Biology subjects

Murphy, A. E.

Publications and source records attributed to Murphy, A. E..

11 recordsLinked to original sources

bioRxiv2026

Decades of Parkinson's disease neuropathology yield a sparse and underpowered map of neuronal vulnerability: a systematic review and meta-analysis

Selective vulnerability is widely assumed in Parkinson's disease (PD), but whether dopaminergic neurons of the substantia nigra are uniquely vulnerable has not been established. Explanations of neuronal degeneration in Parkinson's disease often centre on the distinctive properties of substantia nigra dopaminergic neurons. Whether these properties are necessary for substantial neuronal loss remains unclear. We preregistered a systematic review of 166 post-mortem case-control studies published between 1963 and 2025 and synthesised neuronal counts and densities from 152 studies using a multilevel meta-analysis. After six decades, only 18% of countable brain atlas labels had been examined, and most populations were represented by a single study. Substantial loss beyond dopaminergic and classically pigmented populations shows that neither dopaminergic identity nor neuromelanin is necessary for marked degeneration, challenging key tenets of selective vulnerability in PD. Comparisons across other anatomical and physiological features remain limited by sparse sampling and uncertainty. We identify less-studied populations with substantial estimated loss and estimate the additional sampling needed to improve precision under specified assumptions. These findings direct replication and comparative counting towards uncertainties that limit explanations of neuronal loss across affected and potentially spared populations.

neuroscience↗

bioRxiv2026

orthogene: a Bioconductor package to easily map genes within and across hundreds of species

MotivationMapping genes across identifier systems and species is a routine but critical step in bioinformatics workflows. Despite its ubiquity, gene mapping is frequently handled using bespoke, ad hoc solutions, increasing duplicated effort and introducing opportunities for error. These issues are exacerbated by the prevalence of non-one-to-one homolog relationships and inconsistent handling of gene identifiers across species and databases, which can compromise downstream analyses and reproducibility. ResultsWe present orthogene, an R/Bioconductor package that simplifies gene mapping within and across hundreds of species. orthogene provides a unified, workflow-oriented framework that integrates automated species and identifier standardization, homolog inference across multiple databases, flexible handling of ambiguous homolog relationships, and transformation of gene lists, tables, and high-dimensional matrices into analysis-ready formats. By abstracting common sources of technical complexity while retaining user control, orthogene enables transparent, reproducible, and scalable gene mapping across a wide range of biological contexts. Availabilityhttps://bioconductor.org/packages/orthogene Contactbrian_schilder@alumni.brown.edu

bioinformatics↗

bioRxiv2024

Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states

To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors like cell state, histone mark function or distal effects, that impact the relationship, limiting their findings. Moreover, downstream use of these models for new biological insight is lacking. Here, we present the most comprehensive study of this relationship to date - investigating seven histone marks, in eleven cell types, across a diverse range of cell states. We used convolutional and attention-based models to predict transcription from histone mark activity at promoters and distal regulatory elements. Our work shows that histone mark function, genomic distance and cellular states collectively influence a histone marks relationship with transcription. We found that no individual histone mark is consistently the strongest predictor of gene expression across all genomic and cellular contexts. This highlights the need to consider all three factors when determining the effect of histone mark activity on transcriptional state. Furthermore, we conducted in silico histone mark perturbation assays, uncovering functional and disease related loci and highlighting frameworks for the use of chromatin deep learning models to uncover new biological insight. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/587323v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@18dfcbcorg.highwire.dtl.DTLVardef@cba6b8org.highwire.dtl.DTLVardef@2eb0ceorg.highwire.dtl.DTLVardef@159d5b7_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects

Understanding genetic variants effects on the epigenome is crucial for interpreting genome-wide association studies (GWAS) results, yet profiling these effects across the non-coding genome remains challenging due to the scalability limits of experimental methods. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we propose the development of a deep learning model, Enformer Celltyping, which can both incorporate distal effects of DNA interactions, up to 100,000 base-pairs away, and predict epigenetic signals in a cell type-agnostic fashion. We demonstrate that Enformer Celltypings predictions of the epigenome out-perform the current best-in-class approach, have strong performance in a range of different cell types and biological regions and generalise to cell types assayed independently of the original training set. Finally, we propose an approach to test epigenetic models performance on genetic variant effect predictions using regulatory quantitative trait loci mapping studies, highlighting Enformer Celltypings and other genomic deep learning models limitations for this task. Our work introduces a new, accurate model for cell type-agnostic histone mark predictions and highlights the benefit of transfer learning for more efficient model development for deep learning in genomics. We make our customisable and efficient transfer learning approach for Enformer available to the community.

