Structural Variants Research Articles

Three-dimensional genome landscape of primary human cancers.

Genome conformation underlies transcriptional regulation by distal enhancers, and genomic rearrangements in cancer can alter critical regulatory interactions. Here we profiled the three-dimensional genome architecture and enhancer connectome of 69 tumor samples spanning 15 primary human cancer types from The Cancer Genome Atlas. We discovered the following three archetypes of enhancer usage for over 100 oncogenes across human cancers: static, selective gain or dynamic rewiring. Integrative analyses revealed the enhancer landscape of noncancer cells in the tumor microenvironment for genes related to immune escape. Deep whole-genome sequencing and enhancer connectome mapping provided accurate detection and validation of diverse structural variants across cancer genomes and revealed distinct enhancer rewiring consequences from noncoding point mutations, genomic inversions, translocations and focal amplifications. Extrachromosomal DNA promoted more extensive enhancer rewiring among several types of focal amplification mechanisms. These results suggest a systematic approach to understanding genome topology in cancer etiology and therapy.

AbSet: A Standardized Data Set of Antibody Structures for Machine Learning Applications.

Machine learning algorithms have played a fundamental role in the development of therapeutic antibodies by being trained on data sets of sequences and/or structures. However, structural data sets remain limited, especially those that include antibody-antigen complexes. Additionally, many of the available structures are not standardized, and antibody-specific databases often do not provide molecular descriptors that could enhance ML models. To address this gap, we introduce AbSet, a curated dataset comprising over 800,000 antibody structures and corresponding molecular descriptors, including both experimentally determined and in silico-generated antibody-antigen complexes. We systematically retrieved antibody structures from the Protein Data Bank (PDB), applied rigorous standardization protocols, and expanded the dataset through large-scale protein-protein docking to generate structural variants of antibody-antigen interactions. Each model was classified as high, medium, acceptable, or incorrect quality based on structural similarity to reference experimental complexes. This classification enables both the construction of a decoy set of confirmed non-binders and the generation of high-confidence augmented structural data for machine learning applications. AbSet is publicly available via the Zenodo repository, with accompanying scripts hosted on GitHub (https://github.com/SFBBGroup/AbSet.git).

Laboratory evolution of the bacterial genome structure through insertion sequence activation.

The genome structure fundamentally shapes bacterial physiology, ecology, and evolution. Though insertion sequences (IS) are known drivers of drastic evolutionary changes in the genome structure, the process is typically slow and challenging to observe in the laboratory. Here, we developed a system to accelerate IS-mediated genome structure evolution by introducing multiple copies of a high-activity IS in Escherichia coli. We evolved the bacteria under relaxed neutral conditions, simulating those leading to IS expansion in host-restricted endosymbionts and pathogens. Strains accumulated a median of 24.5 IS insertions and underwent over 5% genome size changes within ten weeks, comparable to decades-long evolution in wild-type strains. The detected interplay of frequent small deletions and rare large duplications updates the view of genome reduction under relaxed selection from a simple consequence of the deletion bias to a nuanced picture including transient expansions. The high IS activity resulted in structural variants of IS and the emergence of composite transposons, illuminating potential evolutionary pathways for ISs and composite transposons. The extensive genome rearrangements we observed establish a baseline for assessing the fitness effects of IS insertions, genome size changes, and rearrangements, advancing our understanding of how mobile elements shape bacterial genomes.

Expanding biobank pharmacogenomics through machine learning calls of structural variation.

Biobanks linking genetic data with clinical health records provide exciting opportunities for pharmacogenomic (PGx) research on genetic variation and drug response. Designed as central and multi-use resources, biobanks can facilitate diverse PGx research efforts, including the study of drug efficacy and adverse effects. Specialized PGx alleles and phenotypes are critical for such studies and can be conveniently called from existing array-based genotypes routinely collected in most biobanks. We describe a central callset of PGx alleles and phenotypes in over 80,000 participants of the Michigan Genomics Initiative (MGI) biobank, created using the PyPGx software on TOPMed imputed genotypes. The array-based PGx allele calls demonstrate concordance (>92%) with a set of PCR-validated alleles collected during clinical care, but do not identify PGx alleles dependent on structural variation, including the clinically important CYP2D6*5 deletion. To address this, we developed a support vector machine trained on genotype array SNV probe intensities to classify CYP2D6*5 carriers. This method had >99% accuracy and reclassified ∼7% of African American and ∼4% of White MGI participants to lower activity metabolizer phenotypes, predicting higher risks of adverse drug reactions. We demonstrate that central PGx callsets created with existing tools and genetic data can be augmented by customized calls for challenging alleles based on structural variants to broaden the research potential and clinical utility of biobanks. These PGx callsets can be created in biobanks with existing array-based genotype data and highlight the utility of advanced computational methods in PGx allele identification.

