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PubMed2025

BTS: a scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across >1000s of omics datasets.

MOTIVATION: statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results. RESULTS: We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for (i) identifying affected cell types and functional elements (context-mapping) and (ii) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100× more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases. AVAILABILITY AND IMPLEMENTATION: Docker image is available at https://hub.docker.com/r/wanglab/bts with preinstalled BTS R package (https://bitbucket.org/wanglab-upenn/BTS-R) and BTS GWAS summary statistics analysis pipeline (https://bitbucket.org/wanglab-upenn/bts-pipeline).

Genome-Wide Association Study↗
PubMed2025

Pre-Meta: priors-augmented retrieval for LLM-based metadata generation.

MOTIVATION: While high-throughput sequencing technologies have dramatically accelerated genomic data generation, the manual processes required for dataset annotation and metadata creation impede the efficient discovery and publication of these resources across disparate public repositories. Large language models (LLMs) have the potential to streamline dataset profiling and discovery. However, their current limitations in generalizing across specialized knowledge domains, particularly in fields such as biomedical genomics, prevent them from fully realizing this potential. This article presents Pre-Meta, an LLM-agnostic and domain-independent data annotation pipeline with an enriched retrieval procedure that leverages related priors-such as pre-generated metadata tags and ontologies-as auxiliary information to improve the accuracy of automated metadata generation. RESULTS: Validated using five selected metadata fields sampled across 1500 papers, the Pre-Meta assisted annotation experiment-without finetuning and prompt optimization-demonstrates a systemic improvement in the annotation task: shown through a 23%, 72%, and 75% accuracy gain from conventional RAG adoptions of GPT-4o mini, Llama 8B, and Mistral 7B respectively. AVAILABILITY AND IMPLEMENTATION: The code, data access, and scripts are available at: https://github.com/SINTEF-SE/LLMDap.

Metadata↗
PubMed2025

DeNoFo: a file format and toolkit for standardized, comparable de novo gene annotation.

MOTIVATION: De novo genes emerge from previously non-coding regions of the genome, challenging the traditional view that new genes primarily arise through duplication and adaptation of existing ones. Characterized by their rapid evolution and their novel structural properties or functional roles, de novo genes represent a young area of research. Therefore, the field currently lacks established standards and methodologies, leading to inconsistent terminology and challenges in comparing and reproducing results. RESULTS: This work presents a standardized annotation format to document the methodology of de novo gene datasets in a reproducible way. We developed DeNoFo, a toolkit to provide easy access to this format that simplifies annotation of datasets and facilitates comparison across studies. Unifying the different protocols and methods in one standardized format, while providing integration into established file formats, such as fasta or gff, ensures comparability of studies and advances new insights in this rapidly evolving field. AVAILABILITY AND IMPLEMENTATION: DeNoFo is available through the official Python Package Index (PyPI) and at https://github.com/EDohmen/denofo. All tools have a graphical user interface and a command line interface. The toolkit is implemented in Python3, available for all major platforms and installable with pip and uv.

Software↗
PubMed2025

Automating candidate gene prioritization with large language models: from naive scoring to literature-grounded validation.

MOTIVATION: Identifying promising therapeutic targets from thousands of genes in transcriptomic studies remains a major bottleneck in biomedical research. While large language models (LLMs) show potential for gene prioritization, they suffer from hallucination and lack systematic validation against expert knowledge. RESULTS: The framework identified 609 sepsis-relevant genes with >94% filtering efficiency, demonstrating strong enrichment for inflammatory pathways including TNF-α signaling, complement activation, and interferon responses. Literature validation yielded 30 ultra-high confidence therapeutic candidates, including both established sepsis genes (IL10, TREM1, S100A9, NLRP3) and novel targets warranting investigation. Benchmark validation against expert-curated databases achieved 71.2% recall, with systematic correlation between computational confidence and evidence quality. The final candidate set balanced discovery (11 novel genes) with validation (19 known genes), maintaining biological coherence throughout the filtering process. This framework demonstrates that rigorous methodology can transform unreliable LLM outputs into systematically validated biological insights. By combining computational efficiency with literature grounding, the approach provides a practical tool for prioritizing experimental validation efforts. The modular design enables adaptation to other diseases through knowledge base substitution, offering a systematic approach to literature-guided biomarker discovery. AVAILABILITY AND IMPLEMENTATION: We developed a two-stage computational framework that combines LLM-based screening with literature validation for systematic gene prioritization. Starting with 10 824 genes from the BloodGen3 repertoire, we applied multi-criteria evaluation for sepsis relevance, followed by retrieval-augmented generation using 6346 curated sepsis publications. A novel faithfulness evaluation system verified that LLM predictions aligned with retrieved literature evidence. Source code and implementation details are available at https://github.com/taushifkhan/llm-geneprioritization-framework, vector database at https://doi.org/10.5281/zenodo.15802241, and Interactive demonstration at https://llm-geneprioritization.streamlit.app/.

