Biology subjects

de Boer, C. G.

Publications and source records attributed to de Boer, C. G..

15 recordsLinked to original sources

bioRxiv2026

Berkson's Paradox: A Common Statistical Error in Genomics Analyses

Berkson's paradox describes a scenario where a spurious correlation arises between two independent variables within a subset of samples selected based on a third variable which the first two variables cause. The conditions for Berkson's paradox are perfect in genomics due to many variables we study being causally intertwined, common selection criteria being biased either intentionally or not by the variables of interest, and our propensity to look towards correlations for windows into underlying mechanism. Here, we provide examples of cases where spurious correlations are likely to arise in common genomics analyses, and suggest analysis approaches to avoid misinterpreting spurious correlations.

genomics↗

bioRxiv2026

Virus-like particle-delivered base editor collection to expand the genome engineering toolbox

Virus-like particles (VLPs) enable transient, non-integrating delivery of CRISPR-Cas9 ribonucleoprotein cargo. Although VLPs have been reported for efficient DNA editing via base editors RNP delivery, the diversity of base editors tested as VLPs remains limited. We generated and benchmarked a panel of 12 base editors on the v5 eVLP backbone, targeting three genomic loci (HEK3, B2M, PDCD1) across five VLP dosages in LentiX-293T cells. Editing efficiency was generally dosage-dependent across all editors and varied by editor class and identity; PAM-flexible variants had lower editing efficiency than NGG-restricted counterparts, and the dual-function SPACE base editors showed reduced efficiency. We further characterized position-specific editing efficiencies and outcomes of the base editor VLP collection, revealing that a wide variety of mutation types are possible with the base editors in this collection.

synthetic biology↗

bioRxiv2026

Position-dependent variant effects reveal importance of context in genomic regulation

Gene expression is governed by the DNA sequence, which is read out through complex interactions between transcription factors (TFs), co-activators, and chromatin. Massively Parallel Reporter Assays (MPRAs) provide a high-throughput framework for functionally characterizing how regulatory DNA sequences impact the expression of a model gene. MPRAs have also proven to be useful for measuring the effects of genetic variation, where each allele is typically tested in the center of [~]200 bp of genomic context cloned into the MPRA; but the impact of variant position and local context remains largely unexplored. In this study, we systematically investigate how shifting the position of a variant within an MPRA probe influences its regulatory activity using models that predict expression in MPRAs from DNA sequence. We find that while the direction of variant effects is usually preserved across positions, the magnitude of expression changes can vary substantially depending on where the variant is placed within the construct. This positional bias appears to be largely explained by the strong position-dependent activity of TFs whose binding the variants perturb. In a subset of cases, interactions consistent with cooperativity between TFs also contributes to position-specific effects. [~]1% of variants appear to disrupt RNA polymerase III (Pol III) promoters within Alu elements, resulting in position-specificity because both A and B boxes are required for function and exclusion of either motif due to window shifts disrupts the variants effects. However, we saw little evidence to support the hypothesis that the positional dependence of variant effects resulted from redundancy of motifs. Overall, our study demonstrates the complexity of cis-regulatory grammar and how it can confound the interpretation of regulatory variants.

genomics↗

bioRxiv2025

Evolutionary simulations reveal role for genomic recombination in the evolution of gene regulatory network complexity and robustness

The gene regulatory networks (GRNs) of eukaryotes are dramatically more complex than the GRNs of prokaryotes, but we lack a complete picture of the selective pressures that have shaped this difference. Here, we use a biochemically informed model of gene regulation to simulate GRN evolution and explore the role that reproductive strategy plays in shaping regulatory complexity. We find that recombining and non-recombining populations converge to the same level of complexity, even in the absence of selection. However, recombination modifies the rate at which complexity emerges, accelerating convergence to the complexity plateau in changing environments while slowing the process in static environments. Our results suggest that, rather than being under direct selection, regulatory complexity may emerge as a byproduct of other evolutionary processes. These results highlight how reproductive strategy and environmental change interact to influence evolutionary trajectories.

