Abstract Parkinson’s disease (PD) is the fastest-growing neurologic disease and a leading cause of disability worldwide. PD affects the body and mind, is progressive, and there is no prevention or cure. Gut microbiome, a recently recognized contributing factor in PD, offers new leads for understanding the underlying pathobiology and devising new treatments. Here, we present the most comprehensive study of the PD gut microbiome to date, comprising three large datasets with a sample size of 1,006 PD and 544 neurologically healthy controls, generated with uniform methodology from subject recruitment to data analysis, and characterized using deep shotgun metagenome sequencing, genome-wide genotypes, and metadata. We begin by describing the gut dysbiosis at the species, gene, pathway, and functional level. Next, we find that PD-associated genetic variants at the SNCA gene region are associated with increased abundance of opportunistic pathogens and depletion of fiber degraders in the PD gut. We show that the presence of opportunistic pathogens at high levels in the gut increases the penetrance of SNCA variants for PD risk, raising the GWAS-derived odds ratio from less than 1.5 to over 8. The genetic variants identified here control splicing of the SNCA transcripts into alpha-synuclein isoforms with varying affinity for pathological aggregation. These data suggest pathogens are triggers for disease in the setting of genetic susceptibility, and the link to the genome implicates the microbes in the causation of PD. Finally, shifting focus to translation, we show that not all PD patients have the same dysbiotic features, and propose a conceptual framework to identify microbiome-based biomarkers to select appropriate patients for targeted microbiome-based clinical trials and personalized treatment.

Public omics repositories contain vast amounts of valuable data, but their metadata suffers from extreme heterogeneity, unstandardized terminologies, and quality issues that severely limit data reusability and cross-study integration. While prospective metadata standards exist, the majority of published omics data remain in non-standardized formats requiring retrospective curation. We performed comprehensive manual curation and harmonization of clinical metadata from 212,027 samples across 468 studies in two major repositories: curatedMetagenomicData (93 studies, 22,588 samples) and cBioPortal (375 studies, 189,438 samples). Through systematic ontology mapping, we consolidated redundant, dispersed information into much fewer harmonized columns, reduced unique values, and increased the completeness of major attributes. This curation process revealed common metadata quality issues, including typos, inconsistent terminologies, misplaced values, conflicting annotations, and inappropriately merged information across attributes. We document the challenges, decisions, and solutions encountered during large-scale metadata harmonization across two distinct omics domains. The harmonized metadata, accessible through the OmicsMLRepoR Bioconductor package, enables repository-wide queries and cross-study analyses previously challenging with heterogeneous metadata. Our experience provides practical guidance for similar curation efforts and demonstrates the value of investing in retrospective metadata improvement for existing public omics resources.

We propose a new random change point model that utilizes routinely recorded individual-level HIV viral load data to estimate the timing of antiretroviral therapy (ART) initiation in people living with HIV. The change point distribution is assumed to follow a zero-inflated exponential distribution for the longitudinal data, which is also subject to left-censoring, and the underlying data-generating mechanism is a nonlinear mixed-effects model. We extend the Stochastic EM (StEM) algorithm by combining a Gibbs sampler with a Metropolis–Hastings sampling. We apply the method to real HIV data to infer the timing of ART initiation since diagnosis. Additionally, we conduct simulation studies to assess the performance of our proposed method.

The expansion of microbiome research has led to the accumulation of interlinked datasets encompassing versatile taxonomic and functional assays. The analysis of increasingly large and heterogeneous multi-modal microbiome data would benefit from unified approaches supporting the design of modular data science workflows through interoperable methods. The Bioconductor project has recently developed an optimized statistical programming framework for multi-assay data integration. Building on this foundation, we introduce a community-developed open source ecosystem for microbiome data science. In contrast to the previous alternatives, the methodology is specifically designed to support joint analysis of hierarchical, interlinked, and heterogeneous multi-table datasets that are increasingly common in modern microbiome research. This data science ecosystem encompasses open data, methods, tutorials, and an active online community. These resources support standardized and reproducible data wrangling, joint analysis, and reporting. We have detailed the functionality and usage in the online book , which offers guidance for prospective users and contributors. ### Competing Interest Statement The authors have declared no competing interest. European Union, https://ror.org/019w4f821, 952914 University of Turku, https://ror.org/05vghhr25

Human DNA is unavoidably present in metagenomic analyses of human microbiomes. While current protocols remove human DNA before submission to public repositories, mitochondrial DNA (mtDNA) has been overlooked and frequently persists. We discuss the privacy risks and research opportunities associated with mtDNA, urging consideration by the scientific, ethics, and legal communities.

