Biology subjects

Schilder, B. M.

Publications and source records attributed to Schilder, B. M..

11 recordsLinked to original sources

bioRxiv2026

Genetic background shapes AI-predicted variant effects

Predicting the consequences of genetic variants remains a major goal in biomedicine. Conventional approaches typically assess single-nucleotide variants in the context of a single reference genome, without accounting for genetic diversity that can modulate variant effects. Here we introduce the personalized variant effect predictor (pVEP) framework, which quantifies how genetic background across thousands of human genomes from globally diverse populations shapes computational predictions of clinical variant effects. Across deep learning models spanning protein structure, splicing, and noncoding regulation, pVEP reveals that many clinical variants exhibit heterogeneous predicted effects across haplotypes, with the same variant predicted to be pathogenic in some genetic backgrounds and benign in others. We find support for underlying molecular mechanisms, including shifts in predicted protein contacts and changes in splice-site recognition. Overall, personalized genomic context emerges as a systematically underappreciated variable in variant annotation and clinical interpretation, with particular implications for genetically diverse populations.

genomics↗

bioRxiv2026

orthogene: a Bioconductor package to easily map genes within and across hundreds of species

MotivationMapping genes across identifier systems and species is a routine but critical step in bioinformatics workflows. Despite its ubiquity, gene mapping is frequently handled using bespoke, ad hoc solutions, increasing duplicated effort and introducing opportunities for error. These issues are exacerbated by the prevalence of non-one-to-one homolog relationships and inconsistent handling of gene identifiers across species and databases, which can compromise downstream analyses and reproducibility. ResultsWe present orthogene, an R/Bioconductor package that simplifies gene mapping within and across hundreds of species. orthogene provides a unified, workflow-oriented framework that integrates automated species and identifier standardization, homolog inference across multiple databases, flexible handling of ambiguous homolog relationships, and transformation of gene lists, tables, and high-dimensional matrices into analysis-ready formats. By abstracting common sources of technical complexity while retaining user control, orthogene enables transparent, reproducible, and scalable gene mapping across a wide range of biological contexts. Availabilityhttps://bioconductor.org/packages/orthogene Contactbrian_schilder@alumni.brown.edu

bioinformatics↗

bioRxiv2025

Gene expression patterns of the developing human face at single cell resolution reveal cell type contributions to normal facial variation and disease risk

Craniofacial development gives rise to the complex structures of the face and involves the interplay of diverse cell types. Despite its importance, our understanding of human-specific craniofacial developmental mechanisms and their genetic underpinnings remains limited. Here, we present a comprehensive single-nucleus RNA sequencing (snRNA-seq) atlas of human craniofacial development from craniofacial tissues of 24 embryos that span six key time points during the embryonic period (4-8 post-conception weeks). This resource resolves the transcriptional dynamics of seven major cell types and uncovers distinct major cell types, including muscle progenitors and cranial neural crest cells (CNCCs), as well as dozens of subtypes of ectoderm and mesenchyme. Comparative analyses reveal substantial conservation of major cell types, alongside human biased differences in gene expression programs. CNCCs, which play a crucial role in craniofacial morphogenesis, exhibit the lowest marker gene conservation, underscoring their evolutionary plasticity. Spatial transcriptomics further localizes cell populations, providing a detailed view of their developmental roles and anatomical context. We also link these developmental processes to genetic variation, identifying cell type-specific enrichments for common variants associated with facial morphology and rare variants linked to orofacial clefts. Intriguingly, Neanderthal-introgressed sequences are enriched near genes with biased expression in cartilage and specialized ectodermal subtypes, suggesting their contribution to modern human craniofacial features. This atlas offers unprecedented insights into the cellular and genetic mechanisms shaping the human face, highlighting conserved and distinctly human aspects of craniofacial biology. Our findings illuminate the developmental origins of craniofacial disorders, the genetic basis of facial variation, and the evolutionary legacy of ancient hominins. This work provides a foundational resource for exploring craniofacial biology, with implications for developmental genetics, evolutionary biology, and clinical research into congenital anomalies.