bioinformatics↗

bioRxiv2025

GAME: Genomic API for Model Evaluation

The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models on practical applications has been challenging because individual projects evaluate their models in ad hoc ways, and there is substantial heterogeneity of both model architectures and benchmarking tasks. To address this challenge, we have created GAME, a system for large-scale, community-led standardized model benchmarking on user-defined evaluation tasks. We borrow concepts from the Application Programming Interface (API) paradigm to allow for seamless communication between pre-trained models and benchmarking tasks, ensuring consistent evaluation protocols. Because all models and benchmarks are inherently compatible in this framework, the continual addition of new models and new benchmarks is easy. We also developed a Matcher module powered by a large language model (LLM) to automate ambiguous task alignment between benchmarks and models. Containerization of these modules enhances reproducibility and facilitates the deployment of models and benchmarks across computing platforms. By focusing on predicting underlying biochemical phenomena (e.g. gene expression, open chromatin, DNA binding), we ensure that tasks remain technology-independent. We provide examples of benchmarks and models implementing this framework, and anticipate that the community will contribute their own, leading to an ever-expanding and evolving set of models and evaluation tasks. This resource will accelerate genomics research by illuminating the best models for a given task, motivating novel functional genomic benchmarks, and providing a more nuanced understanding of model abilities.

bioinformatics↗

bioRxiv2025

RAPID-DASH: Single-Day Assembly of Guide RNA Arrays for Multiplexed CRISPR-Cas9 Applications

Guide RNA (gRNA) arrays can enable targeting multiple genomic loci simultaneously using CRISPR-Cas9. In this study, we present a streamlined and efficient method to rapidly construct gRNA arrays with up to 10 gRNA units in a single day. We demonstrate that gRNA arrays maintain robust functional activity across all positions, and can incorporate libraries of gRNAs, combining scalability and multiplexing. Our approach will streamline combinatorial perturbation research by enabling the economical and rapid construction, testing, and iteration of gRNA arrays. To facilitate the adaptation of this approach, we have made a web tool to design oligo sequences necessary to assemble gRNA arrays.

synthetic biology↗

bioRxiv2025

Detecting and avoiding homology-based data leakage in genome-trained sequence models

Models that predict function from DNA sequence have become critical tools in deciphering the roles of genomic sequences and genetic variation within them. However, traditional approaches for dividing the genomic sequences into training data, used to create the model, and test data, used to determine the models performance on unseen data, fail to account for the widespread homology within genomes. Using simulations, we illustrate how homology-based data leakage can lead to overestimation of model performance. Across a variety of genomics models, we demonstrate that performance on test sequences varies systematically by their similarity with training sequences. Models generally perform well on distant sequences, reflecting the application of learned generalizable principles. At higher and intermediate similarity, models rely on memorized associations, inflating performance when function is conserved between homologs but failing when homologous sequences have functionally diverged. To dissect and mitigate these effects, we introduce hashFrag, a scalable solution for homology detection and data partitioning. Using hashFrag, we demonstrate how to create homology-aware evaluations of model performance, and improve model generalizability by providing improved splits for model training. Altogether, we establish how homology creates a systematic bias in genome-trained models and must be accounted for to ensure reliable evaluation of sequence-to-function predictors.

bioinformatics↗

bioRxiv2025

Variant effects depend on polygenic background: experimental, clinical, and evolutionary implications

Both rare and common genetic variants contribute to human disease, and emerging evidence suggests that they combine additively to influence disease liability. However, the non-linear relationship between disease liability and disease prevalence means that risk variants may have more severe phenotypic consequences in high-risk polygenic backgrounds and minimal impact in low-risk backgrounds, resulting in uneven selection across the population. As a result, selection coefficients may be better modeled as distributions that differ across populations, time, environments, and individuals rather than single values. Further, the number of genes contributing to a trait and epistasis between alleles enhance negative selection due to the increased variance pushing more individuals to phenotypic extremes. Because disease-relevant phenotypes may be masked in certain genetic backgrounds, polygenic background should be considered when characterizing the molecular underpinnings of complex traits.

genomics↗

bioRxiv2024

Linking candidate causal autoimmune variants to T cell networks using genetic and epigenetic screens in primary human T cells.

