Biology subjects

Murray, L.

Publications and source records attributed to Murray, L..

14 recordsLinked to original sources

bioRxiv2026

The Genome In A Bottle HG002 assembly-based variant benchmark set enables comprehensive benchmarking of small and structural variants.

High-accuracy, high-resolution benchmark data are fundamental to improving the accuracy and generalizability of sequencing technologies and associated algorithms. Most existing benchmarks for DNA sequencing rely on mapping to a single linear reference, omitting inaccessible and highly repetitive complex genomic regions. Here, we present the Genome in a Bottle (GIAB) Consortium's HG002 v5.0q benchmark set, a more comprehensive variant benchmark derived from a fully phased, highly curated, and accurate diploid assembly of HG002. Our approach leverages recent advances in long-read sequencing and assembly to provide confident small- and structural-variant calls across previously inaccessible regions, including segmental duplications, homopolymers, tandem repeats, and complex immune loci. The v5.0 benchmark significantly expands the callable genome relative to v4.2.1, adding x200 Mbp of sequence and increasing variant detection by 18 % for small variants and 3-fold for structural variants compared to previous benchmarks. The benchmark regions include more variants in repetitive regions, including 1.2 x segmental duplications, 1.8 x tandem repeats, and 2.0 x homopolymers, compared to v4.2.1. Separate and combined small- and structural-variant benchmarks are available for three versions of the human reference genome (GRCh37, GRCh38, and CHM13). We validate the reliability of this benchmark set by comparing multiple sequencing technologies and variant calling pipelines and curating differences. The v5.0q benchmark provides a critical resource for the development and validation of next-generation sequencing technologies and analysis methods.

genomics↗

bioRxiv2026

A complete human pancreatic cancer genome

Cancer genome sequencing is essential for understanding tumor evolution and advancing precision medicine.1 However, reference gaps and germline variants obscure detection of small and large somatic variants and methylation in repetitive regions.1-3 It is common for tumor cells to gain or lose chromosome arms due to somatic structural changes that occur inside highly repetitive satellite DNA sequences in the centromeres.4 To identify the full spectrum of somatic variants, including complex rearrangements, we construct and curate near-complete, haplotype-resolved assemblies of the most recent common ancestor of an early-passage broadly-consented hypodiploid pancreatic cancer cell line and matched normal tissues. The tumor assembly completely recapitulates all 35 tumor chromosomes observed with karyotyping, with multiple translocation-induced hybrid chromosomes. The hybrid chromosomes contain putative functional dicentric and fused centromeres, nested foldback inversions causing 14 breakpoints with a haplotype switch in a single event, and centromeric satellite tandem duplications up to 136 kbp. Direct comparison of tumor and normal assembly haplotypes uncovers >7,000 variants altering >1 Mbp of sequence in repetitive regions that have been hidden by reference gaps and germline variants. 44 % of somatic small variants change representation because they alter germline variants on GRCh38, impacting mutational signatures and kataegis/omikli clusters. Most somatic LINE insertions originate from two hypomethylated non-reference germline LINE insertions, highlighting their impact on insertion mutation burden. These assemblies demonstrate that centromeric, acrocentric, and telomeric regions conventionally excluded from analysis harbor extensive somatic and epigenetic changes. Resolving complete tumor genomes enables a deeper understanding of cancer structural plasticity and the endpoints of breakage-fusion-bridge cycles. These assembled, curated paired normal-tumor benchmarks will serve as a critical foundation for developing future algorithms to characterize the most intractable regions of cancer genomes.

genomics↗

bioRxiv2026

Brain morphological pattern is associated with the presence, severity, and transition of transdiagnostic psychiatric disorders in preadolescents