Humans↗
PubMed2025

raxtax: a k-mer-based non-Bayesian taxonomic classifier.

MOTIVATION: Taxonomic classification in biodiversity studies is the process of assigning the anonymous sequences of a marker gene (barcode) or whole genomes (metagenomics) to a specific lineage using a reference database that contains named sequences in a known taxonomy. This classification is important for assessing the diversity of biological systems. Taxonomic classification faces two main challenges: first, accuracy is critical as errors can propagate to downstream analysis results; and second, the classification time requirements can limit study size and study design, in particular when considering the constantly growing reference databases. To address these two challenges, we introduce raxtax, an efficient, novel taxonomic classification tool for barcodes that uses common k-mers between all pairs of query and reference sequences. We also introduce two novel uncertainty scores which take into account the fundamental biases of reference databases. RESULTS: We validate raxtax on three widely-used empirical reference databases and show that it is 2.7-100 times faster than competing state-of-the-art tools on the largest database while being equally accurate. In particular, raxtax exhibits increasing speedups with growing query and reference sequence numbers compared to existing tools (for 100 000 and 1 000 000 query and reference sequences overall, it is 1.3 and 2.9 times faster, respectively), and therefore alleviates the taxonomic classification scalability challenge. AVAILABILITY AND IMPLEMENTATION: raxtax is available at https://github.com/noahares/raxtax under a CC-NC-BY-SA license. The scripts and summary metrics used in our analyses are available at https://github.com/noahares/raxtax_paper_scripts. The source code, sequence data, and summarized results of the analyses are available at https://doi.org/10.5281/zenodo.15057027.

Software↗
PubMed2025

Benchmarking methods for measuring biosynthetic gene cluster similarity and determination of gene cluster families.

MOTIVATION: Natural products are often produced by a set of biosynthetic enzymes that are encoded by genes clustered together in the producer's genome, referred to as a biosynthetic gene cluster (BGC). The ability to compare and cluster BGCs is essential for several applications, including predicting which bacteria will make a known product and assessing the potential diversity of natural products produced by a set of bacteria. There are multiple methods for comparing and clustering BGCs based on their similarity, but there has been a lack of investigation into how strongly BGC similarity relates to product structural similarity and how these methods perform relative to each other. RESULTS: Using publicly available databases, we developed a benchmark dataset to assess how well different BGC similarity metrics correlate with the structural similarity of their products and how well these methods cluster BGCs. We found that all methods showed moderate correlation between BGC and structural similarity, with correlations improving for more similar BGCs and varying significantly by BGC biosynthetic class. Analysis of outliers revealed some outliers were due to mistakes or omissions in public datasets, while others represented deviation between BGC similarity and product structural similarity. All methods generally performed better on clustering metrics, with BiG-SCAPE performing the best after errors in the public datasets had been corrected. AVAILABILITY AND IMPLEMENTATION: Scripts and data required to reproduce the results are available at https://github.com/aswalker-lab/BGC-clustering-benchmark and processed similarity, clusters, and scaffolds are also available at https://huggingface.co/datasets/allie-walker/BGC-clustering-benchmark. Code is also available at Zenodo: 10.5281/zenodo.17373546.

Multigene Family↗
PubMed2025

polars-bio-fast, scalable, and out-of-core operations on large genomic interval datasets.

MOTIVATION: Genomic studies very often rely on computationally intensive analyses of relationships between features, which are typically represented as intervals along a 1D coordinate system (such as positions on a chromosome). In this context, the Python programming language is extensively used for manipulating and analyzing data stored in a tabular form of rows and columns, called a DataFrame. Pandas is the most widely used Python DataFrame package and has been criticized for inefficiencies and scalability issues, which its modern alternative-Polars-aims to address with a native backend written in the Rust programming language. RESULTS: polars-bio is a Python library that enables fast, parallel and out-of-core operations on large genomic interval datasets. Its main components are implemented in Rust, using the Apache DataFusion query engine and Apache Arrow for efficient data representation. It is compatible with Polars and Pandas DataFrame formats. In a real-world comparison (107 versus 1.2×106 intervals), our library runs overlap queries 6.5×, nearest queries 15.5×, count_overlaps queries 38×, and coverage queries 15× faster than Bioframe. On equally sized synthetic sets (107 versus 107), the corresponding speedups are 1.6×, 5.5×, 6×, and 6×. In streaming mode, on real and synthetic interval pairs, our implementation uses 90× and 15× less memory for overlap, 4.5× and 6.5× less for nearest, 60× and 12× less for count_overlaps, and 34× and 7× less for coverage than Bioframe. Multi-threaded benchmarks show good scalability characteristics. To the best of our knowledge, polars-bio is the most efficient single-node library for genomic interval DataFrames in Python. AVAILABILITY AND IMPLEMENTATION: polars-bio is an open-source Python package distributed under the Apache License available for major platforms, including Linux, macOS, and Windows in the PyPI registry. The online documentation is https://biodatageeks.org/polars-bio/ and the source code is available on GitHub: https://github.com/biodatageeks/polars-bio and Zenodo: https://doi.org/10.5281/zenodo.16374290. are available at Bioinformatics online.