Genetic variants associated with autoimmune diseases are highly enriched within putative cis-regulatory regions of CD4+ T cells, suggesting that they alter disease risk via changes in gene regulation. However, very few genetic variants have been shown to affect T cell gene expression or function. We tested >18,000 autoimmune disease-associated variants for allele-specific expression using massively parallel reporter assays in primary human CD4+ T cells. The 545 expression-modulating variants (emVars) identified greatly enrich for likely causal variants. We provide evidence that many emVars are mediated by common upstream regulatory conduits, and that putative target genes of primary T cell emVars are highly enriched within a lymphocyte activation network. Using bulk and single-cell CRISPR-interference screens, we confirm that emVar-containing T cell cis-regulatory elements modulate both known and novel target genes that regulate T cell proliferation, providing plausible mechanisms by which these variants alter autoimmune disease risk.

genetics↗

bioRxiv2023

Hold out the genome: A roadmap to solving the cis-regulatory code

Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The "cis-regulatory code" - the rules that cells use to determine when, where, and how much genes should be expressed - has proven to be exceedingly complex, but recent advances in the scale and resolution of functional genomics assays and Machine Learning have enabled significant progress towards deciphering this code. However, we will likely never solve the cis-regulatory code if we restrict ourselves to models trained only on genomic sequences; regions of homology can easily lead to overestimation of predictive performance, and there is insufficient sequence diversity in our genomes to learn all relevant parameters. Fortunately, randomly synthesized DNA sequences enable us to test a far larger sequence space than exists in our genomes in each experiment, and designed DNA sequences enable a targeted query of the sequence space to maximally improve the models. Since cells use the same biochemical principles to interpret DNA regardless of its source, models that are trained on these synthetic data can predict genomic activity, often better than genome-trained models. Here, we provide an outlook on the field, and propose a roadmap towards solving the cis-regulatory code by training models exclusively on non-genomic DNA sequences, and using genomic sequences solely for evaluating the resulting models.

genomics↗

bioRxiv2022

Biochemical activity is the default DNA state in eukaryotes

Genomes encode for genes and the regulatory signals that enable those genes to be transcribed, and are continually shaped by evolution. Genomes, including those of human and yeast, encode for numerous regulatory elements and transcripts that have limited evidence of conservation or function. Here, we sought to create a genomic null hypothesis by quantifying the gene regulatory activity of evolutionarily naive DNA, using RNA-seq of evolutionarily distant DNA expressed in yeast and computational predictions of random DNA activity in human cells and tissues. In yeast, we found that >99% of bases in naive DNA expressed as part of one or more transcripts. Naive transcripts are sometimes spliced, and are similar to evolved transcripts in length and expression distribution, indicating that stable expression and/or splicing are insufficient to indicate adaptation. However, naive transcripts do not achieve the extreme high expression levels as achieved by evolved genes, and frequently overlap with antisense transcription, suggesting that selection has shaped the yeast transcriptome to achieve high expression and coherent gene structures. In humans, we found that, while random DNA is predicted to have minimal activity, dinucleotide content-matched randomized DNA is predicted to have much of the regulatory activity of evolved sequences, including active chromatin marks at between half (DNase I and H3K4me3) and 1/16th (H3K27ac and H3K4me1) the rate of evolved DNA, and the repression-associated H3K27me3 at about twice the rate of evolved DNA. Naive human DNA is predicted to be more cell type-specific than evolved DNA and is predicted to generate co-occurring chromatin marks, indicating that these are not reliable indicators of selection. However, extreme high activity is rarely achieved by naive DNA, consistent with these arising via selection. Our results indicate that evolving regulatory activity from naive DNA is comparatively easy in both yeast and humans, and we expect to see many biochemically active and cell type-specific DNA sequences in the absence of selection. Such naive biochemically active sequences have the potential to evolve a function or, if sufficiently detrimental, selection may act to repress them.

