Biology subjects

Boyle, A. P.

Publications and source records attributed to Boyle, A. P..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatiotemporal Heterogeneity of HPV Integration Architectures Drive Oncogenic Features of Oropharyngeal Cancer

Viral integration is common in human papillomavirus-positive oropharyngeal squamous cell carcinoma (HPV+ OPSCC), but its structural and clinical significance remains unclear. We integrated targeted capture sequencing of 197 HPV+ OPSCCs with long-read nanopore sequencing of 17 tumors and models to characterize HPV-host rearrangements. We developed a novel quantitative informatics framework that classifies integration structures as Type1 (simple, single/low-copy) or Type2 (clustered, high-copy, rearranged). HPV integration occurred in 83% of tumors, with recurrent hotspots including TP63, MYC, CD274, TRAF2, and KMT2C. Type2 events were enriched for HPV E2 disruption, focal human amplifications, preferential transcription, and noncanonical HPV-host RNA fusion isoforms. Type2 tumors showed increased HPV oncogene expression and activation of MYC, NF-{kappa}B/TNF, PI3K-AKT-mTOR, Hippo/YAP-TAZ, and epithelial stemness programs. Multi-region and longitudinal analyses revealed intratumoral heterogeneity and clonal selection of Type2 architectures during progression, supporting integration structure as a potential marker of aggressive behavior and risk stratification.

bioinformatics↗

bioRxiv2026

Fiber-TEnCATS reveals haplotype-specific chromatin accessibility and DNA methylation at human L1HS loci

Human-specific long interspersed nuclear element-1 (L1HS) is an active and autonomous retrotransposon in the human genome. Changes in its transcription and transposition are known to affect cellular processes involved in development and aging, and diseases such as neurological disorders and cancer. To better understand natural variability in epigenetic patterns that affect L1HS regulation, we developed a targeted long-read method to simultaneously profile individual haplotypes for DNA methylation and chromatin accessibility across L1HS loci in a healthy human cell line trio. We show that the intronic L1HS in the ZNF638 gene consistently displays high chromatin accessibility and DNA hypomethylation with bidirectional transcription. Our approach also reveals additional intronic and intergenic L1HS copies with allele-specific chromatin accessibility and methylation, and instances of reduced promoter DNA methylation that does not correspond with increased chromatin accessibility. We also identify potential cases of non-Mendelian inheritance of DNA methylation patterns over a subset of L1HS promoters. Our methods high coverage over L1HS loci enables detection and profiling of loci that are missed even by long-read-based assemblies and enables more accurate inheritance tracing of L1HS insertions. Overall, our results offer new insights into the locus-specific regulation of both reference and non-reference L1HS within the human genome.

genomics↗

bioRxiv2026

3D chromatin compartment of round spermatids encodes the spatiotemporal program of histone-to-protamine exchange in spermiogenesis

Sperm formation requires a radical chromatin reorganization, where nucleosomes are replaced by transition proteins (TNPs) and subsequently by protamines (PRM1 and PRM2). Although essential for fertility, the regulatory logic governing this exchange is unknown, but its presumed to be stochastic and unregulated. Using endogenously tagged PRM mouse models and stage-resolved, genome-wide profiling, we revise the order of histone-to-protamine exchange and show that the chromatin remodeling process is highly programmed. Imaging and biochemical experiments reveal a direct histone-to-PRM1 exchange, while TNPs appear after PRM1 but precede PRM2 incorporation. This temporal uncoupling of PRM1 and PRM2 incorporation coincides with dynamic, region-specific chromatin remodeling that is not governed by histone acetylation but is instructed by the three-dimensional nuclear architecture of round spermatids. Therefore, by integrating ATAC-seq, CUT&Tag, and Hi-C, we define a compartment-encoded "blueprint" that prescribes the assembly of the mature sperm epigenome and establishes a complex molecular hierarchy for the histone-to-protamine exchange.

genomics↗

bioRxiv2026

TFBSpedia: a comprehensive human and mouse transcription factor binding sites database