developmental biology↗

bioRxiv2022

EpiCompare: R package for the comparison and quality control of epigenomic peak files

SummaryEpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner. Availability and ImplementationEpiCompare is available on Bioconductor ([≥] v3.15): https://bioconductor.org/packages/release/bioc/html/EpiCompare.html All source code is publically available via GitHub: https://github.com/neurogenomics/EpiCompare Documentation website https://neurogenomics.github.io/EpiCompare EpiCompare DockerHub repository: https://hub.docker.com/repository/docker/neurogenomicslab/epicompare

bioinformatics↗

bioRxiv2022

CUT&Tag recovers up to half of ENCODE ChIP-seq peaks

Techniques for genome-wide epigenetic profiling have been undergoing accelerated development toward recovery of high-quality data from bulk and single cell samples. DNA-protein interactions have traditionally been profiled via chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), which has become the gold standard for studying histone modifications or transcription factor binding. Cleavage Under Targets & Tagmentation (CUT&Tag) is a rapidly expanding new technique that enables profiling of such interactions in situ at high sensitivity and is adaptable to single cell applications. However, thorough evaluation and benchmarking against established ChIP-seq datasets are lacking. Here, we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Combining multiple new and published CUT&Tag datasets, there was an average recall of 54% known ENCODE peaks for both histone modifications. To optimize data analysis steps, we tested peak callers MACS2 and SEACR and identified optimal peak calling parameters. Considering both precision and recall of known ENCODE peaks, the peak callers were comparable in their performance, although peaks produced by MACS2 match ENCODE peak width distributions more closely. We found that reducing PCR cycles during library preparation lowered duplication rates at the expense of ENCODE peak recovery. Despite the moderate ENCODE peak recovery, peaks identified by CUT&Tag represent the strongest ENCODE peaks and show the same functional and biological enrichments as ChIP-seq peaks identified by ENCODE. Our workflow systematically evaluates the merits of methodological adjustments, providing a benchmarking framework for the experimental design and analysis of CUT&Tag studies, and will facilitate future efforts to apply CUT&Tag in human tissues and single cells.

genomics↗

bioRxiv2020

Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies

Microglial cells have emerged as potential key players in brain aging and pathology. To capture the heterogeneity of microglia across ages and regions, and to understand how genetic risk for neurological and psychiatric brain disorders is related to microglial function, large transcriptome studies are essential. Here, we describe the transcriptome analysis of 255 primary human microglia samples isolated at autopsy from multiple brain regions of 100 human subjects. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region, age and sex. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, including novel associations with microglia expression of USP6NL for Alzheimers disease, and P2RY12 for Parkinsons disease. In summary, we have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose molecular mechanisms of action of candidate functional variants in several neurological and psychiatric diseases.

genetics↗

bioRxiv2020

echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline

SummaryecholocatoR integrates a diverse suite of statistical and functional fine-mapping tools in order to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium (LD) panels, quantitative trait loci (QTL) datasets, genome-wide annotations, cell type-specific epigenomics, thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations. Availability and implementationecholocatoR is an open-source R package available through GitHub under the MIT license: https://github.com/RajLabMSSM/echolocatoR

bioinformatics↗

bioRxiv2020

Fine-mapping of Parkinson's disease susceptibility loci identifies putative causal variants

Recent genome-wide association studies have identified 78 loci associated with Parkinsons Disease susceptibility but the underlying mechanisms remain largely unclear. To identify variants likely causal for disease risk, we fine-mapped these Parkinsons-associated loci using four different statistical and functional fine-mapping methods. We then integrated multi-assay cell-type-specific epigenomic profiles to pinpoint the likely mechanism of action of each variant, allowing us to identify Consensus SNPs that disrupt LRRK2 and FCGR2A regulatory elements in microglia, MBNL2 enhancers in oligodendrocytes, and DYRK1A enhancers in neurons. Finally, we confirmed the functional relevance of fine-mapped SNPs using a suite of in silico validation approaches. Together, these results provide a robust list of likely causal variants underlying Parkinsons Disease risk for further mechanistic studies.