Software↗
PubMed2026

Pretraining improves prediction of genomic datasets across species.

MOTIVATION: Recent studies suggest that deep neural network models trained on thousands of human genomic datasets can accurately predict genomic features, including gene expression and chromatin accessibility. However, training these models is computation- and time-intensive, and datasets of comparable size do not exist for most other organisms. RESULTS: Here, we identify modifications to an existing state-of-the-art model that improve model accuracy while reducing training time and computational cost. Using this streamlined model architecture, we investigate the ability of models pretrained on human genomic datasets to transfer performance to a variety of different tasks. Models pretrained on human data but fine-tuned on genomic datasets from diverse tissues and species achieved significantly higher prediction accuracy while significantly reducing training time compared to models trained from scratch, with Pearson correlation coefficients between experimental results and predictions as high as 0.8. Further, we found that including excessive training tasks decreased model performance and that this decrease could be partially but not completely rescued by fine-tuning. Thus, simplifying model architecture, applying pretrained models, and carefully considering the number of training tasks may be effective and economical techniques for building new models across data types, tissues, and species. AVAILABILITY AND IMPLEMENTATION: Code is available on GitHub and Figshare: https://github.com/optimizedlearning/genomicsML, https://doi.org/10.6084/m9.figshare.31796116.

Genomics↗
PubMed2026

Integrating plant phenotypic and genotypic data in the AGENT project: a BrAPI service implementation.

MOTIVATION: The AGENT project established a network of actively cooperating European genebanks, integrating genomic and phenotypic data from accessions of wheat and barley. Due to specific storage demands for phenotypic and genotypic data, the project used separate database instances and backend technologies to manage integrated phenotypic and genotypic data. RESULTS: We discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI. We examine how the consistent mappability of genebank data to the BrAPI model can enable the implementation of effective services. The advantages of BrAPI in transparently linking distributed data entities through embedded, unique identifiers are highlighted. We present a technical solution involving a BrAPI proxy, which combines and merges separate BrAPI endpoints. Finally, we demonstrate the AGENT BrAPI implementation with an illustrative example that validates a suggested SNP for a trait from the literature by linking phenotypic, genotypic and passport data. AVAILABILITY AND IMPLEMENTATION: The BrAPI proxy implementation and documentation is available at the Python Package Index (https://pypi.org/project/brapi-proxy) and archived in Zenodo (doi: 10.5281/zenodo.19436445). SUPPLEMENTARY INFORMATION: A Jupyter Notebook file for the validation example using a marker-trait relationship found in the literature.

Phenotype↗
PubMed2026

GRNContext: an interactive web platform for contextualized gene regulatory networks visualization across human cancers.

SUMMARY: While current Gene Regulatory Network (GRN) databases provide comprehensive reference maps of potential interactions between transcription factors and target genes, they do not specify which regulatory interactions are active within specific biological contexts. This limitation is particularly critical in cancer, where transcriptional programs are inherently tissue-specific. To address this gap, we developed GRNContext, an interactive web platform designed for the visualization, exploration, and comparative analysis of gene regulatory networks contextualized across 33 cancer types from The Cancer Genome Atlas (TCGA). Our approach uses the TFLink human reference GRN as a starting point and integrates TCGA transcriptomic profiles to infer cancer-specific regulatory activity. Regulatory relevance was assessed using complementary machine learning and statistical methods, which were unified into a consensus score to prioritize and filter the most relevant candidate regulators for each target gene. By providing both curated context-specific GRNs and a user-friendly platform, GRNContext constitutes a comprehensive and accessible resource that supports mechanistic investigations, hypothesis generation, and translational research focused on transcriptional regulation in cancer. AVAILABILITY AND IMPLEMENTATION: GRNContext is supported by all major browsers and freely available on the web at https://apps.cienciavida.org/grncontext. It is implemented as a client-server web application featuring a FastAPI backend and a React frontend utilizing Cytoscape.js for interactive network visualization, all containerized via Docker for cross-platform compatibility.

