rworkflows: automating reproducible practices for the R community.

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.

Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset.

Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer's disease (AD) (Mathys et al., 2019). 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 time. 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 in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.

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

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.

Transcriptomic analyses reveal neuronal specificity of Leigh syndrome associated genes.

Leigh syndrome is a rare, inherited, complex neurometabolic disorder with genetic and clinical heterogeneity. Features present in affected patients range from classical stepwise developmental regression to ataxia, seizures, tremor, and occasionally psychiatric manifestations. Currently, more than 100 monogenic causes of Leigh syndrome have been identified, yet the pathophysiology remains unknown. Here, we sought to determine the cellular specificity within the brain of all genes currently associated with Leigh syndrome. Further, we aimed to investigate potential genetic commonalities between Leigh syndrome and other disorders with overlapping clinical features. Enrichment of our target genes within the brain was evaluated with co-expression (CoExp) network analyses constructed using existing UK Brain Expression Consortium data. To determine the cellular specificity of the Leigh associated genes, we employed expression weighted cell type enrichment (EWCE) analysis of single-cell RNA-Seq data. Finally, CoExp network modules demonstrating enrichment of Leigh syndrome associated genes were then utilised for synaptic gene ontology analysis and heritability analysis. CoExp network analyses revealed that Leigh syndrome associated genes exhibit the highest levels of expression in brain regions most affected on MRI in affected patients. EWCE revealed significant enrichment of target genes in hippocampal and somatosensory pyramidal neurons and interneurons of the brain. Analysis of CoExp modules enriched with our target genes revealed preferential association with pre-synaptic structures. Heritability studies suggested some common enrichment between Leigh syndrome and Parkinson disease and epilepsy. Our findings suggest a primary mitochondrial dysfunction as the underlying basis of Leigh syndrome, with associated genes primarily expressed in neuronal cells.

Multidimensional Dynamics of the Proteome in the Neurodegenerative and Aging Mammalian Brain.

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 AppNL-F knockin mouse model of Alzheimer's disease, increased turnover resulted from imbalances in both synthesis and degradation, converging on proteins associated with synaptic vesicle recycling (Dnm1, Cltc, Rims1) and mitochondria (Fis1, Ndufv1). In contrast to disease models, aging in wild-type mice caused a widespread decrease in protein recycling associated with a decrease in autophagic flux. Overall, this simple multidimensional approach enables a comprehensive mapping of proteome dynamics and identifies affected proteins in mouse models of disease and other live animal test settings.

MungeSumstats: a Bioconductor package for the standardization and quality control of many GWAS summary statistics.

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.