Large public datasets of the human microbiome now exist but combining them for large-scale analysis is difficult due to a lack of standardization. We developed curatedMetagenomicData (cMD) 3, a uniformly processed collection of over 22,000 human microbiome samples with manually curated metadata from 94 studies and 42 countries. This large and diverse resource allows for meta-analysis of the links between microbes and human health. Through meta-analysis, we identified hundreds of microbial species and thousands of microbial functions significantly associated with a person's sex, age, body mass index, and disease status, and catalog these as references. We developed an "oral enrichment score" (OES) based on the relative abundance of bacteria typically found in the oral cavity and not in the gut. Higher OES in the gut is a consistent feature in individuals with disease, suggesting that the relative abundance of oral bacteria in the gut is a simple and quantifiable signal of altered microbiome health. These analyses identify modest but widely shared patterns in human microbiomes, serving as a reproducible and readily updatable reference.

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

The Bioconductor project enters its third decade with over two thousand packages for genomic data science, over 100,000 annotation and experiment resources, and a global system for convenient distribution to researchers. Over 60,000 PubMed Central citations and terabytes of content shipped per month attest to the impact of the project on cancer genomic data science. This report provides an overview of cancer genomics resources in Bioconductor. After an overview of Bioconductor project principles, we address exploration of institutionally curated cancer genomics data such as TCGA. We then review genomic annotation and ontology resources relevant to cancer and then briefly survey analytical workflows addressing specific topics in cancer genomics. Concluding sections cover how new software and data resources are brought into the ecosystem and how the project is tackling needs for training of the research workforce. Bioconductor's strategies for supporting methods developers and researchers in cancer genomics are evolving along with experimental and computational technologies. All the tools described in this report are backed by regularly maintained learning resources that can be used locally or in cloud computing environments.

The increasing size of single-cell RNA sequencing (scRNA-seq) datasets poses major computational challenges. This work benchmarks the scalability, efficiency, and accuracy of five widely used analysis frameworks (Seurat, OSCA, scrap-per, Scanpy, and rapids singlecell), focusing on the impact of algorithmic and infrastructural choices on performance. We performed a systematic comparison of these workflows using representative datasets, including a 1.3 million mouse brain cell dataset for scalability and three smaller datasets (BE1, scMixology, and cord blood CITE-seq) with ground truth labels to assess clustering accuracy. Principal Component Analysis (PCA) was used as a paradigmatic step to evaluate the computational performance of six SVD algorithms (exact, ARPACK, IRLBA, randomized, Jacobi, and incremental PCA) across multiple data representations (dense, sparse, HDF5) and hardware configurations (CPU vs GPU). All methods showed high concordance in PCA results, with negligible loss of accuracy in truncated approaches. GPU-based computation using rapids singlecell provided a 15× speed-up over the best CPU methods, with moderate memory usage. On CPU, ARPACK and IRLBA were the most efficient for sparse matrices, while randomized SVD performed best for HDF5-backed data. Among full pipelines, rapids singlecell was the fastest, whereas OSCA and scrapper achieved the highest clustering accuracy (ARI up to 0.97) in datasets with known cell identities. Performance differences were largely driven by the choice of highly variable genes (HVGs) and PCA implementation. The study highlights that scalability in scRNA-seq analysis depends critically on both algorithmic and infrastructural factors. GPU acceleration and optimized BLAS/LAPACK configurations markedly enhance performance, while Bioconductor-based pipelines remain robust in accuracy. The provided benchmarks offer practical guidelines for efficient and reliable analysis of large-scale single-cell datasets.

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

MOTIVATION:Software is vital for the advancement of biology and medicine. Impact evaluations of scientific software have primarily emphasized traditional citation metrics of associated papers, despite these metrics inadequately capturing the dynamic picture of impact and despite challenges with improper citation. RESULTS:To understand how software developers evaluate their tools, we conducted a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We found that although developers realize the value of more extensive metric collection, they find a lack of funding and time hindering. We also investigated software among this community for how often infrastructure that supports more nontraditional metrics were implemented and how this impacted rates of papers describing usage of the software. We found that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seemed to be associated with increased mention rates. Analysing more diverse metrics can enable developers to better understand user engagement, justify continued funding, identify novel use cases, pinpoint improvement areas, and ultimately amplify their software's impact. Challenges are associated, including distorted or misleading metrics, as well as ethical and security concerns. More attention to nuances involved in capturing impact across the spectrum of biomedical software is needed. For funders and developers, we outline guidance based on experience from our community. By considering how we evaluate software, we can empower developers to create tools that more effectively accelerate biological and medical research progress. AVAILABILITY AND IMPLEMENTATION:More information about the analysis, as well as access to data and code is available at https://github.com/fhdsl/ITCR_Metrics_manuscript_website.

