Biology subjects

Mills, R. E.

Publications and source records attributed to Mills, R. E..

12 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↗

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

Domain-specific embeddings uncover latent genetics knowledge

The inundating rate of scientific publishing means every researcher will miss new discoveries from overwhelming saturation. To address this limitation, we employ natural language processing to overcome human limitations in reading, curation, and knowledge synthesis, with domain-specific applications to genetics and genomics. We construct a corpus of 3.5 million normalized genetics and genomics abstracts and implement both semantic and network-based embedding models. Our methods not only capture broad biological concepts and relationships but also predict complex phenomena such as gene expression. Through a rigorous temporal validation framework, we demonstrate that our embeddings successfully predict gene-disease associations, cancer driver genes, and experimentally-verified protein interactions years before their formal documentation in literature. Additionally, our embeddings successfully predict experimentally verified gene-gene interactions absent from the literature. These findings demonstrate that substantial undiscovered knowledge exists within the collective scientific literature and that computational approaches can accelerate biological discovery by identifying hidden connections across the fragmented landscape of scientific publishing.

bioinformatics↗

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↗

bioRxiv2023

Mapping the Complex Genetic Landscape of Human Neurons

When somatic cells acquire complex karyotypes, they are removed by the immune system. Mutant somatic cells that evade immune surveillance can lead to cancer. Neurons with complex karyotypes arise during neurotypical brain development, but neurons are almost never the origin of brain cancers. Instead, somatic mutations in neurons can bring about neurodevelopmental disorders, and contribute to the polygenic landscape of neuropsychiatric and neurodegenerative disease. A subset of human neurons harbors idiosyncratic copy number variants (CNVs, "CNV neurons"), but previous analyses of CNV neurons have been limited by relatively small sample sizes. Here, we developed an allele-based validation approach, SCOVAL, to corroborate or reject read-depth based CNV calls in single human neurons. We applied this approach to 2,125 frontal cortical neurons from a neurotypical human brain. This approach identified 226 CNV neurons, as well as a class of CNV neurons with complex karyotypes containing whole or substantial losses on multiple chromosomes. Moreover, we found that CNV location appears to be nonrandom. Recurrent regions of neuronal genome rearrangement contained fewer, but longer, genes.

neuroscience↗

bioRxiv2022

Human commensal Candida albicans strains demonstrate substantial within-host diversity and retained pathogenic potential

Candida albicans is a frequent colonizer of human mucosal surfaces as well as an opportunistic pathogen. C. albicans is remarkably versatile in its ability to colonize diverse host sites with differences in oxygen and nutrient availability, pH, immune responses, and resident microbes, among other cues. It is unclear how the genetic background of a commensal colonizing population can influence the shift to pathogenicity. Therefore, we undertook an examination of commensal isolates from healthy donors with a goal of identifying site-specific phenotypic adaptation and genetic variation associated with these phenotypes. We demonstrate that healthy people are reservoirs for genotypically and phenotypically diverse C. albicans strains, and that this genetic diversity includes both SNVs and structural rearrangements. Using limited diversity exploitation, we identified a single nucleotide change in the uncharacterized ZMS1 transcription factor that was sufficient to drive hyper invasion into agar. However, our commensal strains retained the capacity to cause disease in systemic models of infection, including outcompeting the SC5314 reference strain during systemic competition assays. This study provides a global view of commensal strain variation and within-host strain diversity of C. albicans and suggests that selection for commensalism in humans does not result in a fitness cost for invasive disease.

microbiology↗

bioRxiv2021

SearcHPV: a novel approach to identify and assemble human papillomavirus-host genomic integration events in cancer