Cognitive function, psychological processes, mental states, and behaviors are key dimensions of human subjective experience that separately relate to mental disorders across diagnostic categories. However, whether these dimensions are linked to common or distinct brain morphological patterns that convey risk or resilience for psychiatric disorders remains unclear. The current study is a longitudinal investigation on 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study aged 9-10 years at baseline. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of cortical morphology (4 metrics: surface area, cortical and subcortical volume, cortical thickness, and sulcal/gyral depth) with multidomain behavioral assessments including cognitive scores and psychological measures indexing motivation, impulse control, mental states, and behaviors across a normative continuum from healthy to pathological. Across morphological measures, we identified a robust latent brain structural variate that correlated positively with cognitive performance and negatively with psychological measures indexing greater psychology. Notably, higher scores on this brain variate reflected larger cortical surface area and cortical volume--especially in the temporal gyri--together with a posterior-anterior gradient in cortical thickness, showing relatively greater thickness in occipital, parietal, and temporal cortices and lower thickness in cingulate and frontal regions. This brain variate and the related cognitive-psychological-behavioral variate remained stable at the 2-year follow-up, demonstrating temporal consistency. Importantly, the brain variate showed a dose-dependent relationship with the cumulative number of psychiatric diagnoses assessed concurrently and at 2-year follow-up, with lower brain variate scores being associated with higher numbers of comorbid diagnoses. In addition, the brain scores were associated with longitudinal transitions between healthy and diagnosed states over the 2-year study period, in which lower scores at baseline were associated with persistent psychiatric diagnoses whereas higher scores at baseline were associated with persistent healthy states, suggesting that the brain scores capture a vulnerability- resilience continuum for psychopathology. By revealing shared brain structural substrates across conventional diagnostic boundaries, these findings advance the neurodevelopmental understanding of psychiatric disorders and highlight the potential utility of morphology-informed approaches for early screening and intervention in youth.

developmental biology↗

bioRxiv2025

Cardiac myofibril networks induce shear stress

Myofibril arrangement is critical to cardiac muscle function in health and disease. Historically, analysis of the impact of myofibril organisation on force and cell contraction has relied on the assumption of uniaxial arrays. However, improvements in imaging indicate that myofibrils form complex networks, though how these networks modulate force has yet to be explored. Here, morphological analysis of sheep left-ventricular cardiomyocytes is utilised to inform a non-linear finite element model of cell contraction. Analysis of deep learning segmentations of Z-Discs demonstrate that myofibrils are oriented about the major axis (mean = 0.03{degrees}) but deviate locally by up to 30{degrees} (standard deviation = 6.56{degrees}). Simulations produce unique deformations for geometries informed by myofibril orientations, displaying internal rotation and off-axis deformations. Moreover, anisotropy generates shear stresses distinct from the uniaxial case, demonstrating spatial relationships that balance shear across the cell. These findings highlight the impact of myofibril networks on forces during cell contraction.

biophysics↗

bioRxiv2025

Maturation of Dorsal Association Tracts during Preadolescence Links to Concurrent and Future Cognitive Performance and Transdiagnostic Psychopathology

Many psychiatric disorders begin during adolescence, coinciding with the rapid development of brain white matter (WM). However, it remains unclear whether deviations from normal WM maturation during this age period contribute to the development of psychopathology. In this study, we developed and validated normative models of brain age based on specific WM tracts using three large-scale developmental datasets (a total of [~]10,000 subjects). We found that tract-specific deviations in WM development of association and limbic/subcortical systems were linked to concurrent cognition and psychopathology. The spatial pattern of the association system aligned closely with distributions of high-order brain networks, and with mitochondrial content and respiratory capacity. The maturation of the association system contributed significantly to better cognitive performance assessed two or three years later. Importantly, delayed WM development especially in dorsal association tracts predicted psychiatric disorders across diagnoses and disorder onset over a 2-year follow-up. By identifying tract-specific WM development during preadolescence as a predictor of cognitive capacity and psychiatric disorder risks, this study provides a valuable framework for tracking individualized brain maturation and understanding the neurobiological underpinnings of cognitive performance and transdiagnostic psychopathology.

neuroscience↗

bioRxiv2025

Intra- and intercellular immune responses across diverse in vitro stimuli and inflammatory disease