genomics↗

bioRxiv2021

Prioritization of autoimmune disease-associated genetic variants that perturb regulatory element activity in T cells

Genome-wide association studies have uncovered hundreds of autoimmune disease-associated loci; however, the causal genetic variant(s) within each locus are mostly unknown. Here, we perform high-throughput allele-specific reporter assays to prioritize disease-associated variants for five autoimmune diseases. By examining variants that both promote allele-specific reporter expression and are located in accessible chromatin, we identify 60 putatively causal variants that enrich for statistically fine-mapped variants by up to 57.8-fold. We introduced the risk allele of a prioritized variant (rs72928038) into a human T cell line and deleted the orthologous sequence in mice, both resulting in reduced BACH2 expression. Naive CD8 T cells from mice containing the deletion had reduced expression of genes that suppress activation and maintain stemness. Our results represent an example of an effective approach for prioritizing variants and studying their physiologically relevant effects.

genetics↗

bioRxiv2021

A comprehensive fitness landscape model reveals the evolutionary history and future evolvability of eukaryotic cis-regulatory DNA sequences

Mutations in non-coding cis-regulatory DNA sequences can alter gene expression, organismal phenotype, and fitness. Fitness landscapes, which map DNA sequence to organismal fitness, are a long-standing goal in biology, but have remained elusive because it is challenging to generalize accurately to the vast space of possible sequences using models built on measurements from a limited number of endogenous regulatory sequences. Here, we construct a sequence-to-expression model for such a landscape and use it to decipher principles of cis-regulatory evolution. Using tens of millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Sacccharomyces cerevisiae, we construct a deep transformer neural network model that generalizes with exceptional accuracy, and enables sequence design for gene expression engineering. Using our model, we predict and experimentally validate expression divergence under random genetic drift and strong selection weak mutation regimes, show that conflicting expression objectives in different environments constrain expression adaptation, and find that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for detecting selective constraint on gene expression using our model and natural sequence variation, and validate it using observed cis-regulatory diversity across 1,011 yeast strains, cross-species RNA-seq from three different clades, and measured expression-to-fitness curves. Finally, we develop a characterization of regulatory evolvability, use it to visualize fitness landscapes in two dimensions, discover evolvability archetypes, quantify the mutational robustness of individual sequences and highlight the mutational robustness of extant natural regulatory sequence populations. Our work provides a general framework that addresses key questions in the evolution of cis-regulatory sequences.

genomics↗

bioRxiv2020

Genome-wide discovery of lupus genetic risk variant allelic regulatory activity

Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3,073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we constructed a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into the Epstein-Barr virus-transformed B cell line GM12878 revealed 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. Comparison of MPRA results in GM12878 and Jurkat T cell lines highlights shared and unique allelic transcriptional regulatory mechanisms at SLE risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around allelic variants identifies one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second, larger class of TFs that bind allelically without direct alteration of their motif by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE.

genomics↗

bioRxiv2019

MAUDE: Inferring expression changes in sorting-based CRISPR screens

Pooled CRISPR screens allow high-throughput interrogation of genetic elements that alter expression of a reporter gene readout. New computational methods are needed to model these data. We created MAUDE (Mean Alterations Using Discrete Expression) for quantifying the impact of guide RNAs on a target genes expression in a pooled, sorting-based expression screen. MAUDE quantifies guide-level effects by modeling the distribution of cells across sorting expression bins. It then combines guides to estimate the statistical significance and effect size of targeted genetic elements. We show that MAUDE significantly improves over previous approaches and provide experimental design guidelines to best leverage MAUDE.

bioinformatics↗