Mapping the genomic locations and patterns of transcription factor binding sites (TFBS) is essential for understanding gene regulation and advancing treatments for diseases driven by DNA modifications, including epigenetic changes and sequence variants. Although several TFBS databases exist, no study has systematically benchmarked these databases across different sequencing technologies and computational algorithms. In this study, we addressed this gap by constructing a TFBS database that integrates all available ENCODE cell line ATAC-seq and Cistrome Data Browser ChIP-seq datasets, comprising 11.3 million human and 1.87 million mouse TFBS. We also integrated previously published TFBS resources (Factorbook, Unibind, RegulomeDB, and ENCODE_footprint) and found each contains a substantial fraction of unique TFBS predictions, highlighting significant discrepancies among existing resources. To assess the accuracy of the combined TFBS regions, we assembled ten independent genomic annotation datasets for evaluation and found that TFBS regions predicted by multiple databases are more likely to represent true and biologically meaningful binding sites. For each predicted TFBS region, we define two scores: the confidence score reflects prediction reliability, while the importance score represents biological functional relevance. Finally, we introduce TFBSpedia, a lightweight and efficient search engine that enables rapid retrieval of TFBS regions and comprehensive annotation information across the integrated databases.

bioinformatics↗

bioRxiv2025

Multi-platform framework for mapping somatic retrotransposition in human tissues

Mobile element insertions (MEI) shape the human genome in both germline and somatic tissues. While inherited MEIs are well characterized, mapping somatic MEIs (sMEI) in non-cancer tissues remains challenging due to their low allelic fraction and repetitive nature. We established an integrative framework for sMEI analysis leveraging modern sequencing technologies and analytical innovations. We first benchmarked sMEI detection and demonstrated advantages of long-read and MEI-targeted sequencing for ultra-low-frequency events using a mixture of well-established cell lines. We then showed that haplotype phasing and donor-specific assemblies refine sMEI detection, effectively distinguishing from germline and false signals in in-silico tumor-normal mixtures. We further developed a source-tracing strategy based on internal sequence variation, expanding the catalogue of active source elements beyond traditional transduction-based methods. Applying this framework to donor tissues, we identified 18 rare somatic L1 insertions, revealing structural and source diversity. Our work provides a foundational framework and biological insight into sMEIs.

genomics↗

bioRxiv2025

Fast and Accurate Draft Genome Patching with GPatch

Recent advancements in sequencing technologies have yielded numerous long-read draft genomes, promising to enhance understanding of genomic variation. However, draft genomes are typically highly fragmented, posing challenges for functional genomics. We introduce GPatch, a tool that constructs chromosome-scale pseudoassemblies from fragmented drafts using alignments to a reference assembly. GPatch produces complete, accurate, gap-free assemblies preserving over 95% of nucleotides from human and non-human draft genomes. We show that GPatch pseudoassemblies can be used to construct Hi-C matrices, whereas fragmented draft assemblies cannot. Until complete genome assembly becomes routine, GPatch presents a necessary tool for maximizing the utility of draft genomes. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/655567v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@a8485aorg.highwire.dtl.DTLVardef@262cd9org.highwire.dtl.DTLVardef@4a67f8org.highwire.dtl.DTLVardef@efebc1_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Long-term Reprogramming and Altered Ontogeny of Classical Monocytes Mediates Enhanced Lung Injury in Sepsis Survivor Mice

Patients who survive sepsis are predisposed to new hospitalizations for respiratory failure, but the underlying mechanisms are unknown. Using a murine model in which prior sepsis predisposes to enhanced lung injury, we previously discovered that classical monocytes persist in the lungs after long-term recovery from sepsis and exhibit enhanced cytokine expression after secondary challenge with intra-nasal lipopolysaccharide. Here, we hypothesized that immune reprogramming of post-sepsis monocytes and altered ontogeny predispose to enhanced lung injury. Monocyte depletion and/or adoptive transfer was performed three weeks and three months after sepsis. Monocytes from post-sepsis mice were necessary and sufficient for enhanced LPS-induced lung injury and promoted neutrophil degranulation. Prior sepsis enhanced JAK-STAT signaling and AP-1 binding in monocytes and shifted monocytes toward the neutrophil-like monocyte lineage. In human sepsis and/or pneumonia survivors, monocytes were predictive of 90-day mortality and exhibit transcriptional and proteomic neutrophil-like signatures. We conclude that sepsis reprograms monocytes into a pro-inflammatory phenotype and skews bone marrow progenitors and monocytes toward the neutrophil-like lineage, predisposing to neutrophil degranulation and lung injury.

immunology↗

bioRxiv2025

Cryptic intronic transcriptional initiation generates efficient endogenous mRNA templates for C9orf72-associated RAN translation

