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24 records · Page 2Linked to original sources

PubMed2025

Single-Cell Splicing Isoform Atlas of the Adult Human Heart and Heart Failure.

BACKGROUND: Alternative splicing plays crucial roles in normal heart development and cardiac disease by influencing protein-coding sequences, functional domains, and molecular networks. However, a detailed characterization of the human heart isoform landscape remains incomplete. METHODS: Leveraging long-read single-nucleus RNA sequencing and computational analysis, we dissected full-length isoform heterogeneities, expression patterns, and usage shifts across cell types, cell states, and cardiac conditions of the adult left ventricle. We applied in silico approaches to assess the functional relevance of identified isoforms; validated isoform compositions of representative cardiac genes using reverse transcription quantitative polymerase chain reaction and targeted amplicon sequencing; and developed a web server for interactive navigation of our results. RESULTS: The data revealed that isoform heterogeneity is widespread in the cardiac cellular system, serving as a posttranscriptional buffer mechanism that calibrates the molecule reservoirs in human hearts. In healthy left ventricles, ≈30% of cell type-specific genes were polyform, using multiple isoforms tailored to cell type-specific programs. Among ubiquitously expressed genes, >300 showed differential isoform usage with cell type specificity in normal hearts. Comparisons of cardiomyocytes across conditions uncovered 379 genes with marked isoform usage shifts, most of which are predicted to change protein coding outcomes through direct changes in protein coding sequences and switches between intron retention and non-protein-coding biotypes. In contrast, cell state-specific programs tend to operate on monoform genes associated with changes among cell states. In addition, our data revealed heart failure-associated differential isoform usage events in stromal and immune cell types in the cardiac microenvironment. CONCLUSIONS: We present a comprehensive atlas of splicing isoforms in the normal adult heart and heart failure through long-read single-nucleus RNA sequencing and computational analyses. The results suggest crucial roles of isoforms in buffering core cellular programs and contributing to disease-associated cell states. The full-length details of these cell-specific isoforms serve as an important reference for downstream translational and mechanistic studies and are available on our online data portal at https://github.com/gaolabtools/heart-isoform-atlas.

Humans↗

PubMed2025

NAVIP: Unraveling the influence of neighboring small sequence variants on functional impact prediction.

Once a suitable reference sequence has been generated, intra-species variation is often assessed by re-sequencing. Variant calling processes can reveal all differences between strains, accessions, genotypes, or individuals. These variants can be enriched with predictions about their functional implications based on available structural annotations, i.e., gene models. Although these functional impact predictions on a per-variant basis are often accurate, some challenging cases require the simultaneous incorporation of multiple adjacent variants into this prediction process. Examples include neighboring variants which modify each other's functional impact. The Neighborhood-Aware Variant Impact Predictor (NAVIP) considers all variants within a given protein coding sequence when predicting the effect. As a proof of concept, variants between the Arabidopsis thaliana accessions Columbia-0 and Niederzenz-1 were annotated. NAVIP is freely available on GitHub (https://github.com/bpucker/NAVIP) and accessible through a web server (https://pbb-tools.de).

Arabidopsis↗

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

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 greatly enhances 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/griffithlab/dgidb-mcp-server and includes instructions for accessing the server through the Claude desktop app.

Journal Article↗

PubMed2025

Evaluating 12 automated, whole-genome sequencing analysis pipelines for Mycobacterium tuberculosis complex: a comparative study.

BACKGROUND: Reliance on complex, custom-built bioinformatics pipelines is a barrier to the implementation of whole-genome sequencing (WGS) of Mycobacterium tuberculosis in high-burden settings in some low-income and middle-income countries (LMICs). Automated analysis pipelines could address this inequity in access to WGS-based diagnostics and surveillance. This study aimed to systematically evaluate the performance and usability of publicly available WGS pipelines for M tuberculosis. METHODS: We identified automated M tuberculosis WGS analysis pipelines through searches of PubMed and GitHub from database inception up to Aug 31, 2024. Accuracy, cost, accessibility, and scalability were assessed for each pipeline. We evaluated the accuracy of genotypic drug susceptibility testing (gDST) using publicly available sequences with phenotypic susceptibility data for 12 antituberculosis drugs. We estimated pooled sensitivity and specificity for each pipeline, across all drugs, by conducting a bivariate meta-analysis, with random effects representing between-drug variability. Lineage classifications were compared, and a previously epidemiologically well-characterised dataset was used to compare measures of genomic relatedness. FINDINGS: Among 28 candidate pipelines, 16 were excluded as they were unmaintained and inexecutable. 12 pipelines (11 compatible with Illumina and four compatible with Nanopore), all free to use, were included for evaluation. Six pipelines processed and stored data remotely, but for five of these six, scalability was limited by the need to upload sequences through web portals. For local processing pipelines, scalability was dependent on substantial local computational resources, data storage capacity, and command-line interfaces that limited user-friendliness. Only one of six remote-processing pipelines removed human DNA sequences before server upload. gDST was similarly accurate across ten of 11 Illumina-compatible pipelines and three of four Nanopore-compatible pipelines. All pipelines classified the main lineages consistently, although there were differences at sublineage resolution. Outputs from three of four pipelines reporting genomic relatedness were compatible with commonly cited single nucleotide polymorphism difference thresholds. INTERPRETATION: Numerous automated analysis pipelines capable of enhancing equity in M tuberculosis WGS are available. Given the overall similarities between the pipelines evaluated in this study in terms of gDST performance, lineage classification, and genomic relatedness inference, non-functional attributes such as availability, accessibility, scalability, and privacy could represent the point of difference for prospective users in LMICs with a high burden of tuberculosis. FUNDING: The Rhodes Trust, Wellcome, Ellison Institute of Technology, and the UK National Institute for Health and Care Research Oxford Biomedical Research Centre.

Mycobacterium tuberculosis↗

PubMed2026

SimpleMicrobiome: An integrated web-based platform for streamlined microbiome data analysis and visualization.

Microbiome studies require multiple analytical steps after initial sequence processing. These steps commonly include data harmonization, preprocessing, taxonomic profiling, diversity analysis, differential abundance testing, predictive modeling, network inference, and preparation of publication-ready outputs. Although robust packages are available for many of these tasks, routine use often depends on command-line workflows, repeated data reformatting, and method-specific scripting. These requirements can limit accessibility for experimental researchers and complicate consistent analysis across interdisciplinary teams. We developed SimpleMicrobiome, a web-based R Shiny platform that integrates established microbiome analysis methods into a single interactive downstream workflow. The application accepts standard abundance, taxonomy, and metadata tables, supports interactive preprocessing and sample filtering, and provides modules for taxa profile visualization, alpha and beta diversity analysis, ANCOM-BC2 and MaAsLin2 differential abundance testing, Random Forest modeling with SHAP-based interpretation, microbial association network inference using SparCC and SPIEC-EASI through NetCoMi, correlation heatmaps, and dbRDA/CAP-style association biplots. The platform is implemented as a modular Shiny application so that preprocessing choices are propagated across downstream analyses, results can be exported as figures and tables, and the same application can be run through the public server, source-code installation, or a Docker image. SimpleMicrobiome consolidates major downstream microbiome analysis tasks in an accessible browser-based environment while retaining links to established analytical frameworks. The platform may reduce technical barriers for non-programming users, improve consistency across exploratory and reporting-oriented analyses, and support collaborative microbiome research. The public application is available at https://simplemicrobiome.mglab.org, the source code is available at https://github.com/yjcho2252/SimpleMicrobiome, and a Docker image for local deployment is available at https://hub.docker.com/r/mglab2252/simplemicrobiome.

differential abundance↗