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Article ; Online: rworkflows: automating reproducible practices for the R community.

Schilder, Brian M / Murphy, Alan E / Skene, Nathan G

2024 Volume 15, Issue 1, Page(s) 149

Abstract: Despite calls to improve reproducibility in research, achieving this goal remains elusive even within computational fields. Currently, >50% of R packages are distributed exclusively through GitHub. While the trend towards sharing open-source software has ...

Abstract	Despite calls to improve reproducibility in research, achieving this goal remains elusive even within computational fields. Currently, >50% of R packages are distributed exclusively through GitHub. While the trend towards sharing open-source software has been revolutionary, GitHub does not have any default built-in checks for minimal coding standards or software usability. This makes it difficult to assess the current quality R packages, or to consistently use them over time and across platforms. While GitHub-native solutions are technically possible, they require considerable time and expertise for each developer to write, implement, and maintain. To address this, we develop rworkflows; a suite of tools to make robust continuous integration and deployment ( https://github.com/neurogenomics/rworkflows ). rworkflows can be implemented by developers of all skill levels using a one-time R function call which has both sensible defaults and extensive options for customisation. Once implemented, any updates to the GitHub repository automatically trigger parallel workflows that install all software dependencies, run code checks, generate a dedicated documentation website, and deploy a publicly accessible containerised environment. By making the rworkflows suite free, automated, and simple to use, we aim to promote widespread adoption of reproducible practices across a continually growing R community.
Language	English
Publishing date	2024-01-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-44484-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis.

Murphy, Alan E / Skene, Nathan G

Nature communications

2022 Volume 13, Issue 1, Page(s) 7851

MeSH term(s)	Software ; High-Throughput Nucleotide Sequencing/methods ; RNA/genetics ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2022-12-22
Publishing country	England
Document type	Letter ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-35519-4
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Article ; Online: rworkflows

Brian M. Schilder / Alan E. Murphy / Nathan G. Skene

Nature Communications, Vol 15, Iss 1, Pp 1-

automating reproducible practices for the R community

2024 Volume 10

Abstract: Abstract Despite calls to improve reproducibility in research, achieving this goal remains elusive even within computational fields. Currently, >50% of R packages are distributed exclusively through GitHub. While the trend towards sharing open-source ... ...

Abstract	Abstract Despite calls to improve reproducibility in research, achieving this goal remains elusive even within computational fields. Currently, >50% of R packages are distributed exclusively through GitHub. While the trend towards sharing open-source software has been revolutionary, GitHub does not have any default built-in checks for minimal coding standards or software usability. This makes it difficult to assess the current quality R packages, or to consistently use them over time and across platforms. While GitHub-native solutions are technically possible, they require considerable time and expertise for each developer to write, implement, and maintain. To address this, we develop rworkflows; a suite of tools to make robust continuous integration and deployment ( https://github.com/neurogenomics/rworkflows ). rworkflows can be implemented by developers of all skill levels using a one-time R function call which has both sensible defaults and extensive options for customisation. Once implemented, any updates to the GitHub repository automatically trigger parallel workflows that install all software dependencies, run code checks, generate a dedicated documentation website, and deploy a publicly accessible containerised environment. By making the rworkflows suite free, automated, and simple to use, we aim to promote widespread adoption of reproducible practices across a continually growing R community.
Keywords	Science ; Q
Subject code	005
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: EpiCompare: R package for the comparison and quality control of epigenomic peak files.

Choi, Sera / Schilder, Brian M / Abbasova, Leyla / Murphy, Alan E / Skene, Nathan G

Bioinformatics advances

2023 Volume 3, Issue 1, Page(s) vbad049

Abstract: Summary: EpiCompare 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 ...

Abstract	Summary: EpiCompare 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 implementation: EpiCompare is available on Bioconductor (≥ v3.15): https://bioconductor.org/packages/release/bioc/html/EpiCompare.html; all source code is publicly available via GitHub: https://github.com/neurogenomics/EpiCompare; documentation website https://neurogenomics.github.io/EpiCompare; and EpiCompare DockerHub repository: https://hub.docker.com/repository/docker/neurogenomicslab/epicompare.
Language	English
Publishing date	2023-04-13
Publishing country	England
Document type	Journal Article
ISSN	2635-0041
ISSN (online)	2635-0041
DOI	10.1093/bioadv/vbad049
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Article ; Online: MungeSumstats: a Bioconductor package for the standardization and quality control of many GWAS summary statistics.