Intronic GGGGCC hexanucleotide repeat expansions in C9orf72 are the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Despite its intronic location, this repeat avidly supports synthesis of pathogenic dipeptide repeat (DPR) proteins via repeat-associated non-AUG (RAN) translation. However, the template RNA species that undergoes RAN translation endogenously remains unclear. Using long-read based 5 RNA ligase-mediated rapid amplification of cDNA ends (5 Repeat-RLM-RACE), we identified novel C9orf72 transcripts initiating within intron 1 in a C9BAC mouse model, patient-derived iNeurons, and iNeuron-derived polysomes. These cryptic m7G-capped mRNAs are at least partially polyadenylated and are more abundant than transcripts derived from intron retention or circular intron lariats. In RAN translation reporter assays, novel intronic template transcripts - even those with short (32 nucleotide) leaders - exhibited robust expression compared to exon-intron and repeat-containing lariat reporters. To assess endogenous lariat repeat RNA contributions to RAN translation, we enhanced endogenous lariat stability by knocking down the lariat debranching enzyme Dbr1. However, this modulation did not impact DPR production in patient-derived iNeurons. These findings identify cryptic, linear, m7G-capped intronic-initiating C9orf72 mRNAs as an endogenous template for RAN translation and DPR production, with implications for disease pathogenesis and therapeutic development. SIGNIFICANCE STATEMENTAn intronic GGGGCC repeat expansion in C9orf72 supports an unusual translational initiation process known as repeat-associated non-AUG (RAN) translation to produce toxic dipeptide repeat (DPR) proteins that contribute to neurodegeneration in ALS and FTD. How an intronic repeat RNA engages with ribosomes to support such translation is unclear. Here we identify a series of novel mRNA transcripts that initiate within the repeat-containing intron to create linear m7G-capped templates for RAN translation from GGGGCC repeats. These cryptic mRNAs are present in patient iNeurons, engage with ribosomes, and robustly support RAN translation. This finding has important implications for both our understanding of the mechanism by which RAN translation occurs and on therapeutic development in this currently untreatable class of neurodegenerative disorders.

neuroscience↗

bioRxiv2024

A personalized multi-platform assessment of somatic mosaicism in the human frontal cortex

Somatic mutations in individual cells create genomic mosaicism, influencing genetic disorders and cancers. While clonal mutations in cancers are well-studied, rarer somatic variants in normal tissues remain poorly characterized. This study systematically evaluates detection methods using a personalized donor-specific assembly (DSA) from a neurotypical individuals dorsolateral prefrontal cortex assessed with Oxford Nanopore, NovaSeq, linked-read sequencing, Cas9-targeted long-read sequencing (TEnCATS), and single-neuron MALBAC amplification. The haplotype-resolved DSA improved cross-platform analysis, dramatically increasing phasing rates. Germline SNVs, structural variations (SVs), and transposable elements (TEs) were recalled with 99.4%-99.7% accuracy in bulk tissue, and phased haplotype analysis reduced false positives by 15.4%-75.1% for putative somatic candidates. Long-read single-neuron sequencing detected nine somatic SV candidates, demonstrating enhanced sensitivity for rare variants, while TEnCATS identified eight low-frequency somatic TE candidates. These findings highlight advanced methodologies for precise somatic variant detection, critical for understanding mosaicisms role in health and disease.

genomics↗

bioRxiv2024

Enhancing Portability of Trans-Ancestral Polygenic Risk Scores through Tissue-Specific Functional Genomic Data Integration

Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations - SURF, TURF, and TLand - to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.

genetics↗

bioRxiv2023

Organ-specific prioritization and annotation of non-coding regulatory variants in the human genome

Identifying non-coding regulatory variants in the human genome remains a challenging task in genomics. Recently, we released the second version of our leading regulatory variant database, RegulomeDB. Building upon this comprehensive database, we developed a novel machine-learning architecture, TLand, which utilizes RegulomeDB-derived features to predict regulatory variants at the cell- or organ-specific level. In our holdout benchmarking, TLand consistently outperformed state-of-the-art models, demonstrating its ability to generalize to new cell lines or organs. We trained three types of organ-specific TLand models to overcome the common model bias toward high data availability cell lines or organs. These models accurately prioritize relevant organs for 2 million GWAS SNPs associated with GWAS traits. Moreover, our analysis of top-scoring variants in specific organ models showed a high enrichment of relevant GWAS traits. We expect that TLand and RegulomeDB will further advance our ability to understand human regulatory variants genome-wide.