In vitro stimulation of healthy human immune cells is commonly used to reproduce the immune states observed in disease, both to understand pathology and to test therapeutic approaches. However, experiments typically focus on individual cell types and stimuli and a comprehensive cellular comparison of common immunomodulators and their relevance to disease is lacking. To this end, we performed integrated single-cell transcriptomic profiling of human peripheral blood mononuclear cells treated with 11 different common in vitro stimuli, totalling over 150,000 cells from 21 immune cell subtypes. Comparative analysis of the immunomodulations revealed their shared and unique pathways, for instance stimulation via the T cell receptor (anti-CD3, CytoStimTM) and IFN- induced broad activation signatures including off-target effects across multiple cell types, whereas TNF- and LPS elicited more specific responses. Ligand-receptor interaction mapping also uncovered the common and distinct intercellular signalling pathways across stimuli. Comparing the stimuli to patient samples enabled identification of specific inflammatory disease features best replicated by each. For example, IFN- stimulation recapitulated signatures of SLE across cell types, whereas LPS induced SLE-like changes specifically within monocytes. Comparative cell-cell network analysis showed that in vitro stimuli were able to recreate some, but not all, aspects of intercellular interactions upregulated in SLE, highlighting the limitations of these model systems. This resource provides new insights into the similarities and differences of established immune stimuli at cellular resolution and facilitates appropriate use of in vitro systems to study pathways relevant to disease.

immunology↗

bioRxiv2024

Machine Learning Predictions Surpass Individual mRNAs as a Proxy of Single-cell Protein Expression

BackgroundExpansive repositories of single-cell RNA-seq data are now available. These data are often analysed assuming that mRNA abundance reflects the expression of their cognate proteins. However, post-transcriptional and translational regulation make mRNA an inadequate proxy for protein. High sparsity in low abundance mRNAs from single-cell transcriptomics data further complicates the extrapolation of protein expression levels. Although methods for single-cell surface protein quantification exist, they incur additional technical steps at greater expense and have yet to see wide-spread adoption. Computational approaches for protein imputation from scRNAseq data have been published, which learn transcriptome-wide patterns that predict protein expression. These models can then be applied to infer surface protein expression on RNA-seq only data, to increase the utility of existing data repositories. ResultsWe tested 8 such methods and compared the accuracy of predictions between approaches, and against cognate mRNAs as a direct proxy. Predictions from the trained models outperformed the use of mRNA expression as a proxy. We identify notable cases of cell surface proteins with very poor correlation with their mRNA that were predicted very successfully by imputation using the whole transcriptome. We find cell type signatures are a major determinant of predicted protein levels and, as such, prediction methods require representative training data. ConclusionsThese results reiterate that mRNA level is not a reliable predictor of cell surface protein expression, and that whole-transcriptome informed imputation can improve protein estimations given appropriately trained models.

bioinformatics↗

bioRxiv2024

A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.

Somatic mosaicism is an important cause of disease, but mosaic and somatic variants are often challenging to detect because they exist in only a fraction of cells. To address the need for benchmarking subclonal variants in normal cell populations, we developed a benchmark containing mosaic variants in the Genome in a Bottle Consortium (GIAB) HG002 reference material DNA from a large batch of a normal lymphoblastoid cell line. First, we used a somatic variant caller with high coverage (300x) Illumina whole genome sequencing data from the Ashkenazi Jewish trio to detect variants in HG002 not detected in at least 5% of cells from the combined parental data. These candidate mosaic variants were subsequently evaluated using >100x BGI, Element, and PacBio HiFi data. High confidence candidate SNVs with variant allele fractions above 5% were included in the HG002 draft mosaic variant benchmark, with 13/85 occurring in medically relevant gene regions. We also delineated a 2.45 Gbp subset of the previously defined germline autosomal benchmark regions for HG002 in which no additional mosaic variants >2% exist, enabling robust assessment of false positives. The variant allele fraction of some mosaic variants is different between batches of cells, so using data from the homogeneous batch of reference material DNA is critical for benchmarking these variants. External validation of this mosaic benchmark showed it can be used to reliably identify both false negatives and false positives for a variety of technologies and detection algorithms, demonstrating its utility for optimization and validation. By adding our characterization of mosaic variants in this widely-used cell line, we support extensive benchmarking efforts using it in simulation, spike-in, and mixture studies.