Murphy, Alan E / Schilder, Brian M / Skene, Nathan G

Bioinformatics (Oxford, England)

2021 Volume 37, Issue 23, Page(s) 4593–4596

Abstract: Motivation: Genome-wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization of the file formats used has proven problematic when running secondary analysis tools or ... ...

Abstract	Motivation: Genome-wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. Results: To address this issue, 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. Availability and implementation: MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Genome-Wide Association Study ; Quality Control ; Reference Standards ; Software
Language	English
Publishing date	2021-10-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab665
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Article ; Online: A genomic lifespan program that reorganises the young adult brain is targeted in schizophrenia.

Skene, Nathan G / Roy, Marcia / Grant, Seth Gn

eLife

2017 Volume 6

Abstract: The genetic mechanisms regulating the brain and behaviour across the lifespan are poorly understood. We found that lifespan transcriptome trajectories describe a calendar of gene regulatory events in the brain of humans and mice. Transcriptome ... ...

Abstract	The genetic mechanisms regulating the brain and behaviour across the lifespan are poorly understood. We found that lifespan transcriptome trajectories describe a calendar of gene regulatory events in the brain of humans and mice. Transcriptome trajectories defined a sequence of gene expression changes in neuronal, glial and endothelial cell-types, which enabled prediction of age from tissue samples. A major lifespan landmark was the peak change in trajectories occurring in humans at 26 years and in mice at 5 months of age. This species-conserved peak was delayed in females and marked a reorganization of expression of synaptic and schizophrenia-susceptibility genes. The lifespan calendar predicted the characteristic age of onset in young adults and sex differences in schizophrenia. We propose a genomic program generates a lifespan calendar of gene regulation that times age-dependent molecular organization of the brain and mutations that interrupt the program in young adults cause schizophrenia.
MeSH term(s)	Adolescent ; Adult ; Aged ; Animals ; Brain/metabolism ; Child ; Child, Preschool ; Female ; Gene Expression Profiling ; Gene Expression Regulation/genetics ; Genomics ; Humans ; Infant ; Male ; Mice ; Mice, Inbred C57BL ; Middle Aged ; Mutation ; Nerve Tissue Proteins/metabolism ; Neuroglia ; Neurons/metabolism ; Prefrontal Cortex/metabolism ; Schizophrenia/metabolism ; Sex Characteristics ; Synapses/metabolism ; Transcriptome ; Young Adult
Chemical Substances	Nerve Tissue Proteins
Language	English
Publishing date	2017-09-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.17915
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Article: Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment.

Skene, Nathan G / Grant, Seth G N

Frontiers in neuroscience

2016 Volume 10, Page(s) 16

Abstract: The cell types that trigger the primary pathology in many brain diseases remain largely unknown. One route to understanding the primary pathological cell type for a particular disease is to identify the cells expressing susceptibility genes. Although ... ...

Abstract	The cell types that trigger the primary pathology in many brain diseases remain largely unknown. One route to understanding the primary pathological cell type for a particular disease is to identify the cells expressing susceptibility genes. Although this is straightforward for monogenic conditions where the causative mutation may alter expression of a cell type specific marker, methods are required for the common polygenic disorders. We developed the Expression Weighted Cell Type Enrichment (EWCE) method that uses single cell transcriptomes to generate the probability distribution associated with a gene list having an average level of expression within a cell type. Following validation, we applied EWCE to human genetic data from cases of epilepsy, Schizophrenia, Autism, Intellectual Disability, Alzheimer's disease, Multiple Sclerosis and anxiety disorders. Genetic susceptibility primarily affected microglia in Alzheimer's and Multiple Sclerosis; was shared between interneurons and pyramidal neurons in Autism and Schizophrenia; while intellectual disabilities and epilepsy were attributable to a range of cell-types, with the strongest enrichment in interneurons. We hypothesized that the primary cell type pathology could trigger secondary changes in other cell types and these could be detected by applying EWCE to transcriptome data from diseased tissue. In Autism, Schizophrenia and Alzheimer's disease we find evidence of pathological changes in all of the major brain cell types. These findings give novel insight into the cellular origins and progression in common brain disorders. The methods can be applied to any tissue and disorder and have applications in validating mouse models.
Language	English
Publishing date	2016-01-27
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2016.00016
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Article ; Online: A genomic lifespan program that reorganises the young adult brain is targeted in schizophrenia

