Abstract Summary DNA methylation microarrays enable cost-effective, high-throughput profiling of human and mouse samples and are widely used to study epigenetic alterations in cancer. Patient-Derived Xenograft (PDX) models complement this approach by preserving primary tumor characteristics while enabling in vivo studies of drug response and therapy resistance. However, it remains difficult to quantify mouse DNA contamination in PDX DNA methylation data generated using microarrays. We present XenoMix, a light-weight computational tool for quantifying mouse DNA contamination directly from DNA methylation data in PDX models. XenoMix leverages the high sequence homology between human and mouse genomes to detect and quantify unexpected signal contributions originating from mouse DNA. Using ground truth mixtures, XenoMix achieved Pearson’s correlation coefficients higher than 0.99. When applied to PDX data, we identified varying levels of mouse contamination originating from biological and technological variability. XenoMix establishes a critical quality control step in DNA methylation analysis of PDX models, and we show that the estimated contamination can be incorporated as a confounding variable in downstream analyses. Availability and Implementation XenoMix is available as a standalone R package from https://git.dkfz.de/b370/scepigen/XenoMix and as a module in the RnBeads software package. Supplementary information Supplementary data are available at Bioinformatics online.
Abstract Summary Deep learning can scale image-based plant phenotyping but typically targets predefined traits, whereas manual phenotypic description is more flexible but difficult to scale. We present Phenotype eXplorer Generative Plant Traits (pxGPT), a Python framework that converts multi-view plant images into structured phenotype records. The workflow combines free-text description, dataset-specific schema generation with human review, and schema-constrained information extraction. We evaluated the extraction stage on three Brassica datasets using 78,973 annotations from ten annotators who had no access to pxGPT predictions. For 38-45 qualitative traits per dataset, pxGPT-human agreement ranged from 0.572 to 0.712, compared with 0.620 to 0.739 agreement among human annotators. All 32,736 required trait fields were present; 93 values were empty, and no qualitative descriptor violations were recorded. For selected morphological structures, pooled false-positive rates were 0.5-8.0%, while sensitivity varied across structure types in one mature-plant dataset (37.5-100%). The curated schemas were applied unchanged with two commercial models and one locally hosted open-weight model, with differences in mean accuracy of 0.03 or less. In the evaluated Brassica datasets, these results support schema-guided extraction of visible plant traits without separate model training for each trait. Human-guided schema development offers a path to extend pxGPT to other crops and imaging conditions, with accuracy requiring validation in each new setting. Availability and Implementation The software is available at https://github.com/YaoChengLab/pxgpt and an archival version of the source code is available on Zenodo (https://doi.org/10.5281/zenodo.22931745). Curated schemas used in this study, annotation guides, phenotype records and human annotations are available at https://doi.org/10.5281/zenodo.22907554. Supplementary information Supplementary data are available at Bioinformatics online.
Abstract Motivation Microbiome–metabolome–disease pathways play a central role in host health, but studying them is limited by the data available. Large clinical microbiome cohorts often lack metabolomic measurements, while multi-omics studies with paired profiles are typically small or incompletely phenotyped. Combining such complementary datasets is challenging: the metabolite is not measured in the clinical cohort, microbiome data are high-dimensional and compositional, and log-ratio analyses depend on an arbitrarily chosen reference taxon. Results We propose a framework that integrates a large target cohort that has microbiome and outcome data with a smaller external study that has paired microbiome–metabolome profiles. A high-dimensional structural equation model jointly links the microbiome, the metabolome and the disease outcome. It allows for unmeasured confounding and groups microbes into functional categories. We address compositionality with the additive log-ratio (ALR) transformation, estimate parameters by penalized two-stage least squares, and test metabolite–disease associations with a regularized permutation test. A novel multiple-reference procedure treats each microbe in turn as the ALR reference. It combines the resulting p-values with the Cauchy combination test and classifies microbes by stability selection, which makes inference robust to the choice of reference. In simulations, the method controls the type I error rate, and its power increases with both target and external sample sizes. An application to inflammatory bowel disease, integrating the FRAN and iHMP cohorts, reveals biologically interpretable microbiome–metabolome–disease links. Availability and Implementation The method is implemented in the R package MMDP, freely available at https://github.com/lei-fang-stat/MMDP. Supplementary information Supplementary data are available at Bioinformatics online.
Abstract Motivation Differential gene expression (DGE) analysis is foundational for interpreting RNA sequencing data, but it conflates direct biological effects with correlations propagated through gene co-expression. Across three RNA sequencing datasets (including a genome-scale perturb-seq experiment), we find that only a small fraction of differentially expressed genes have direct effects on the trait of interest, while the majority are undirected or passengers whose associations are mediated through other genes. Results To distinguish direct effect genes, we introduce conditional differential gene expression (CDGE) analysis, a framework that tests for conditional rather than marginal association between each gene and the trait of interest. Implemented via the GhostKnockoff procedure with lasso regression, CDGE delivers false discovery rate control, operates on summary statistics from existing DGE pipelines, and accommodates batch effects. The genes identified by CDGE mediate the effects of most other differentially expressed genes and show stronger enrichment for known protein-protein interactions and biological pathways than DGE-identified genes. These results suggest that the field has been systematically over-interpreting DGE outputs, and that distinguishing direct from mediated effects is essential for prioritizing genes for functional follow-up and therapeutic development. Availability To facilitate public access to knockoff-based CDGE analysis, we have developed the R package “KnockoffCDGE” as an integrated implementation, which is freely available at https://github.com/GuJQ5/KnockoffCDGE (DOI: 10.5281/zenodo.23241517). Supplementary information Supplementary data are available at Bioinformatics online.
Abstract Summary Human genetic evidence provides a strong foundation for selecting drug targets. However, consistent benchmarking of target-indication evidence against clinical trial outcomes has been hindered by proprietary data and customised pipelines. To address this, we present the Python package ct-validation, which evaluates whether target-indication pairs supported by specific evidence sources are enriched for clinical progression. This package computes relative success (RS) and odds ratios (OR) with confidence intervals across clinical phase transitions. It also supports ontology-based semantic disease matching and offers interfaces in Python, on the command line, and via the Model Context Protocol (MCP). By remapping the benchmark from Minikel et al. (2024) to the Experimental Factor Ontology (EFO), we reproduced their per-transition enrichment, with a maximum difference of only 1.4% across any transition. In a systematic 3x3 benchmark that independently varies the genetic evidence and clinical trial sources, we found that a single curated open clinical trial source reproduces the signal of the proprietary comparator (RS = 2.92 vs 2.65; 2.34 vs 2.55 without oncology). However, aggregating open clinical trial registries compresses this signal (RS = 1.52); this loss is primarily driven by the breadth of the indication data rather than the genetic evidence. Availability and implementation ct-validation is implemented in Python (version 3.11 or higher), distributed via pip, and freely available at https://github.com/gero-science/ct-validation, with support for Python, command-line, and Model Context Protocol (MCP) interfaces. Archived code: https://doi.org/10.5281/zenodo.22901497. Benchmark data: https://doi.org/10.5281/zenodo.22901451. Supplementary information Supplementary data are available at Bioinformatics online.