Investigating the Performance of Oxford Nanopore Long-Read Sequencing with Respect to Illumina Microarrays and Short-Read Sequencing

Oxford Nanopore Technologies (ONT) long-read sequencing (LRS) has emerged as a promising genomic analysis tool, yet comprehensive benchmarks with established platforms across diverse datasets remain limited. This study aimed to benchmark LRS performance against Illumina short-read sequencing (SRS) and microarrays for variant detection across different genomic contexts and to evaluate the impact of experimental factors. We sequenced 14 human genomes using the three platforms and evaluated single nucleotide variants (SNVs), insertions/deletions (indels), and structural variants (SVs) detection, stratifying by high-complexity, low-complexity, and dark genome regions while assessing effects of multiplexing, depth, and read length. LRS SNV accuracy was slightly lower than that of SRS in high-complexity regions (F-measure: 0.954 vs. 0.967) but showed comparable sensitivity in low-complexity regions. LRS showed robust performance for small (1–5 bp) indels in high-complexity regions (F-measure: 0.869), but SRS agreement decreased significantly in low-complexity regions and for larger indel sizes. Within dark regions, LRS identified more indels than SRS, but showed lower base-level accuracy. LRS identified 2.86 times more SVs than SRS, excelling at detecting large variants (>6 kb), with SV detection improving with sequencing depth. Sequencing depth strongly influenced variant calling performance, whereas multiplexing effects were minimal. Our findings provide valuable insights for optimising LRS applications in genomic research and diagnostics.

Putting Structural Variants Into Practice: The Role of Chromosomal Inversions in the Management of Marine Environments.

Major threats to marine species and ecosystems include overfishing, invasive species, pollution and climate change. The changing climate not only imposes direct threats through the impacts of severe marine heatwaves, cyclones and ocean acidification but also complicates fisheries and invasive species management by driving species range shifts. The dynamic nature of these threats means that the future of our oceans will depend on the ability of species to adapt. This has led to calls for genetic interventions focussed on enhancing species' adaptive capacity, including translocations, restocking and selective breeding. Assessing the benefits and risks of such approaches requires an improved understanding of the genetic architecture of adaptive variation, not only in relation to climate-resilient phenotypes but also locally adapted populations and the fitness of hybrids. Large structural genetic variants such as chromosomal inversions play an important role in local adaptation by linking multiple adaptive loci. Consequently, inversions are likely to be particularly important when managing for adaptive capacity. However, under some circumstances, they also accumulate deleterious mutations, potentially increasing the risk of inbreeding depression. Genetic management that takes account of these dual roles on fitness is likely to be more effective at ensuring population persistence. We summarise evolutionary factors influencing adaptive and deleterious variation of inversions, review inversions found in marine taxa, and provide a framework to predict the consequences of ignoring inversions in key management scenarios. We conclude by describing practical methods to bridge the gap between evolutionary theory and practical application of inversions in conservation.

Genomic landscape of diffuse glioma revealed by whole genome sequencing

Diffuse gliomas are the commonest malignant primary brain tumour in adults. Herein, we present analysis of the genomic landscape of adult glioma, by whole genome sequencing of 403 tumours (256 glioblastoma, 89 astrocytoma, 58 oligodendroglioma; 338 primary, 65 recurrence). We identify an extended catalogue of recurrent coding and non-coding genetic mutations that represents a source for future studies and provides a high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and extrachromosomal DNA. Finally, we relate these to clinical outcome. As well as identifying drug targets for treatment of glioma our findings offer the prospect of improving treatment allocation with established targeted therapies.

