researchers in structural and functional genomics with minimal computational expertise.

High-grade gliomas (HGG) are highly aggressive tumors, which are predominately fatal for adults and pediatric patients. Identifying cancer-selective therapeutic targets remains a critical unmet need. The overexpression of endoplasmic reticulum (ER) chaperones in various cancers is well documented. Moreover, tumor cells exhibit an atypical surface expression of ER chaperones, suggesting the potential for selective targeting. Our study examined the differences in the mRNA, total protein, and surface expression levels of seven key ER chaperones, compared with those in non-neoplastic samples. Notably, a poor correlation was found between mRNA, protein, and surface protein levels, underscoring the limitations of transcriptomics alone in target discovery. We also highlight the limitations of surfaceome studies which exclude noncanonical membrane proteins, such as ectopically expressed ER chaperones, which often escape detection by conventional bioinformatic pipelines. For the first time, this study advances our understanding of the surface expression of ER chaperones in both adult and pediatric HGG. Our findings highlight the importance of surfaceome analysis in the discovery of cancer selective targets against this devastating disease.

Heat stress poses a significant global challenge to sustainable livestock production, leading to detrimental impacts on animal production and welfare. Reduced appetite and increased body temperature further disrupt the gastrointestinal microbial ecosystem of heat-stressed animals, altering nutrient digestion and affecting host production. However, reported heat-stress-associated microbes have varied across studies, partly due to inconsistencies in microbiota analysis pipelines and taxonomic levels reported. In this study, to identify consistent rumen microbial taxa influenced by heat stress and evaluate potential of rumen microbiota in heat stress prediction, we collected publicly available raw 16S rRNA gene amplicon sequencing data of rumen fluid samples from lactating Holstein cattle housed in thermoneutral or heat stress condition from eight studies, analyzed their microbial composition using a consistent bioinformatic pipeline, and built machine learning models with the rumen microbiota profile to predict heat stress. Important rumen microbial taxa were selected using Boruta (a feature selection algorithm to identify important features) as potential biomarkers to predict heat stress, such as lactate-producing bacteria Lactobacillales, fiber-degrading bacteria Ruminococcaceae UCG-001, and methanogenic archaea Methanomicrobium. Additionally, the random forest model using the available animal factors and relative abundance of rumen microbial taxa showed a much higher performance for heat stress prediction, compared to the model without rumen microbiota profile (Area Under the Curve: 0.851 vs. 0.440). This study confirmed a distinct rumen microbiota signature in heat-stressed lactating Holstein cattle and identified specific rumen microbial taxa as potential biomarkers that could be targeted to mitigate heat-stress responses in dairy cows.

Inborn errors of immunity (IEIs) constitute a diverse group of more than 500 disorders resulting from pathogenic variants in over 500 causative genes, with most being monogenic diseases. The use of exome sequencing based on next-generation sequencing technologies has significantly advanced the discovery of causative variants underlying IEIs and has achieved diagnostic yields of up to 40%. Despite these advances, a substantial proportion of patients still remain genetically undiagnosed due to limitations in detecting deep intronic or structural variants. Accordingly, we applied whole-genome sequencing to a cohort of patients suspected of IEIs in order to evaluate its diagnostic yield and capacity to identify novel structural genomic alterations. We analyzed data from 25 probands presenting with suspected IEIs based on clinical features, who were enrolled through the National Bio-Big Data Program's whole genome sequencing (WGS) project at Samsung Medical Center, spanning July 2020 to February 2022. The study utilized a stepwise analytical protocol involving initial candidate gene panel analysis for detecting small variants, subsequent investigation of structural variants, and then a genotype-driven approach utilizing in-house bioinformatics pipelines. All identified variants were assessed for pathogenicity in accordance with the 2015 ACMG/AMP guidelines for the interpretation of sequence variants. Causative variants were detected in 10 (40%) probands using candidate gene panel analysis, which included BTK, CYBB, DKC1, DNAH11, DNAH5, IL2RG, NFKB2, PIK3CD and SH2D1A. Genotype-driven analysis identified pathogenic variants in two (8%) probands involving NF1 and PTPN11, while an additional five (20%) probands were found to have structural variants, including BTK, LRBA and SH2D1A. In total, genetic analysis revealed causative variants in 60% of patients. Variants of uncertain significance were identified in four cases among three probands (12%). WGS facilitated the robust identification of causative genetic variants, including complex structural changes. These results suggest that employing WGS in patients suspected of IEIs could provide additional diagnostic yield.