Alan P. Boyle

The paper characterizes HPV integration structures in oropharyngeal cancer and links Type2 integration to oncogenic features and aggressive tumor behavior.

The paper shows that sepsis reprograms monocytes into a pro-inflammatory state and shifts their lineage toward neutrophil-like cells, increasing lung injury risk in survivors.

The paper develops a targeted long-read method to profile haplotype-specific chromatin accessibility and DNA methylation at L1HS loci in human cells, revealing allele-specific patterns and non-Mendelian inheritance.

The paper defines a 3D chromatin compartment blueprint that programs histone-to-protamine exchange during spermiogenesis using ATAC-seq, CUT&Tag, and Hi-C.

The paper constructs a comprehensive TFBS database by integrating multiple resources and evaluates their accuracy using independent datasets.

This study assesses somatic mosaicism in human frontal cortex using multi-platform sequencing, showing improved variant detection with haplotype-resolved assemblies and single-neuron sequencing.

The paper develops a framework to detect and trace somatic retrotransposition events in human tissues, identifying rare L1 insertions and improving detection accuracy.

The paper identifies cryptic intronic C9orf72 transcripts as endogenous templates for RAN translation, leading to DPR production in ALS/FTD.

GPatch creates chromosome-scale, gap-free pseudoassemblies from fragmented draft genomes, enabling accurate Hi-C matrix construction.

This study shows that AAGGG repeat expansions cause synaptic defects in CANVAS neurons independently of RFC1, with phenotypes corrected by repeat deletion.

This paper improves trans-ancestral polygenic risk scores by integrating tissue-specific functional genomic data, showing better predictive accuracy across multiple traits.

LRphase is a tool for phasing long sequencing reads using known haplotypes to assign haplotype identity to variants.

The paper uses MPRA to identify functional regulatory variants associated with schizophrenia and shows they differ from eQTLs in genomic location and regulatory impact.

The paper develops TLand, a machine-learning model that prioritizes and annotates non-coding regulatory variants by organ, using RegulomeDB features and outperforming existing models.

The paper investigates why studies fail to find associations between de novo noncoding variants and complex disorders like ASD, identifying annotation quality and variant reliability as key issues.

Explain-seq is a pipeline that trains and interprets sequence-based deep learning models to predict enhancer activities and identify transcription factor motifs in genomic data.

The paper extends the TRACE method to incorporate variant data, identifying footprint-QTLs that correlate with transcription factor binding and regulatory variants.

The paper updates RegulomeDB to improve non-coding variant annotation by integrating new functional genomic data and probabilistic scores from the SURF algorithm.

SEMplMe predicts the effect of DNA methylation on transcription factor binding affinity across all positions in a motif, outperforming SELEX-based predictions for CTCF.

OnRamp provides a cost-effective method for plasmid validation using nanopore sequencing, enabling full plasmid sequencing and detection of sequence variation in complex regions.

This paper introduces TURF, a tool to prioritize regulatory variants with tissue-specific function in non-coding regions using ENCODE data, showing high performance in predicting functional variants linked to GWAS traits.

The paper demonstrates that the lac operator-repressor system functions in zebrafish cells to modulate gene expression.

The paper uses Cas9 targeted nanopore sequencing to enrich and detect mobile elements in human genomes, identifying non-reference MEIs missed by existing methods.

SquiggleNet classifies nanopore signals in real time with high accuracy, outperforming traditional methods in speed and memory usage.

F-Seq2 improves peak calling by incorporating dynamic statistics and kernel density estimation, outperforming existing tools on ATAC-seq and ChIP-seq data.

The paper develops genome-wide enhancer-to-target gene assignments across >500 cell types and shows they outperform naive methods in gene set enrichment analysis.

MapGL infers the evolutionary gain and loss of short genomic sequence features using phylogenetic maximum parsimony, simplifying the process with a unified software tool.

TRACE predicts transcription factor footprints using chromatin accessibility data and PWMs, outperforming existing methods by integrating multiple motifs in a single model.

The paper shows transposable elements contribute significantly to species- and cell-specific chromatin looping variation through CTCF binding site expansions in human and mouse.

Poly-Enrich improves gene set enrichment testing for genomic regions by modeling peak counts with a negative binomial model, outperforming previous methods in certain scenarios.