Humans↗
PubMed2026

PhyloNaP: a user-friendly database of phylogeny for natural product-producing enzymes.

SUMMARY: Phylogenetic analysis is widely used to predict enzyme function, yet building annotated and reusable trees is labor-intensive and requires extensive knowledge about the specific enzymes. Existing resources rarely cover biosynthetic enzymes and lack the context needed for meaningful analysis. We present PhyloNaP, the first large-scale resource dedicated to phylogenies of biosynthetic enzymes. PhyloNaP provides ∼51 000 annotated and interactive trees enriched with chemical, functional, and taxonomic information. Users can classify their own sequences via phylogenetic placement, enabling functional inference in an evolutionary context. A contribution portal allows the community to submit curated trees. By combining scale, breadth of annotation, and interactive functionality, PhyloNaP fills a major gap in bioinformatics resources for enzyme discovery and annotation, with immediate applications to secondary metabolism and beyond. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at https://phylonap.cs.uni-tuebingen.de.

Phylogeny↗
PubMed2026

Meta2DB: curated shotgun metagenomic feature sets and metadata for health state prediction.

SUMMARY: Meta2DB is a curated metagenomic and metadata database that provides structurally consistent microbiome taxonomy feature count tables for 13 897 samples across 84 studies, 23 disease states, and 34 geographical locations. All samples were uniformly processed using a streamlined metagenomic classification pipeline that employs a unique and comprehensive reference database indexed to contain all sequences across all kingdoms of life that were present in the NCBI Nucleotide (nt) database retrieved on 4 January 2023. This pipeline leverages high-performance computing (HPC) resources at Lawrence Livermore National Laboratory and was used to process 50TB of publicly available raw metagenomic sequence data. Extensive metadata curation was carried out through a combination of manual curation and automated parsing, producing a consistent inter-study metadata table specifically structured to facilitate training of ML models for prediction of human health. AVAILABILITY: Data is available at https://gdo-meta2db.llnl.gov/ and https://zenodo.org/records/17315984.

Metadata↗
PubMed2026

nf-core/magmap: Map metatranscriptomes to large collections of genomes.

SUMMARY: The lack of publicly available reference genomes has forced annotation of metatranscriptomes to either use direct alignment of sequence reads to reference databases or de novo assembly. As more and more natural environments are covered by metagenomic surveys, this is rapidly changing. This opens up the possibility of genome-resolved studies of prokaryotic metatranscriptomes by mapping to genomes from public repositories or metagenome-assembled genomes derived from the same environment. Here, we present the nf-core/magmap pipeline that provides a reproducible, easy-to-access, and well-documented workflow for selecting reference genomes, mapping to them, and quantifying features. Genomes can be drawn from public sources or originate from private collections. The pipeline is primarily aimed at prokaryotic communities but can, together with collections of reference mature gene sequences, also be applied to eukaryotes. AVAILABILITY AND IMPLEMENTATION: The nf-core/magmap pipeline is implemented in Nextflow and part of the nf-core collaboration. The pipeline is available at the nf-core website (https://nf-co.re/magmap) and GitHub (https://github.com/nf-core/magmap).

Software↗
PubMed2026

Searching the druggable genome using large language models.

SUMMARY: The druggable genome encompasses the genes that are known or predicted to interact with drugs. The Drug-Gene Interaction Database (DGIdb) provides an integrated resource for discovering and contextualizing these interactions, supporting a broad range of research and clinical applications. DGIdb is currently accessed through structured web interfaces and API calls, requiring users to translate natural-language questions into database-specific query patterns. To allow for the use of DGIdb through natural language, we developed the DGIdb Model Context Protocol (MCP) server, which allows large language models (LLMs) access to up-to-date information through the DGIdb API. We demonstrate that the MCP server improves an LLM's ability to answer questions requiring accurate, up-to-date biomedical knowledge drawn from structured external resources. AVAILABILITY AND IMPLEMENTATION: The DGIdb MCP server is detailed at https://github.com/dgidb/dgidb-mcp-server and includes instructions for accessing the server through the Claude desktop app.

Large Language Models↗
PubMed2026

An integrated culturomic and genomic database and analysis platform for methanogenic archaea.