SUMMARY:LEfSe is a widely used Python package and Galaxy module for metagenomic biomarker discovery and visualization, utilizing the Kruskal-Wallis test, Wilcoxon Rank-Sum test, and Linear Discriminant Analysis. R/Bioconductor provides a large collection of tools for metagenomic data analysis but has lacked an implementation of this widely used algorithm, hindering benchmarking against other tools and incorporation into R workflows. We present the lefser package to provide comparable functionality within the R/Bioconductor ecosystem of statistical analysis tools, with improvements to the original algorithm for performance, accuracy, and reproducibility. We benchmark the performance of lefser against the original algorithm using human and mouse metagenomic datasets. AVAILABILITY AND IMPLEMENTATION:Our software, lefser, is distributed through the Bioconductor project (https://www.bioconductor.org/packages/release/bioc/html/lefser.html), and all the source code is available in the GitHub repository https://github.com/waldronlab/lefser.

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities. In this Review, Segata, Waldron and colleagues discuss important key concepts of machine learning that are relevant to microbiologists and provide them with a set of tools essential to apply machine learning in microbiology research.

Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.

The link between the oral microbiome and neurodevelopmental disorders remains a compelling hypothesis, still requiring confirmation in large-scale datasets. Leveraging over 7000 whole-genome sequenced salivary samples from 2025 US families with children diagnosed with autism spectrum disorders (ASD), our cross-sectional study shows that the oral microbiome composition can discriminate ASD subjects from neurotypical siblings (NTs, AUC = 0.66), with 108 differentiating species (q < 0.005). The relative abundance of these species is highly correlated with cognitive impairment as measured by Full-Scale Intelligence Quotient (IQ). ASD children with IQ < 70 also exhibit lower microbiome strain sharing with parents (p < 10-6) with respect to NTs. A two-pronged functional enrichment analysis suggests the contribution of enzymes from the serotonin, GABA, and dopamine degradation pathways to the distinct microbial community compositions observed between ASD and NT samples. Although measures of restrictive eating diet and proxies of oral hygiene show relatively minor effects on the microbiome composition, the observed associations with ASD and IQ may still represent unaccounted-for underlying differences in lifestyle among groups. While causal relationships could not be established, our study provides substantial support to the investigation of oral microbiome biomarkers in ASD.

Short demo of basic MultiAssayExperiment functionality.

Multiple clinical trials targeting the gut microbiome are being conducted to optimize treatment outcomes for immune checkpoint blockade (ICB). To improve the success of these interventions, understanding gut microbiome changes during ICB is urgently needed. Here through longitudinal microbiome profiling of 175 patients treated with ICB for advanced melanoma, we show that several microbial species-level genome bins (SGBs) and pathways exhibit distinct patterns from baseline in patients achieving progression-free survival (PFS) of 12 months or longer (PFS ≥12) versus patients with PFS shorter than 12 months (PFS <12). Out of 99 SGBs that could discriminate between these two groups, 20 were differentially abundant only at baseline, while 42 were differentially abundant only after treatment initiation. We identify five and four SGBs that had consistently higher abundances in patients with PFS ≥12 and <12 months, respectively. Constructing a log ratio of these SGBs, we find an association with overall survival. Finally, we find different microbial dynamics in different clinical contexts including the type of ICB regimen, development of immune-related adverse events and concomitant medication use. Insights into the longitudinal dynamics of the gut microbiome in association with host factors and treatment regimens will be critical for guiding rational microbiome-targeted therapies aimed at enhancing ICB efficacy.

Supplementary Table 2. List of pathways identified for the statistically significant up-regulated probesets in cancer as compared to normal fallopian tube.

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that although developers recognize the utility of analyzing data related to the impact or usage of their software, they struggle to find the time or funding to support such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of the evaluations of their software so that they can more fully benefit from such assessments.

Supplementary Methods from MicroRNA-301 Mediates Proliferation and Invasion in Human Breast Cancer