A Systematic Review of the Advances and New Insights into Copy Number Variations in Plant Genomes

Copy number variations (CNVs), as an important structural variant in genomes, are widely present in plants, affecting their phenotype and adaptability. In recent years, CNV research has not only focused on changes in gene copy numbers but has also been linked to complex mechanisms such as genome rearrangements, transposon activity, and environmental adaptation. The advancement in sequencing technologies has made the detection and analysis of CNVs more efficient, not only revealing their crucial roles in plant disease resistance, adaptability, and growth development, but also demonstrating broad application potential in crop improvement, particularly in selective breeding and genomic selection. By studying CNV changes during the domestication process, researchers have gradually recognized the important role of CNVs in plant domestication and evolution. This article reviews the formation mechanisms of CNVs in plants, methods for their detection, their relationship with plant traits, and their applications in crop improvement. It emphasizes future research directions involving the integration of multi-omics to provide new perspectives on the structure and function of plant genomes.

Single-cross prediction with imputed multi-omic data: A case study in rapeseed.

Advancements in sequencing technologies enabled the assembly and characterization of plant genomes with high resolution. In breeding programs, this data is combined with phenotypic information in genomic prediction to select genotypes based on their genetic profiles. Although SNP arrays are commonly used for genotyping, they capture only a fraction of the genomewide diversity. To address this, one approach involves genotyping the entire population with arrays, sequence a subset using whole-genome sequencing (WGS) or assessing gene expression profiles, followed by imputing the data across the entire population. This study evaluates the effect of imputed WGS markers (SNPs and structural variants) and expression data on genomic prediction in a rapeseed hybrid breeding population. A combination of SNP arrays, WGS, and RNA sequencing was employed, followed by imputation of marker and expression data. Genomic prediction was utilized to estimate general and specific combining ability effects in untested hybrids. However, while adding imputed whole-genome and expression data increased marker density and linkage disequilibrium, it didn´t enhance prediction accuracy compared to SNP array data. This is attributed to redundancy in relationship, imputation errors, or environmental influences on gene expressions. This suggests that SNP arrays continue to be reliable for genomic prediction in rapeseed hybrid breeding.

Genomic signatures in plasma circulating tumor DNA reveal treatment response and prognostic insights in mantel cell lymphoma

BackgroundMantle cell lymphoma (MCL) is an aggressive subtype of B-cell non-Hodgkin’s lymphoma. The applicability of circulating tumor DNA (ctDNA) for predicting treatment response and prognosis in MCL remains underexplored.MethodsThis study included 34 MCL patients receiving first-line chemoimmunotherapy. We assessed the ability of plasma ctDNA to detect tumor-specific genetic alterations and explored its potential as a noninvasive biomarker for treatment response and prognosis in MCL.ResultsCommonly mutated genes in MCL included CCND1 (93.5%), ATM (48.4%), KMT2D (25.8%), and TP53 (25.8%). Subgroup analysis of tissue samples showed that CDKN2A mutations (P = 0.028), along with alterations in BCR and TCR signaling (P = 0.004) and the PI3K pathway (P = 0.008), were enriched in the blastoid subtype. ATM mutations (P = 0.041) were more prevalent in MIPI-low patients, while epigenetic chromatin remodeling pathway alterations (P = 0.028) were more common in MIPI-high patients. Plasma ctDNA demonstrated high sensitivity for detecting structural variants (96.6%), followed by mutations (71.3%) and copy number variants (30.0%). 75% of patients exhibited moderate-to-high concordance in detecting genomic variants between plasma and tissue samples. Pretreatment ctDNA levels exhibited high specificity in predicting clinical efficacy but had a suboptimal sensitivity of 68.2%. Higher ctDNA levels were significantly associated with shorter progression-free survival (PFS; P = 0.002) and overall survival (OS; P = 0.009). Additional ctDNA-based genetic features associated with shorter PFS included TP53 (P = 0.002), TRAF2 (P = 0.023), and SMARCA4 (P = 0.023) mutations, while TP53 (P = 0.006) and TERT (P = 0.031) mutations predicted shorter OS. Persistent positive ctDNA in post-treatment plasma samples indicated molecular relapse and poor prognosis, whereas undetectable ctDNA defined a subset of patients with favorable survival outcomes.ConclusionsThis study identified plasma ctDNA as a promising biomarker that noninvasively captures tumor-derived genetic variants associated with treatment response and survival outcomes in MCL, highlighting the clinical value of ctDNA for diagnosis, recurrence prediction, and surveillance monitoring.