Nathan G Skene / Marcia Roy / Seth GN Grant

eLife, Vol

2017 Volume 6

Abstract	The genetic mechanisms regulating the brain and behaviour across the lifespan are poorly understood. We found that lifespan transcriptome trajectories describe a calendar of gene regulatory events in the brain of humans and mice. Transcriptome trajectories defined a sequence of gene expression changes in neuronal, glial and endothelial cell-types, which enabled prediction of age from tissue samples. A major lifespan landmark was the peak change in trajectories occurring in humans at 26 years and in mice at 5 months of age. This species-conserved peak was delayed in females and marked a reorganization of expression of synaptic and schizophrenia-susceptibility genes. The lifespan calendar predicted the characteristic age of onset in young adults and sex differences in schizophrenia. We propose a genomic program generates a lifespan calendar of gene regulation that times age-dependent molecular organization of the brain and mutations that interrupt the program in young adults cause schizophrenia.
Keywords	transcriptomics ; Schizophrenia ; post-mortem ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2017-09-01T00:00:00Z
Publisher	eLife Sciences Publications Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multidimensional Dynamics of the Proteome in the Neurodegenerative and Aging Mammalian Brain.

Andrews, Byron / Murphy, Alan E / Stofella, Michele / Maslen, Sarah / Almeida-Souza, Leonardo / Skehel, J Mark / Skene, Nathan G / Sobott, Frank / Frank, René A W

Molecular & cellular proteomics : MCP

2021 Volume 21, Issue 2, Page(s) 100192

Abstract: The amount of any given protein in the brain is determined by the rates of its synthesis and destruction, which are regulated by different cellular mechanisms. Here, we combine metabolic labeling in live mice with global proteomic profiling to ... ...

Abstract	The amount of any given protein in the brain is determined by the rates of its synthesis and destruction, which are regulated by different cellular mechanisms. Here, we combine metabolic labeling in live mice with global proteomic profiling to simultaneously quantify both the flux and amount of proteins in mouse models of neurodegeneration. In multiple models, protein turnover increases were associated with increasing pathology. This method distinguishes changes in protein expression mediated by synthesis from those mediated by degradation. In the App
MeSH term(s)	Aging ; Alzheimer Disease/metabolism ; Animals ; Brain/metabolism ; Disease Models, Animal ; Mammals/metabolism ; Mice ; Mice, Transgenic ; Proteome/metabolism ; Proteomics/methods
Chemical Substances	Proteome
Language	English
Publishing date	2021-12-31
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2075924-1
ISSN	1535-9484 ; 1535-9476
ISSN (online)	1535-9484
ISSN	1535-9476
DOI	10.1016/j.mcpro.2021.100192
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Zs.A 5599: Show issues

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Article ; Online: Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson's disease.

Bryois, Julien / Skene, Nathan G / Hansen, Thomas Folkmann / Kogelman, Lisette J A / Watson, Hunna J / Liu, Zijing / Brueggeman, Leo / Breen, Gerome / Bulik, Cynthia M / Arenas, Ernest / Hjerling-Leffler, Jens / Sullivan, Patrick F

Nature genetics

2020 Volume 52, Issue 5, Page(s) 482–493

Abstract: Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell ... ...

Abstract	Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson's disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson's disease.
MeSH term(s)	Animals ; Brain/pathology ; Genome-Wide Association Study/methods ; Humans ; Mice ; Neurons/pathology ; Parkinson Disease/etiology ; Parkinson Disease/genetics ; Parkinson Disease/pathology ; Transcriptome/genetics
Language	English
Publishing date	2020-04-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1108734-1
ISSN	1546-1718 ; 1061-4036
ISSN (online)	1546-1718
ISSN	1061-4036
DOI	10.1038/s41588-020-0610-9
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