Bioinformatics (Oxford, England)最新文献

Motivation: Accurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells.

Results: scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3D visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks-Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling-enabling integrative multi-omic analyses of expressed variation.

Availability: scSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Chemical genomics is a powerful high-throughput approach to systematically link phenotypes to genotypes. However, the vast datasets generated remain challenging to explore due to the lack of integrated, interactive tools for visualisation and analysis. Existing workflows often require multiple independent software tools, limiting data accessibility and collaboration. Therefore, we created a user-friendly platform that enables efficient exploration and sharing of chemical genomics data.

Results: We developed ChemGenXplore, a web-based Shiny application designed to streamline the visualisation and analysis of chemical genomic screens. It offers two primary functionalities: one for exploring pre-implemented datasets and another for analysing user-uploaded datasets. ChemGenXplore enables users to visualise phenotypic profiles, assess gene-gene and condition-condition correlations, perform GO and KEGG enrichment analysis, and generate customisable, interactive heatmaps. To further support collaborative research, ChemGenXplore also facilitates the comparative analysis of chemical genomic and other omics datasets. By consolidating these features into a single interactive and accessible tool, ChemGenXplore facilitates data sharing, enhances reproducibility, and promotes collaboration within the research community.

Availability: ChemGenXplore is freely accessible as a web application at https://chemgenxplore.kaust.edu.sa/. Source code and documentation, including instructions for local installation, are provided on GitHub (https://github.com/Hudaahmadd/ChemGenXplore). A Docker image is also available on DockerHub (https://hub.docker.com/r/hudaahmad/chemgenxplore) to ensure reproducibility and simplify installation.

Contact: example@example.org.

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Accurate in silico identification of B-cell epitope residues is crucial for antibody design and structure-guided vaccine development. Although recent protein language models and structure-aware methods can capture spatial information of tertiary structure when generating residue embeddings, most existing epitope predictors use these embeddings to perform classification for individual residues one by one, without enforcing spatial continuity for reported epitope residues. Such methods often result in biologically implausible predictions because B-cell epitope residues always cluster together on the antigen surface.

Results: We present RoBep, a region-oriented B-cell epitope predictor that explicitly models the spatial clustering of epitope residues. RoBep introduces a novel region constraint mechanism and combines the advanced protein language model ESM-Cambrian with an equivariant graph neural network. Our method outperforms existing structure-based methods on the benchmark dataset, demonstrating improvements of 26%, 45%, 13%, and 43% in F1, MCC, AUPR, and AUROC0.1, respectively. In addition to residue-level predictions, RoBep can also provide antibody-antigen binding regions. Importantly, the predicted epitope residues are ensured to be spatially compact, enhancing biological plausibility and practical relevance for immunotherapeutic design.

Availability: A user-friendly website for using RoBep is provided at https://huggingface.co/spaces/NielTT/RoBep. All datasets, source code used in this work, and implementation instructions of the website are publicly available at https://github.com/YitaoXU/RoBep.

Motivation: Molecular mimicry is used by pathogens to evade the host immune system and manipulate other host cellular processes. It is often mediated by short motifs in non-homologous proteins, whose detection challenges the sensitivity and specificity of existing bioinformatics tools.

Results: We present mimicDetector, a k-mer-based pipeline for identifying protein-level molecular mimicry between pathogens and their hosts. Applied to 17 globally important pathogens, mimicDetector identified a broad and biologically plausible set of mimicry candidates, including helminth proteins mimicking components of the human complement system and a Leishmania infantum mimic of Reticulon-4, a regulator of immune cell recruitment.

Availability: mimicDetector is freely available at https://github.com/kayleerich/mimicDetector/, implemented in Python and Snakemake, and compatible with Unix-based systems.

Supplementary information: Data related to the results are incorporated into the article and online supplementary material available at Bioinformatics online.

Motivation: Malaria, caused by Plasmodium parasites, imposes a significant public health burden. While Plasmodium falciparum remains the primary target of elimination strategies due to its high mortality rate, lesser-known species such as P. malariae, P. vivax, and P. knowlesi continue to contribute to substantial human morbidity. Genomic approaches, including whole-genome sequencing, offer powerful tools for understanding the biology, transmission, and emerging drug resistance of these neglected Plasmodium species. However, there is an urgent need for informatic tools to summarise and visualise the high-dimensional and complex genomic data generated.