Worth the Effort: Lessons for Discovery and Care From an Unusual Case of Gorlin Syndrome

ABSTRACTGorlin‐Goltz Syndrome (GGS) is a rare autosomal dominant genetic disorder encompassing a diverse range of clinical manifestations, including congenital anomalies and predisposition to cancer. Pathogenic variants in PTCH1 and SUFU account for up to 79% and 6% of cases, respectively. Currently, an estimated 15%–27% of individuals with a clinical diagnosis of GGS do not have a pathogenic variant identified in either gene. We report on a 17‐year‐old female referred to the Undiagnosed Disease Network with a clinical diagnosis of GGS that manifested as both classic and unusual findings, including isolated hypogonadotropic hypogonadism and anosmia (Kallmann syndrome), orofacial cleft, and abnormal semicircular canals (SCC). Prior genetic testing, including a targeted gene panel, genomic microarray, exome sequencing, and genome sequencing, was non‐diagnostic, although these studies identified a variant of uncertain significance in CHD7, which may have contributed to elements of the phenotype (e.g., abnormal SCC). Reanalysis of genome sequencing data using research analytic methods, together with karyotyping, FISH, and Sanger sequencing, identified a novel de novo paracentric inversion that truncated PTCH1. These findings underscore the value of in‐depth phenotype‐guided genomic analysis, including chromosomal structural variants, as well as the occurrence of possible dual genetic diagnoses in the same individuals. Moreover, the definitive diagnosis provided the patient and family with a firmer basis for management and counseling.

Validation of a comprehensive long-read sequencing platform for broad clinical genetic diagnosis

Though short read high-throughput sequencing, commonly known as Next-Generation Sequencing (NGS), has revolutionized genomics and genetic testing, there is no single genetic test that can accurately detect single nucleotide variants (SNVs), small insertions/deletions (indels), complex structural variants (SVs), repetitive genomic alterations, and variants in genes with highly homologous pseudogenes. The implementation of a unified comprehensive technique that can simultaneously detect a broad spectrum of genetic variation would substantially increase efficiency of the diagnostic process. The current study evaluated the clinical utility of long-read sequencing as a comprehensive genetic test for diagnosis of inherited conditions. Using Oxford Nanopore Technologies long read nanopore sequencing, we successfully developed and validated a clinically deployable integrated bioinformatics pipeline that utilizes a combination of eight publicly available variant callers. A concordance assessment comparing the known variant calls from a well-characterized, benchmarked sample called NA12878 from the National Institute of Standards and Technology (NIST) with the variants detected by our pipeline for this sample, determined that the analytical sensitivity of our pipeline was 98.87% and the analytical specificity exceeded 99.99%. We then evaluated our pipeline’s ability to detect 167 clinically relevant variants from 72 clinical samples. This set of variants consisted of 80 SNVs, 26 indels, 32 SVs, and 29 repeat expansions, including 14 variants in genes with highly homologous pseudogenes. The overall detection concordance for these clinically relevant variants was 99.4% (95% CI: 99.7%–99.9%). Importantly, in addition to detecting known clinically relevant variants, in four cases, our pipeline yielded valuable additional information in support of clinical diagnoses that could not have been established using short-read NGS alone. Our findings suggest that long-read sequencing is successful in identifying diverse genomic alterations and that our pipeline functions well as the basis for a single diagnostic test for patients with suspected genetic disease.

GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.

Structural variants (SVs) in microbial genomes play a critical role in phenotypic changes, environmental adaptation, and species evolution, with deletion variations particularly closely linked to phenotypic traits. Therefore, accurate and comprehensive identification of deletion variations is essential. Although long-read sequencing technology can detect more SVs, its high error rate introduces substantial noise, leading to high false-positive and low recall rates in existing SV detection algorithms. This paper presents an SV detection method based on graph convolutional networks (GCNs). The model first represents node features through a heterogeneous graph, leveraging the GCN to precisely identify variant regions. Additionally, a knowledge-augmented activation layer (KANLayer) with a learnable activation function is introduced to reduce noise around variant regions, thereby improving model precision and reducing false positives. A clustering algorithm then aggregates multiple overlapping regions near the variant center into a single accurate SV interval, further enhancing recall. Validation on both simulated and real datasets demonstrates that our method achieves superior F1 scores compared to benchmark methods (cuteSV, Sniffles, Svim, and Pbsv), highlighting its advantage and robustness in SV detection and offering an innovative solution for microbial genome structural variation research.