BackgroundHuman papillomavirus (HPV) is a well-established driver of malignant transformation in a number of sites including head and neck, cervical, vulvar, anorectal and penile squamous cell carcinomas; however, the impact of HPV integration into the host human genome on this process remains largely unresolved. This is due to the technical challenge of identifying HPV integration sites, which includes limitations of existing informatics approaches to discover viral-host breakpoints from low read coverage sequencing data. MethodsTo overcome this limitation, we developed a new HPV detection pipeline called SearcHPV based on targeted capture technology and applied the algorithm to targeted capture data. We performed an integrated analysis of SearcHPV-defined breakpoints with genome-wide linked read sequencing to identify potential HPV-related structural variations. ResultsThrough analysis of HPV+ models, we show that SearcHPV detects HPV-host integration sites with a higher sensitivity and specificity than two other commonly used HPV detection callers. SearcHPV uncovered HPV integration sites adjacent to known cancer-related genes including TP63 and MYC, as well as near regions of large structural variation. We further validated the junction contig assembly feature of SearcHPV, which helped to accurately identify viral-host junction breakpoint sequences. We found that viral integration occurred through a variety of DNA repair mechanisms including non-homologous end joining, alternative end joining and microhomology mediated repair. ConclusionsIn summary, we show that SearcHPV is a new optimized tool for the accurate detection of HPV-human integration sites from targeted capture DNA sequencing data.

bioinformatics↗

bioRxiv2021

Cas9 targeted enrichment of mobile elements using nanopore sequencing

Mobile element insertions (MEIs) are highly repetitive genomic sequences that contribute to inter- and intra-individual genetic variation and can lead to genetic disorders. Targeted and whole-genome approaches using short-read sequencing have been developed to identify reference and non-reference MEIs; however, the read length hampers detection of these elements in complex genomic regions. Here, we pair Cas9 targeted nanopore sequencing with computational methodologies to capture active MEIs in human genomes. We demonstrate parallel enrichment for distinct classes of MEIs, averaging 44% of reads on targeted signals. We show an individual flow cell can recover a remarkable fraction of MEIs (97% L1Hs, 93% AluYb, 51% AluYa, 99% SVA_F, and 65% SVA_E). We identify twenty-one non-reference MEIs in GM12878 overlooked by modern, long-read analysis pipelines, primarily in repetitive genomic regions. This work introduces the utility of nanopore sequencing for MEI enrichment and lays the foundation for rapid discovery of elusive, repetitive genetic elements.

genetics↗

bioRxiv2020

Genome Diversity in Ukraine

The main goal of this collaborative effort is to provide genome wide data for the previously underrepresented population in Eastern Europe, and to provide cross-validation of the data from genome sequences and genotypes of the same individuals acquired by different technologies. We collected 97 genome-grade DNA samples from consented individuals representing major regions of Ukraine that were consented for the public data release. DNBSEQ-G50 sequences, and genotypes by an Illumina GWAS chip were cross-validated on multiple samples, and additionally referenced to one sample that has been resequenced by Illumina NovaSeq6000 S4 at high coverage. The genome data has been searched for genomic variation represented in this population, and a number of variants have been reported: large structural variants, indels, CNVs, SNPs and microsatellites. This study provides the largest to-date survey of genetic variation in Ukraine, creating a public reference resource aiming to provide data for historic and medical research in a large understudied population. While most of the common variation is shared with other European populations, this survey of population variation contributes a number of novel SNPs and structural variants that have not been reported in the gnomAD/1KG databases representing global distribution of genomic variation. These endemic variants will become a valuable resource for designing future population and clinical studies, help address questions about ancestry and admixture, and will fill a missing place in the puzzle characterizing human population diversity in Eastern Europe. Our results indicate that genetic diversity of the Ukrainian population is uniquely shaped by the evolutionary and demographic forces, and cannot be ignored in the future genetic and biomedical studies. This data will contribute a wealth of new information bringing forth different risk and/or protective alleles. The newly discovered low frequency and local variants can be added to the current genotyping arrays for genome wide association studies, clinical trials, and in genome assessment of proliferating cancer cells.

genomics↗

bioRxiv2020

Expectations and blind spots for structural variation detection from short-read alignment and long-read assembly