genomics↗

bioRxiv2020

Discordant transcriptional signatures of mitochondrial genes in Parkinson's disease human myeloid cells

An increasing number of identified Parkinsons disease (PD) risk loci contain genes highly expressed in innate immune cells, yet their potential role in pathological mechanisms is not obvious. We have generated transcriptomic profiles of CD14+ monocytes from 230 individuals with sporadic PD and age-matched healthy subjects. We identified dysregulation of genes involved in mitochondrial and proteasomal function. We also generated transcriptomic profiles of primary microglia from autopsied brains of 55 PD and control subjects and observed discordant transcriptomic signatures of mitochondrial genes in PD monocytes and microglia. We further identified PD susceptibility genes, whose expression, relative to each risk allele, is altered in monocytes. These findings reveal that transcriptomic mitochondrial alterations are detectable in PD monocytes and are distinct from brain microglia, and facilitates efforts to understand the roles of myeloid cells in PD.

neuroscience↗

bioRxiv2019

FAIRshake: toolkit to evaluate the findability, accessibility, interoperability, and reusability of research digital resources

As more datasets, tools, workflows, APIs, and other digital resources are produced by the research community, it is becoming increasingly difficult to harmonize and organize these efforts for maximal synergistic integrated utilization. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have prompted many stakeholders to consider strategies for tackling this challenge by making these digital resources follow common standards and best practices so that they can become more integrated and organized. Faced with the question of how to make digital resources more FAIR, it has become imperative to measure what it means to be FAIR. The diversity of resources, communities, and stakeholders have different goals and use cases and this makes assessment of FAIRness particularly challenging. To begin resolving this challenge, the FAIRshake toolkit was developed to enable the establishment of community-driven FAIR metrics and rubrics paired with manual, semi- and fully-automated FAIR assessment capabilities. The FAIRshake toolkit contains a database that lists registered digital resources, with their associated metrics, rubrics, and assessments. The FAIRshake toolkit also has a browser extension and a bookmarklet that enables viewing and submitting assessments from any website. The FAIR assessment results are visualized as an insignia that can be viewed on the FAIRshake website, or embedded within hosting websites. Using FAIRshake, a variety of bioinformatics tools, datasets listed on dbGaP, APIs registered in SmartAPI, workflows in Dockstore, and other biomedical digital resources were manually and automatically assessed for FAIRness. In each case, the assessments revealed room for improvement, which prompted enhancements that significantly upgraded FAIRness scores of several digital resources.

bioinformatics↗

bioRxiv2018

Tensor Decomposition of Stimulated Monocyte and Macrophage Gene Expression Profiles Identifies Neurodegenerative Disease-specific Trans-eQTLs

Recent human genetic studies suggest that cells of the innate immune system have a primary role in the pathogenesis of neurodegenerative diseases. However, the results from these studies often do not elucidate how the genetic variants affect the biology of these cells to modulate disease risk. Here, we applied a tensor decomposition method to uncover disease-associated gene networks linked to distal genetic variation in stimulated human monocytes and macrophages gene expression profiles. We report robust evidence that some disease-associated genetic variants affect the expression of multiple genes in trans. These include a Parkinsons disease locus influencing the expression of genes mediated by a protease that controls lysosomal function, and Alzheimers disease loci influencing the expression of genes involved in type 1 interferon signaling, myeloid phagocytosis, and complement cascade pathways. Overall, we uncover gene networks in induced innate immune cells linked to disease-associated genetic variants, which may help elucidate the underlying biology of disease.

genomics↗