Results: We developed Malaria-GENOMAP, a user-friendly web-based tool, which integrates genomic variant data, such as allele frequencies, with geographical maps and chromosome-wide to gene views for in-depth exploration. The tool includes variation from P. knowlesi (n = 139), P. malariae (n = 158), P. ovale curtisi (n = 36), P. ovale wallikeri (n = 47), P. simium (n = 38), and P. vivax (n = 1,359). It enables the investigation of population structure, geographic associations of mutations, and putative drug resistance markers, offering valuable insights for malaria control efforts.

Availability: Malaria-GENOMAP is available online at https://genomics.lshtm.ac.uk/malaria-genomaps/#/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Scientific software packages impose persistent maintenance costs due to dependency churn, version incompatibilities, and bug triage, even when the underlying algorithms are stable and well described. At the same time, peer-reviewed publications already function as the canonical record of many computational methods, yet translating narrative method descriptions into usable code remains labor intensive and error prone. Recent advances in large language models (LLMs) raise the question of whether published articles alone can serve as sufficient specifications for on-demand code generation, potentially reducing reliance on continuously maintained libraries.

Results: We systematically evaluated state-of-the-art LLMs by tasking them with implementing core algorithms using only the original scientific publications as input. Across a diverse benchmark including random forests, batch correction methods, gene regulatory network inference, and gene set enrichment analysis, we show that modern LLMs can frequently reproduce package-level functionality with performance indistinguishable from established libraries. Failures and discrepancies primarily arose when manuscripts underspecified implementation details or data structures, rather than from limitations in model reasoning. These results demonstrate that literature-driven code generation is already feasible for many well-specified algorithms, while also exposing where current publication standards hinder reproducibility.

Availability: All prompts, generated code, evaluation scripts, and benchmark datasets are publicly available at https://github.com/xomicsdatascience/articles-to-code.

Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: CatsCradle is an R package for single-cell analysis that exploits the duality between cells and the genes they express. Our package provides tools to cluster genes, visualise relationships between them, and to explore relationships between gene clusters (programmes) and cell clusters (cell types).

Availability and implementation: CatsCradle is available freely as an R Bioconductor package (https://bioconductor.org/packages/CatsCradle) and interfaces directly with Seurat and SingleCellExperiment data structures.

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Recombinant protein expression can be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications. We introduce RP3Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli. RP3Net utilizes the most recent protein and genomic foundational models. A curated dataset of internal experimental results from AstraZeneca (AZ) and publicly available data from the Structural Genomics Consortium (SGC) was used for training, validation and testing of RP3Net.

Results: RP3Net achieves an increase in Area Under Receiver Operator Curve (AUROC) of 0.15, compared to a baseline model. When experimentally validated on an independent, prospective, manually selected set of 97 constructs, RP3Net outperformed currently available models, with an AUROC of 0.83, delivering accurate predictions in 77% of the cases, and correctly identifying successfully expressing constructs in 92% of cases.

Availability: The model, along with installation and running instructions, is available under an MIT license at https://github.com/RP3Net/RP3Net, DOI 10.5281/zenodo.17243498.

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: The presence or absence of some genes in a genome can influence whether other genes are likely to be present or absent. Understanding these gene co-occurrence and avoidance patterns reveals fundamental principles of genome organisation, with applications ranging from evolutionary reconstruction to rational design of synthetic genomes.

Implementations: PanForest, presented here, uses random forest classifiers to predict the presence and absence of genes in genomes from the set of other genes present. Performance statistics output by PanForest reveal how predictable each gene's presence or absence is, based on the presence or absence of other genes in the genome. Further, PanForest produces statistics indicating the importance of each gene in predicting the presence or absence of each other gene. The PanForest software can run serially or in parallel, thereby facilitating the analysis of pangenomes at Network of Life scale.

Results: A pangenome of 12,741 accessory genes in 1,000 Escherichia coli genomes was analysed in around 5 hours using 8 processors. To demonstrate PanForest's utility, we present a case study and show that certain genes associated with resistance to antimicrobial drugs reliably predict the presence or absence of other genes associated with resistance to the same drug. Further, we highlight several associations between those genes and others not known to be associated with antimicrobial resistance (AMR), or associated with resistance to other drugs. We envisage PanForest's use in studies from multiple disciplines concerning the dynamics of gene distributions in pangenomes ranging from biomedical science and synthetic biology to molecular ecology.

Availability: The software if freely available with a full manual and can be found with at www.github.com/alanbeavan/PanForest DOI: https://doi.org/10.5281/zenodo.17865482.

Supplementary information: Supplementary data are available at Bioinformatics online.