bioinformatics↗

bioRxiv2023

Explain-seq: an end-to-end pipeline from training to interpretation of sequence-based deep learning models

Interpreting predictive machine learning models to derive biological knowledge is the ultimate goal of developing models in the era of genomic data exploding. Recently, sequence-based deep learning models have greatly outperformed other machine learning models such as SVM in genome-wide prediction tasks. However, deep learning models, which are black-box models, are challenging to interpret their predictions. Here we represented an end-to-end computational pipeline, Explain-seq, to automate the process of developing and interpreting deep learning models in the context of genomics. Explain-seq takes input as genomic sequences and outputs predictive motifs derived from the model trained on sequences. We demonstrated Explain-seq with a public STARR-seq dataset of the A549 human lung cancer cell line released by ENCODE. We found our deep learning model outperformed gkm-SVM model in predicting A549 enhancer activities. By interpreting our well-performed model, we identified 47 TF motifs matched with known TF PWMs, including ZEB1, SP1, YY1, and INSM1. They are associated with epithelial-mesenchymal transition and lung cancer proliferation and metagenesis. In addition, there were motifs that were not matched in the JASPAR database and may be considered as de novo enhancer motifs in the A549 cell line. Availabilityhttps://github.com/nsamzhao/Explain-seq Contactapboyle@umich.edu Supplementary informationSupplementary data are available as attachment.

bioinformatics↗

bioRxiv2023

LRphase: an efficient method for assigning haplotype identity to long reads

MotivationUnderstanding the functional effects of sequence variation is among the primary goals of contemporary genomics. Individual human genomes contain millions of variants which are thought to contribute to phenotypic variability and differential disease risks at the population level. However, because variants rarely act in isolation, we cannot accurately predict functional effects without first considering the potential effects of other interacting variants on the same chromosome. This information can be obtained by phasing the read data from sequencing experiments. However, no standalone tools are available to simply phase reads based on known haplotypes. Here we present LRphase: a user-friendly utility for simple phasing of long sequencing reads. Availability and ImplementationLRphase is implemented in Python, and is freely available at https://github.com/Boyle-Lab/LRphase, under the MIT license. Version 1.1.0, described in this manuscript, is available through the pip and Bioconda repositories (e.g., "pip install lrphase==1.1.0"). Contactapboyle@umich.edu Supplementary InformationSupplementary methods are available as part of the online version of this publication.

bioinformatics↗

bioRxiv2022

Quantitative assessment of association between noncoding variants and transcription factor binding

Association fine-mapping of molecular traits is an essential method for understanding the impact of genetic variation. Sequencing-based assays, including RNA-seq, DNase-seq and ChIP-seq, have been widely used to measure different cellular traits and enabled genome-wide mapping of quantitative trait loci (QTLs). The disruption of cis-regulatory sequence, often occurring through variation within transcription factor binding motifs, has been strongly associated with gene dysregulation and human disease. We recently developed a computational method, TRACE, for transcription factor binding footprint prediction. TRACE integrates chromatin accessibility and transcription factor binding motifs to produce quantitative scores that describe the binding affinity of a TF for a specific TFBS locus. Here we have extended this method to incorporate variant data for 57 Yoruban individuals. Using genome-wide chromatin-accessibility data and human TF binding motifs, we have generated precise, genome-wide predictions of individual-specific transcription factor binding footprints. Subsequent association mapping between these footprints and nearby regulatory variants yielded numerous footprint-variant pairs with significant evidence for correlation, which we call footprint-QTLs (fpQTLs). fpQTLs appear to affect TF binding in a distance-dependent manner and share significant overlap with known dsQTLs and eQTLs. fpQTLs provide a rich resource for the study of regulatory variants, both within and outside known TFBSs, leading to improved functional interpretation of noncoding variation.

bioinformatics↗

bioRxiv2022

Challenges in screening for de novo noncoding variants contributing to genetically complex phenotypes