bioinformatics↗

bioRxiv2023

Small variant benchmark from a complete assembly of X and Y chromosomes

The sex chromosomes contain complex, important genes impacting medical phenotypes, but differ from the autosomes in their ploidy and large repetitive regions. To evaluate variant detection on chromosomes X and Y, we created an 111,725 variant benchmark for the Genome in a Bottle HG002 reference material. We show how complete assemblies can expand benchmarks to difficult regions, but highlight remaining challenges benchmarking complex gene conversions, copy number variable gene arrays, and human satellites.

genomics↗

bioRxiv2023

Somatic small-variant calling methods in Illumina DRAGEN™ Secondary Analysis

We present the DRAGEN somatic pipeline for calling small somatic variants from tumor samples, with or without paired normal samples. The DRAGEN somatic variant caller offers 1) a flexible architecture that can be used on a wide array of somatic use cases; 2) built-in noise models enabling robustness against various sources of noise artifacts (mapping, genome context, or sample specific); 3) performance of joint analysis of tumor and normal samples in the case of a tumor-normal workflow yielding improved accuracy; 4) benefits from FPGA acceleration for efficient run time. We demonstrate the speed and accuracy of the DRAGEN tumor-normal pipeline across a range of whole genome sequencing (WGS) datasets and compare against third party tools such as Mutect2/GATK4 [1] and Strelka2 [2]. DRAGEN secondary analysis outperforms all other tools with its ability to complete a 110x/40x T/N whole-genome analysis in less than two hours. It offers exceptional accuracy, with higher sensitivity and precision than third party tools. We also show that the DRAGEN T/N workflow supports analysis of liquid and late-stage solid tumors by tolerating tumor-in-normal (TiN) contamination.

bioinformatics↗

bioRxiv2022

Simultaneous isolation of proximal and distal lung progenitor cells from individual mice using a 3D printed guide reduces proximal cell contamination of distal lung epithelial cell isolations

The respiratory epithelium consists of multiple, functionally distinct cell-types and is maintained by regionally-specific progenitor populations which repair the epithelium following injury. Several in vitro methods exist for studying lung epithelial repair using primary murine lung epithelial cells, but isolation methods are hampered by a lack of surface markers distinguishing epithelial progenitors along the respiratory epithelium. Here, we developed a 3D-printed lobe divider (3DLD) to aid in simultaneous isolation of proximal versus distal lung epithelial progenitors from individual mice which give rise to differentiated epithelia in multiple in vitro assays. In contrast to 3DLD-isolated distal progenitor cells, classic manual tracheal ligation methods followed by lobe removal resulted in co-isolation of rare proximal progenitors with distal cells which altered the transcriptional landscape of distal organoid cultures. Thus, cell isolation with the 3DLD generates reproducible distal versus proximal progenitor populations and minimizes the potential for contaminating populations to confound in vitro assays. HighlightsO_LI3DLD reproducibly separates lung lobes and extrapulmonary airways (bronchi/trachea) C_LIO_LI3DLD cell isolation yields consistent isolation of distal epithelial cells (DECs) C_LIO_LIContamination of proximal cells in classic DEC isolations may alter in vitro results C_LIO_LI3DLD allows for simultaneous isolation of proximal and DECs from single animals C_LI eTOC blurbAlsafadi et al. describes a new method for simultaneous isolation of lung epithelial proximal and distal progenitors using the aid of a 3D printed device (3DLD). Both isolated cell types differentiate in multiple in vitro assays. The 3DLD guide minimized contamination of proximal cells in distal cell isolations whose presence can alter the transcriptional landscape of distal epithelial organoids.