Virtually all genome sequencing efforts in national biobanks, complex and Mendelian disease programs, and emerging clinical diagnostic approaches utilize short-reads (srWGS), which present constraints for genome-wide discovery of structural variants (SVs). Alternative long-read single molecule technologies (lrWGS) offer significant advantages for genome assembly and SV detection, while these technologies are currently cost prohibitive for large-scale disease studies and clinical diagnostics (∼5-12X higher cost than comparable coverage srWGS). Moreover, only dozens of such genomes are currently publicly accessible by comparison to millions of srWGS genomes that have been commissioned for international initiatives. Given this ubiquitous reliance on srWGS in human genetics and genomics, we sought to characterize and quantify the properties of SVs accessible to both srWGS and lrWGS to establish benchmarks and expectations in ongoing medical and population genetic studies, and to project the added value of SVs uniquely accessible to each technology. In analyses of three trios with matched srWGS and lrWGS from the Human Genome Structural Variation Consortium (HGSVC), srWGS captured ∼11,000 SVs per genome using reference-based algorithms, while haplotype-resolved assembly from lrWGS identified ∼25,000 SVs per genome. Detection power and precision for SV discovery varied dramatically by genomic context and variant class: 9.7% of the current GRCh38 reference is defined by segmental duplications (SD) and simple repeats (SR), yet 91.4% of deletions that were specifically discovered by lrWGS localized to these regions. Across the remaining 90.3% of the human reference, we observed extremely high concordance (93.8%) for deletions discovered by srWGS and lrWGS after error correction using the raw lrWGS reads. Conversely, lrWGS was superior for detection of insertions across all genomic contexts. Given that the non-SD/SR sequences span 90.3% of the GRCh38 reference, and encompass 95.9% of coding exons in currently annotated disease associated genes, improved sensitivity from lrWGS to discover novel and interpretable pathogenic deletions not already accessible to srWGS is likely to be incremental. However, these analyses highlight the added value of assembly-based lrWGS to create new catalogues of functional insertions and transposable elements, as well as disease associated repeat expansions in genomic regions previously recalcitrant to routine assessment.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2019

A robust benchmark for germline structural variant detection

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls [≥]50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.

genomics↗

bioRxiv2018

Translation of upstream open reading frames in a model of neuronal differentiation

Upstream open reading frames (uORFs) initiate translation within mRNA 5 leaders, and have the potential to alter main coding sequence (CDS) translation on transcripts in which they reside. Ribosome profiling (RP) studies suggest that translating ribosomes are pervasive within 5 leaders across model systems. However, the significance of this observation remains unclear. To explore a role for uORF usage in neuronal differentiation, we performed RP on undifferentiated and differentiated human neuroblastoma cells. Using a spectral coherence algorithm (SPECtre), we identify 4,954 uORFs across 31% of all neuroblastoma transcripts. These uORFs predominantly utilize non-AUG initiation codons and exhibit translational efficiencies (TE) comparable to annotated coding regions. Usage of both AUG initiated uORFs and a conserved and consistently translated subset of non-AUG initiated uORFs correlates with repressed CDS translation. Ribosomal protein transcripts are enriched in uORFs, and select uORFs on such transcripts were validated for expression. With neuronal differentiation, we observed an overall positive correlation between translational shifts in uORF/CDS pairs. However, a subset of transcripts exhibit inverse shifts in translation of uORF/CDS pairs. These uORFs are enriched in AUG initiation sites, non-overlapping, and shorter in length.\n\nCumulatively, CDSs downstream of uORFs characterized by persistent translation show smaller shifts in TE with neuronal differentiation relative to CDSs without a predicted uORF, suggesting that fluctuations in CDS translation are buffered by uORF translation. In sum, this work provides insights into the dynamic relationships and potential regulatory functions of uORF/CDS pairs in a model of neuronal differentiation.

molecular biology↗