Carl G. de Boer

The paper identifies Berkson's paradox as a common statistical error in genomics analyses and provides examples and approaches to avoid misinterpreting spurious correlations.

The paper benchmarks 12 base editors delivered via VLPs for DNA editing in LentiX-293T cells, showing dosage-dependent efficiency and varying mutation outcomes.

This study uses evolutionary simulations to show that recombination affects the rate of regulatory complexity emergence in gene networks, but not its final level.

This study evaluates active learning strategies for sequence-to-expression models, finding that uncertainty-based methods outperform random sampling and identify informative sequences with distinct biological features.

This study shows that variant position within MPRAs affects regulatory activity, revealing positional bias in variant effect size estimation.

The paper shows that polygenic backgrounds affect the phenotypic impact of genetic variants across different levels, suggesting that selection coefficients vary with genetic context.

This paper introduces a method to rapidly construct gRNA arrays for CRISPR-Cas9 applications and provides a web tool for designing oligo sequences.

Yorzoi predicts RNA-seq coverage for yeast DNA sequences, enabling in-silico testing and design of synthetic DNA constructs.

The paper identifies 545 expression-modulating variants in CD4+ T cells linked to autoimmune diseases and shows they regulate genes in a lymphocyte activation network.

GAME provides a standardized framework for benchmarking genomics models across tasks, enabling consistent evaluation and community contributions.

This paper identifies homology-induced data leakage in genome-trained sequence models and introduces hashFrag to improve evaluation and generalizability.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

The paper compares plasmid library amplification methods in E. coli and finds liquid culture produces the most uniform libraries regardless of library type.

The paper quantifies the gene regulatory activity of naive DNA in yeast and human cells, finding that naive DNA can produce transcripts and chromatin marks similar to evolved sequences, suggesting biochemical activity is the default state.

The paper proposes a roadmap to solve the cis-regulatory code by training models on synthetic DNA sequences instead of genomic sequences to improve predictive accuracy.

The paper uses base editor screens to assess phosphorylation site functionality in T cell activation, identifying novel sites that modulate transcriptional activity and signaling pathways.

LegNet is a deep learning model for predicting gene expression from DNA sequences, outperforming existing models and enabling promoter sequence design.

GIL designs custom indexing primers for multiplexed sequencing, offering customization and compatibility with existing primers.

The paper identifies causal genetic variants in autoimmune diseases by combining allele-specific reporter assays with chromatin accessibility data, showing their impact on gene expression and T cell function.

The paper identifies 482 SLE risk variants with enhancer activity and characterizes allelic regulatory mechanisms at 27 risk loci using MPRA.

The paper constructs a deep transformer model to predict gene expression from cis-regulatory DNA sequences in yeast, revealing evolutionary constraints and evolvability patterns.

MAUDE improves analysis of sorting-based CRISPR screens by quantifying guide RNA effects on gene expression and estimating statistical significance.

The paper uses random yeast promoters to model cis-regulatory logic, achieving high prediction accuracy for gene expression.

BROCKMAN identifies transcription factor activity variation in single-cell ATAC-seq data by analyzing k-mer frequencies associated with epigenomic marks.