Methanogenic archaea research is challenged by limited strain resources, fragmented genomic data, inconsistent genome quality, substantial uncultured lineages, and difficulties in laboratory culturing, hindering advances in biogas production, climate mitigation, and microbial ecology. These archaea play crucial roles in global carbon cycling and anaerobic environments, yet scattered data and unculturable strains limit systematic studies and applications. To address this, we created MethArDB (Methanogenic Archaeal Genome Database), a specialized database for methanogenic archaea, compiling 3919 genomes, 87 host-associated plasmids, and 42 phages, with standardized quality classifications (complete, scaffold, draft), protein sequences, and metadata on geography, habitats, metabolism, and inheritable elements. Integrated MethArCT (Methanogenic Archaeal Culturomics Toolkit) employs a dual-threshold orthologous/paralogous protein analysis to evaluate metabolic pathway completeness, predicting cultivation parameters and suggesting candidate cultivation strategies, including potential medium formulations and conditions, to support strain isolation. Overall, MethArDB and MethArCT form an integrated platform combining genomics and culturomics to facilitate methanogenic archaea research. Database URL:  http://methardb.cn.

Genome, Archaeal↗
PubMed2025

The GSA Family in 2025: A Broadened Sharing Platform for Multi-omics and Multimodal Data.

The Genome Sequence Archive family (GSA family) provides a comprehensive suite of database resources for archiving, retrieving, and sharing multi-omics data for the global academic and industrial communities. It currently comprises four distinct database members: the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa), the Genome Sequence Archive for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human), the Open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix), and the Open Biomedical Imaging Archive (OBIA, https://ngdc.cncb.ac.cn/obia). Compared to its 2021 version, the GSA family has expanded significantly by introducing a new repository, the OBIA, and by comprehensively upgrading the existing databases. Notable enhancements to the existing members include broadening the range of accepted data types, strengthening quality control systems, improving the data retrieval system, and refining data-sharing management mechanisms.

Humans↗
PubMed2025

Identification of key genes related to bone metastasis of breast cancer using bioinformatics methods and construction of a prognostic model.

Breast cancer (BC) ranks among the most prevalent cancers in females, with bone metastasis significantly compromising patients' quality of life and survival rates. Enhancing our comprehension of BC bone metastasis mechanisms at the molecular level holds promise for improving BC treatment and prognosis. Leveraging bioinformatics tools, we integrated multiple datasets, conducted comprehensive analyses across various databases, identified biomarkers associated with BC bone metastasis, and constructed a prognostic model. Firstly, 3 BC bone metastasis-related datasets were downloaded from gene expression omnibus, the data were merged, and batch effects were removed, followed by identification of differentially expressed genes (DEGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed using the STRING database to screen hub genes. Then, survival analysis of hub genes was performed using the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using key genes with survival differences, and the model was evaluated. Two hundred ninety-two DEGs were identified. Gene ontology and KEGG pathway enrichment analysis yielded 769 biological processes (BPs), 78 cellular components, 43 molecular functions, and 50 KEGG pathways. Fifteen hub genes were selected from the protein-protein interaction network. Survival analysis revealed 6 genes related to BC survival. The prognostic model identified 4 genes with important predictive value for BC prognosis. Our study utilized bioinformatics analysis to identify a series of DEGs related to BC bone metastasis. Based on further selection of hub genes, we constructed a relatively ideal prognostic model for BC, and identified 4 genes (DLGAP5, TPX2, PLK1, and CENPN) with valuable predictive value for BC prognosis.

Humans↗
PubMed2004

EnsMart: a generic system for fast and flexible access to biological data.

The EnsMart system (www.ensembl.org/EnsMart) provides a generic data warehousing solution for fast and flexible querying of large biological data sets and integration with third-party data and tools. The system consists of a query-optimized database and interactive, user-friendly interfaces. EnsMart has been applied to Ensembl, where it extends its genomic browser capabilities, facilitating rapid retrieval of customized data sets. A wide variety of complex queries, on various types of annotations, for numerous species are supported. These can be applied to many research problems, ranging from SNP selection for candidate gene screening, through cross-species evolutionary comparisons, to microarray annotation. Users can group and refine biological data according to many criteria, including cross-species analyses, disease links, sequence variations, and expression patterns. Both tabulated list data and biological sequence output can be generated dynamically, in HTML, text, Microsoft Excel, and compressed formats. A wide range of sequence types, such as cDNA, peptides, coding regions, UTRs, and exons, with additional upstream and downstream regions, can be retrieved. The EnsMart database can be accessed via a public Web site, or through a Java application suite. Both implementations and the database are freely available for local installation, and can be extended or adapted to 'non-Ensembl' data sets.

Animals↗