genomics↗

bioRxiv2023

Avoiding false discoveries: Revisiting an Alzheimer's disease snRNA-Seq dataset

Mathys et al., conducted the first single-nucleus RNA-Seq study (snRNA-Seq) of Alzheimers disease (AD)1. The authors profiled the transcriptomes of approximately 80,000 cells from the prefrontal cortex, collected from 48 individuals - 24 of which presented with varying degrees of AD pathology. With bulk RNA-Seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time2. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-Seq differential expression, resulting 549 times fewer differentially expressed genes at a false discovery rate (FDR) of 0.05.

neuroscience↗

bioRxiv2022

Transcriptomic analyses reveal neuronal specificity of Leigh syndrome associated genes

Leigh syndrome (subacute necrotising encephalomyelopathy) is a rare inherited, complex, and typically early onset mitochondrial disorder with clinical and genetic heterogeneity. It owes its heterogeneous nature to the complex nature of mitochondrial genetics and the significant interactions that occur between the mitochondrial and nuclear genomes. Stepwise developmental regression is a classical feature of patients with Leigh syndrome, but other neurological features include ataxia, tremor, seizures, and occasionally psychiatric manifestations. To date the involvement of more than 100 genes has been identified in patients with Leigh syndrome. Despite this, the pathophysiology of Leigh syndrome remains unknown, although it is thought to be due to primary mitochondrial dysfunction. Here, we sought to determine the cellular specificity of all Leigh syndrome associated genes within the brain and investigate a potential common genetic mechanism between Leigh syndrome and other disorders with overlapping clinical features. We utilized co-expression network analyses constructed using existing UK Brain Expression Consortium (UKBEC) data from 10 brain regions to identify regions of the brain where our genes of interest were enriched. Next, expression weighted cell type enrichment (EWCE) was employed to determine the cellular specificity of Leigh syndrome associated genes. Association with pre-or post-synaptic structures was identified using synaptic gene ontology (SynGO) analysis. Finally, heritability analysis was performed on the co-expression network modules demonstrating enrichment of Leigh syndrome associated genes to investigate potential genetic relationships with Parkinson disease, epilepsy, and schizophrenia. Co-expression network analyses reveal that genes encoding oxidative phosphorylation subunits and assembly factors exhibit the highest levels of expression in brain regions most affected on MRI and neuropathology in Leigh syndrome. Further, over two thirds of the genes were found to be enriched within the substantia nigra and 19 genes appeared to cluster, with selective enrichment in the putamen, substantia nigra, medulla and thalamus. EWCE analyses of single cell RNA-Seq data from mouse brain revealed significant enrichment of Leigh syndrome associated genes in hippocampal and somatosensory pyramidal neurones and interneurons of the brain. SynGO analysis demonstrated that expression of these genes is preferentially associated with pre-synaptic structures. Heritability studies suggested that some modules in which Leigh syndrome associated genes were enriched were also enriched for Parkinson disease and epilepsy. In conclusion, our findings suggest a primary mitochondrial dysfunction as the underlying basis of Leigh syndrome with causative genes primarily expressed in neuronal cells, explaining the relative sparing of glial cells observed in neuropathological studies.

genetics↗

bioRxiv2022

EpiCompare: R package for the comparison and quality control of epigenomic peak files

SummaryEpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner. Availability and ImplementationEpiCompare is available on Bioconductor ([≥] v3.15): https://bioconductor.org/packages/release/bioc/html/EpiCompare.html All source code is publically available via GitHub: https://github.com/neurogenomics/EpiCompare Documentation website https://neurogenomics.github.io/EpiCompare EpiCompare DockerHub repository: https://hub.docker.com/repository/docker/neurogenomicslab/epicompare

bioinformatics↗

bioRxiv2022

A balanced measure shows superior performance of pseudobulk methods over mixed models and pseudoreplication approaches in single-cell RNA-sequencing analysis