Validation studies and multiomics analysis of Zhx2 as a candidate quantitative trait gene underlying brain oxycodone metabolite (oxymorphone) levels and behavior.

Sensitivity to the subjective reinforcing properties of opioids has a genetic component and can predict addiction liability of opioid compounds. We previously identified Zhx2 as a candidate gene underlying increased brain concentration of the oxycodone (OXY) metabolite oxymorphone (OMOR) in BALB/cJ (J) versus BALB/cByJ (By) females that could increase OXY state-dependent reward. A large structural intronic variant is associated with a robust reduction of Zhx2 expression in J mice, which we hypothesized enhances OMOR levels and OXY addiction-like behaviors. We tested this hypothesis by restoring the Zhx2 loss-of-function in J mice (mouse endogenous retroviral element knockout) and modeling the loss-of-function variant through knocking out the Zhx2 coding exon (exon 3 knockout [E3KO]) in By mice and assessing brain OXY metabolite levels and behavior. Consistent with our hypothesis, Zhx2 E3KO females showed an increase in brain OMOR levels and OXY-induced locomotor activity. However, contrary to our hypothesis, state-dependent expression of OXY conditioned place preference decreased in E3KO females and increased in E3KO males. We also overexpressed Zhx2 in the livers and brains of J mice and observed Zhx2 overexpression in select brain regions that was associated with reduced OXY state-dependent learning. Integrative transcriptomic and proteomic analysis of E3KO mice identified astrocyte function, cell adhesion, extracellular matrix properties, and endothelial cell functions as pathways influencing brain OXY metabolite concentration and behavior. These results support Zhx2 as a quantitative trait gene underlying brain OMOR concentration that is associated with changes in OXY behavior and implicate potential quantitative trait mechanisms that together inform our overall understanding of Zhx2 in brain function. SIGNIFICANCE STATEMENT: This study validated Zhx2 as a gene whose dysfunction increases brain levels of a highly potent and addictive metabolite of oxycodone, oxymorphone, in a female-specific manner. This result has broad implications for understanding the role of oxycodone metabolism and brain oxymorphone levels in the addiction liability of oxycodone (the active ingredient in OxyContin) and highlights the need for the study of sex differences in opioid metabolism as it relates to the addiction liability of opioids and opioid use disorder.

Implementation and clinical utility of multigene panels for bleeding, platelet, and thrombotic disorders.

Implementation and clinical utility of multigene panels for bleeding, platelet, and thrombotic disorders.

The most common structural variant expected at the GBA1 locus may be detected by a simple amplification method: Implications for screening Parkinson’s disease variants

The most common structural variant expected at the GBA1 locus may be detected by a simple amplification method: Implications for screening Parkinson’s disease variants

Integration of Optical Genome Mapping in the Cytogenomic and Molecular Work-Up of Hematological Malignancies: Expert Recommendations From the International Consortium for Optical Genome Mapping.

The latest updates to the classification of hematolymphoid malignancies using the World Health Organization (WHO, 5th ed.) and ICC (International Consensus Classification) criteria highlight the critical need for comprehensive and precise cytogenomic data for diagnosis, prognostication, and treatment. This presents significant challenges for clinical laboratories, requiring a complex workflow using multiple assays to detect different types of structural chromosomal variants (copy number changes, fusions, inversions) across the entire genome. Optical genome mapping (OGM) is an advanced cytogenomic tool for genome-wide detection of structural chromosomal alterations at the gene/exon level. Studies demonstrate that OGM facilitates the identification of novel cytogenomic biomarkers, improves risk stratification, and expands therapeutic targets and personalized treatment strategies. OGM is easy to implement and highly accurate in detecting structural variants (SVs) across various diagnostic entities. Consequently, many centers are integrating OGM into the clinical cytogenetic workflow for hematological malignancies. However, systemic clinical adoption has remained limited due to the lack of expert recommendations on clinical indications, testing algorithms, and result interpretation. To address this, experts from the International Consortium for OGM and relevant multidisciplinary fields developed recommendations for the integration of OGM as a standard-of-care cytogenetic assay for the diagnostic workflow in various clinical settings. These recommendations standardize the use of OGM across laboratories, ensure high-quality cytogenetic data, guide clinical trial design and development, and provide a basis for updates to diagnostic and classification models.