cell biology↗

bioRxiv2020

Identification of a novel missense variant in SPDL1 associated with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a fatal disorder characterised by progressive, destructive lung scarring. Despite significant progress, the genetic determinants of this disease remain incompletely defined. Using next generation sequencing data from 752 individuals with sporadic IPF and 119,055 controls, we performed both variant- and gene-level analyses to identify novel IPF genetic risk factors. Our variant-level analysis revealed a novel rare missense variant in SPDL1 (NM_017785.5 p.Arg20Gln; p = 2.4 x 10-7, odds ratio = 2.87). This signal was independently replicated in the FinnGen cohort (combined p = 2.2 x 10-20), firmly associating this variant as a novel IPF risk allele. SPDL1 encodes Spindly, a protein involved in mitotic checkpoint signalling during cell division that has not been previously described in fibrosis. Our results highlight a novel mechanism underlying IPF, providing the potential for new therapeutic discoveries in a disease of great unmet need.

genetics↗

bioRxiv2020

Probabilistic programming: a powerful new approach to statistical phylogenetics

Statistical phylogenetic analysis currently relies on complex, dedicated software packages, making it difficult for evolutionary biologists to explore new models and inference strategies. Recent years have seen more generic solutions based on probabilistic graphical models, but this formalism can only partly express phylogenetic problems. Here we show that universal probabilistic programming languages (PPLs) solve the expressivity problem, while still supporting automated generation of efficient inference algorithms. To prove the latter point, we develop automated generation of sequential Monte Carlo (SMC) algorithms for PPL descriptions of arbitrary biological diversification (birth-death) models. SMC is a new inference strategy for these problems, supporting both parameter inference and efficient estimation of Bayes factors that are used in model testing. We take advantage of this in automatically generating SMC algorithms for several recent diversification models that have been difficult or impossible to tackle previously. Finally, applying these algorithms to 40 bird phylogenies, we show that models with slowing diversification, constant turnover and many small shifts generally explain the data best. Our work opens up several related problem domains to PPL approaches, and shows that few hurdles remain before these techniques can be effectively applied to the full range of phylogenetic models.

evolutionary biology↗

bioRxiv2018

Plasmodium falciparum mature schizont transcriptome variation among clinical isolates and laboratory-adapted clones

Malaria parasite genes exhibit variation in both sequence and expression level. There is much information on sequence polymorphism, but less resolution on natural variation in transcriptomes of parasites at specific developmental stages. This is largely because it is challenging to obtain highly replicated sampling of transcriptomes to overcome potentially confounding technical and biological variation. We address the issue in the major human parasite Plasmodium falciparum by obtaining RNA-seq profiles of multiple independent replicate preparations of mature schizont-stage parasites from a panel of clinical isolates recently established in culture and from long-term laboratory-adapted clones. With a goal of robustly identifying variably expressed genes, we show that increasing the numbers of biological sample replicates greatly improves the discovery rate. Generally, six independent replicates of each parasite culture is recommendable as being significantly to lower numbers, although for highly expressed genes variable expression can be detected when fewer replicates are available. A broad comparison identifies genes differing in relative expression between cultured clinical isolates and laboratory-adapted clones. Genes more highly expressed in the laboratory-adapted clones include an AP2 transcription factor gene Pf3D7_0420300 and putative methyl transferase genes. The variable expression of several known merozoite invasion ligands is confirmed, and previously uncharacterised genes are shown to be differentially expressed among clinical isolates. New RT-qPCR assays validate the variation in transcript levels of these genes, and allow quantitation of expression to be extended to a wider panel of clinical isolate samples. These variably expressed genes are new candidates for investigation as potential determinants of alternative parasite developmental pathways or targets of immunity.\n\nAuthor summaryUnderstanding parasite diversity and adaptation may require characterisation of gene expression variation, and is vital if chemotherapeutic or vaccine development is to consider new candidate targets, but it is technically challenging to generate precise data on clinical isolates. Here, we analyse the transcriptomes of mature Plasmodium falciparum schizonts using RNA-sequencing, using large numbers of biological replicate samples to minimise the impact of inter-replicate variation on observed patterns of differential expression. This identifies genes that are differentially expressed in long term laboratory-adapted parasites and recently cultured clinical isolates, as well as among different clinical isolates. In additional samples of schizonts grown in the first cycle ex vivo prior to any erythrocyte invasion, expression levels of a selected panel of these genes vary among isolates, but mean levels are similar to those in the continuously cultured clinical isolates, indicating that the latter are useful for experimental studies requiring biological replication.

cell biology↗