Understanding the genetic basis for complex, heterogeneous disorders, such as autism spectrum disorder (ASD), is a persistent challenge in human medicine. Owing to their phenotypic complexity, the genetic mechanisms underlying these disorders may be highly variable across individual patients. Furthermore, much of their heritability is unexplained by known regulatory or coding variants. Indeed, there is evidence that much of the causal genetic variation stems from rare and de novo variants arising from ongoing mutation. These variants occur mostly in noncoding regions, likely affecting regulatory processes for genes linked to the phenotype of interest. However, because there is no uniform code for assessing regulatory function, it is difficult to separate these mutations into likely functional and nonfunctional subsets. This makes finding associations between complex diseases and potentially causal de novo single-nucleotide variants (dnSNVs) a difficult task. To date, all but one published study in this area has failed to find any significant associations between dnSNVs from ASD patients and any class of known regulatory elements. We sought to identify the underlying reasons for this and present strategies for overcoming these challenges. We show that, contrary to previous claims, the main reason for failure to find robust statistical enrichments is not the number of families sampled, but the quality and relevance to ASD of the annotations used to prioritize dnSNVs, and the reliability of the set of dnSNVs itself. We present a list of recommendations for designing future studies of this sort that will help researchers avoid common pitfalls.

genetics↗

bioRxiv2022

Annotating and prioritizing human non-coding variants with RegulomeDB

Nearly 90% of the disease risk-associated variants identified from genome-wide association studies (GWAS) are in non-coding regions of the genome. The annotations obtained from analyzing functional genomics assays can provide additional information to pinpoint causal variants, which are often not the lead variants identified from association studies. However, the lack of available annotation tools limits the use of such data. To address the challenge, we have previously built the RegulomeDB database for prioritizing and annotating variants in non-coding regions1, which has been a highly utilized resource for the research community (Supplementary Fig. 1). RegulomeDB annotates a variant by intersecting its position with genomic intervals identified from functional genomic assays and computational approaches. It also incorporates those hits of a variant into a heuristic ranking score, representing its potential to be functional in regulatory elements. Here we present a newer version of the RegulomeDB web server, RegulomeDB v2.1 (http://regulomedb.org). We improve and boost annotation power by incorporating thousands of newly processed data from functional genomic assays in GRCh38 assembly, and now include probabilistic scores from the SURF algorithm that was the top performing non-coding variant predictor in CAGI 52. We also provide interactive charts and genome browser views to allow users an easy way to perform exploratory analyses in different tissue contexts.

bioinformatics↗

bioRxiv2022

On-Ramp: a tool for rapid, multiplexed validation of plasmids using nanopore sequencing

Recombinant plasmid vectors are versatile tools which have facilitated discoveries in molecular biology, genetics, proteomics, and many other fields. As the enzymatic and bacterial processes used to create recombinant DNA can introduce errors, sequence validation is an essential step in plasmid assembly. Sanger sequencing is the current standard for plasmid validation, however this method is limited by an inability to sequence through complex secondary structure and lacks scalability when applied to full-plasmid sequencing of multiple plasmids due to read-length limits. While next-generation sequencing (NGS) does provide full-plasmid sequencing at scale, it is impractical and costly when utilized outside of library-scale validation. Here we present OnRamp (Oxford nanopore-based Rapid Analysis of Multiplexed Plasmids), an alternative method for routine plasmid validation which combines the advantages of NGSs full plasmid coverage and scalability with Sangers affordability and accessibility by leveraging nanopores novel long-read sequencing technology. We include customized wet-lab protocols for plasmid preparation along with a pipeline designed for analysis of read data obtained using these protocols. This analysis pipeline is built into the OnRamp webapp (http://OnRamp.BoyleLab.org), which generates alignments between actual and predicted plasmid sequences, quality scores, and read-level views in a user-friendly manner, precluding the need for programming experience in analyzing nanopore results. Here we describe the OnRamp protocols and pipeline, and demonstrate our ability to obtain full sequences from pooled plasmids while detecting sequence variation even in regions of high secondary structure, at less than half the cost of equivalent Sanger sequencing.

molecular biology↗

bioRxiv2021

Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome

Understanding the functional consequences of genetic variation in the non-coding regions of the human genome remains a challenge. We introduce here a computational tool, TURF, to prioritize regulatory variants with tissue-specific function by leveraging evidence from functional genomics experiments, including over three thousand functional genomics datasets from the ENCODE project provided in the RegulomeDB database. TURF is able to generate prediction scores at both organism and tissue/organ-specific levels for any non-coding variant on the genome. We present that TURF has an overall top performance in prediction by using validated variants from MPRA experiments. We also demonstrate how TURF can pick out the regulatory variants with tissue-specific function over a candidate list from associate studies. Furthermore, we found that various GWAS traits showed the enrichment of regulatory variants predicted by TURF scores in the trait-relevant organs, which indicates that these variants can be a valuable source for future studies.

bioinformatics↗