Hf6Al7 and Hf4.44(1)Nb1.56(1)Al7 – The First Fully Ordered Main Group Metal Containing W6Fe7 Type Compound and its Ternary Coloring Variant

Attempts to synthesize Hf4NbAl7 led to the discovery of nominal Hf4Nb2Al7 instead of the envisioned product. It was identified based on powder diffraction data. The compound can be described as a substitutional / coloring variant of the rhombohedral W6Fe7 type structure (space group R‐3m). The formation of the ternary compound motivated the synthesis of the binary compound Hf6Al7. Different synthetic strategies led to its discovery. The crystal structures of the binary as well as its ternary coloring variant were refined from single‐crystal X‐ray diffraction data showing both the same obverse‐reverse twinning. For the Nb containing phase, a composition of Hf4.44(1)Nb1.56(1)Al7 was refined, in agreement with the powder X‐ray data. Solid state 27Al NMR investigations indicate the formation of a crystalline compound, however, only one distinct signal can be observed in contrast to the two crystallographic Al positions. Magnetic susceptibility measurements confirm the expected Pauli‐paramagnetic character. Quantum‐chemical calculations alongside analyses of the chemical bonding with the LOBSTER program package showed that a strong Hf–Hf interaction is present, which transforms into an even stronger Nb–Nb interaction in nominal Hf4Nb2Al7. For the latter, three substitutional variants were calculated clearly indicating the experimentally observed one as the most stable.

Recombination and transposition drive genomic structural variation potentially impacting life history traits in a host-generalist fungal plant pathogen

BackgroundAn understanding of plant pathogen evolution is important for sustainable management of crop diseases. Plant pathogen populations must maintain adequate heritable phenotypic variability to survive. Polymorphisms ≥ 50 bp, known as structural variants (SVs), could contribute strongly to this variability by disrupting gene activities. SV acquisition is largely driven by mobile genetic elements called transposons, though a less appreciated source of SVs is erroneous meiotic double-strand break repair. The relative impacts of transposons and recombination on SV diversity and the overall contribution of SVs to phenotypic variability is elusive, especially in host generalists.ResultsWe use 25 high-quality genomes to create a graphical pan-genome of the globally distributed host-generalist crop pathogen Sclerotinia sclerotiorum. Outcrossing and recombination rates in this self-fertile species have been debated. Using bisulfite sequencing and short-read data from 190 strains, we show that S. sclerotiorum has many hallmarks of eukaryotic meiosis, including recombination hot and cold spots, centromeric and genic recombination suppression, and rapid linkage disequilibrium decay. Using a new statistic that captures average pairwise structural variation, we show that recombination and transposons make distinct contributions to SV diversity. Furthermore, despite only 5% of genes being dispensable, SVs often had a stronger impact than other variants across 14 life history traits measured in 103 distinct strains.ConclusionsTransposons and recombination make distinct contributions to SV diversity in S. sclerotiorum. Despite limited gene content diversity, SVs may strongly impact phenotypic variability. This sheds light on the genomic forces shaping adaptive flexibility in host generalists.

BamSliceR: a Bioconductor package for rapid, cross-cohort variant and allelic bias analysis

Abstract Motivation The NCI Genomic Data Commons (GDC) provides controlled access to sequencing data from thousands of subjects, enabling large-scale study of impactful genetic alterations such as both simple and complex germline and structural variants. However, efficient analysis requires significant computational resources and expertise, especially when calling variants from raw sequence reads. To solve these problems, we developed bamSliceR, a R/Bioconductor package that builds upon the GenomicDataCommonspackage to extract aligned sequence reads from cross-GDC meta-cohorts, followed by targeted analysis of variants and effects (including transcript-aware variant annotation from transcriptome-aligned GDC RNA data). Results Here we demonstrate population-scale genomic & transcriptomic analyses with minimal compute burden using bamSliceR, identifying recurrent, clinically relevant sequence and structural variants in the TARGET AML and BEAT-AML cohorts. We then validate results in the (non-GDC) Leucegene cohort, demonstrating how the bamSliceR pipeline can be seamlessly applied to replicate findings in non-GDC cohorts. These variants directly yield clinically impactful and biologically testable hypotheses for mechanistic investigation Availability and implementation bamSliceR has been submitted to the Bioconductor project, where it is presently under review, and is available on GitHub at https://github.com/trichelab/bamSliceR