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publications
MungeSumstats: a Bioconductor package for the standardization and quality control of many GWAS summary statistics
Published in Bioinformatics, 2021
we have developed MungeSumstats, a Bioconductor R package for the standardization and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardized, tabular summary statistic file, VCF or R native data object.
Recommended citation: Murphy, A. E., Schilder, B. M. & Skene, N. G. Bioinformatics 37, 4593–4596 (2021)
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A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis
Published in Nature Communications, 2022
Based on our findings, we recommend the use of pseudobulk approaches for differential expression in single-cell RNA-sequencing analyses.
Recommended citation: Murphy, A. E. & Skene, N. G. Nat. Commun. 37, 7851 (2022)
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Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimers disease dataset
Published in eLife, 2023
Reanalysis reveals the impact of quality control and differential analysis methods on the discovery of disease-associated genes on the first Alzheimers disease single nucleus RNA-seq dataset.
Recommended citation: Murphy, A. E., Fancy, N. & Skene, N. G. eLife 12:RP90214 (2023)
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Predicting cell type-specific epigenomic profiles accounting for distal genetic effects
Published in Nature Communications, 2024
Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits.
Recommended citation: Murphy, A.E., Beardall, W., Rei, M. et al. Nat. Commun. 15, 9951 (2024)
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Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states
Published in Nucleic Acids Research (NAR), 2024
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.
Recommended citation: Murphy, A.E., Askarova,A. , Lenhard, B. et al. Nucleic Acids Research, gkae1212, (2024).
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
TA role - MSc Biomedical Data Science - Data Science
Masters course, Faculty of Medicine, 2023
A data science course covering dimensionality reduction, unsupervised and supervised machine learning techniques applied to biological datasets.
TA role - BSc Medical Biosciences - Statistics for Biologists
Undergraduate course, Faculty of Medicine, 2023
A statistics workshop covering the principles of statistical analyses of biological data and experimental design.