Recently, Zimmerman et al.,1 highlighted the importance of accounting for the dependence between cells from the same individual when conducting differential expression analysis on single-cell RNA-sequencing data. Their work proved the inadequacy of pseudoreplication approaches for such analysis - This was an important step forward that was conclusively proven by them. A hierarchical single-cell expression simulation approach (hierarchicell) was developed by Zimmerman et al.,1 to generate non-differentially expressed genes upon which performance was evaluated using the type 1 error rate; the proportion of non-differentially expressed genes indicated as differentially expressed by a model. However, evaluating such models on their type 1 or type 2 error rate in isolation is insufficient to determine their true performance - for example, a method with low type 1 error may have a high type 2 error rate. Moreover, because no seed was set for the pseudo-random number generator used in hierarchicell, the different methods evaluated by Zimmerman et al. were done so on different simulated datasets. Here, we corrected these issues, reran the authors analysis and found pseudobulk methods outperformed mixed models. ContactAlan Murphy: a.murphy@imperial.ac.uk, Nathan Skene: n.skene@imperial.ac.uk Code availabilityThe modified version of hierarchicell which returns all error metrics, uses the same simulated data across approaches and has checkpointing capabilities (if runs are aborted or crashed) is available at: https://github.com/neurogenomics/hierarchicell. The benchmarking script along with the results are available at: https://github.com/Al-Murphy/reanalysis_scRNA_seq_benchmark.

bioinformatics↗

bioRxiv2021

scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

bioinformatics↗

bioRxiv2021

MungeSumstats: A Bioconductor package for the standardisation and quality control of many GWAS summary statistics

SummaryGenome-wide association studies (GWAS) summary statistics have popularised and accelerated genetic research. However, a lack of standardisation of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardisation and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardised, tabular summary statistic file, VCF or R native data object. ContactAlan Murphy: a.murphy@imperial.ac.uk, Nathan Skene: n.skene@imperial.ac.uk Availability and implementationMungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats Supplementary informationThe analysis deriving the most common summary statistic formats is available at: https://al-murphy.github.io/SumstatFormats

genomics↗

bioRxiv2021

A Scenic River in Rural Ohio Shows Elevated Antibiotic Resistance Genes, Including Acinetobacter Tetracycline and Macrolide Resistance, Downstream of Wastewater Treatment Plant Effluent

The entry of antibiotic resistance genes (ARGs) into aquatic systems has been documented for large municipal wastewater treatment plants, but there is less study of the impact of smaller plants that are situated on small rural rivers. We sampled water metagenomes for ARG and taxa composition from the Kokosing River, a small rural river in Knox County, Ohio, which has been designated an Ohio State Scenic River for retention of natural character. Samples were obtained 1.0 km upstream, 120 m downstream, and 6.4 km downstream from the effluent release of the Mount Vernon wastewater treatment plant (WWTP). ARGS were identified in metagenomes using ShortBRED markers from the CARD database screened against UniPROT. Through all seasons, the metagenome just downstream of the WWTP effluent showed a substantial elevation of at least 15 different ARGs, including 6 ARGs commonly associated with Acinetobacter baumannii such as msrE, mphE (macrolide resistance) and tet(39) (tetracycline resistance). The ARGs most prevalent near the effluent pipe persisted 6.4 km downriver. Using MetaPhlAn2 clade-specific marker genes, the taxa distribution near the effluent showed elevation of reads annotated as Acinetobacter species as well as gut-associated taxa, Bacteroides and Firmicutes. The ARG levels and taxa prevalence showed little dependence on seasonal chlorination of the effluent. Nitrogen and phosphorus were elevated near the effluent pipe but had no consistent correlation with ARG levels. We show that in a rural river microbiome, year-round wastewater effluent substantially elevates ARGs including those associated with multidrug-resistant A. baumanii. IMPORTANCEAntibiotic resistance is a growing problem worldwide, with frequent transmission between pathogens and environmental organisms. Rural rivers can support high levels of recreational use by people unaware of inputs from treated wastewater, while WWTPs can generate a small but significant portion of flow volume into a river surrounded by forest and agriculture. There is little information on the rural impacts of WWTP effluent on the delivery and transport of antibiotic resistance genes. In our study, the river water proximal to wastewater effluent shows evidence for the influx of multidrug-resistant Acinetobacter baumanii, an opportunistic pathogen of concern for hospitals but also widespread in natural environments. Our work highlights the importance of wastewater effluent in management of environmental antibiotic resistance, even in high quality, rural river systems.

microbiology↗