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At least 19 recordsLinked to original sources

PubMed2025

HTSinfer: inferring metadata from bulk Illumina RNA-Seq libraries.

SUMMARY: The Sequencing Read Archive is one of the largest and fastest-growing repositories of sequencing data, containing tens of petabytes of sequenced reads. Its data is used by a wide scientific community, often beyond the primary study that generated them. Such analyses rely on accurate metadata concerning the type of experiment and library, as well as the organism from which the sequenced reads were derived. These metadata are typically entered manually by contributors in an error-prone process, and are frequently incomplete. In addition, easy-to-use computational tools that verify the consistency and completeness of metadata describing the libraries to facilitate data reuse, are largely unavailable. Here, we introduce HTSinfer, a Python-based tool to infer metadata directly and solely from bulk RNA-sequencing data generated on Illumina platforms. HTSinfer leverages genome sequence information and diagnostic genes to rapidly and accurately infer the library source and library type, as well as the relative read orientation, 3' adapter sequence and read length statistics. HTSinfer is written in a modular manner, published under a permissible free and open-source license and encourages contributions by the community, enabling easy addition of new functionalities, e.g. for the inference of additional metrics, or the support of different experiment types or sequencing platforms. AVAILABILITY AND IMPLEMENTATION: HTSinfer is released under the Apache License 2.0. Latest code is available via GitHub at https://github.com/zavolanlab/htsinfer, while releases are published on Bioconda. A snapshot of the HTSinfer version described in this article was deposited at Zenodo at 10.5281/zenodo.13985958.

Metadata↗

PubMed2025

Pre-Meta: priors-augmented retrieval for LLM-based metadata generation.

MOTIVATION: While high-throughput sequencing technologies have dramatically accelerated genomic data generation, the manual processes required for dataset annotation and metadata creation impede the efficient discovery and publication of these resources across disparate public repositories. Large language models (LLMs) have the potential to streamline dataset profiling and discovery. However, their current limitations in generalizing across specialized knowledge domains, particularly in fields such as biomedical genomics, prevent them from fully realizing this potential. This article presents Pre-Meta, an LLM-agnostic and domain-independent data annotation pipeline with an enriched retrieval procedure that leverages related priors-such as pre-generated metadata tags and ontologies-as auxiliary information to improve the accuracy of automated metadata generation. RESULTS: Validated using five selected metadata fields sampled across 1500 papers, the Pre-Meta assisted annotation experiment-without finetuning and prompt optimization-demonstrates a systemic improvement in the annotation task: shown through a 23%, 72%, and 75% accuracy gain from conventional RAG adoptions of GPT-4o mini, Llama 8B, and Mistral 7B respectively. AVAILABILITY AND IMPLEMENTATION: The code, data access, and scripts are available at: https://github.com/SINTEF-SE/LLMDap.

Metadata↗

PubMed2025

ClarID: A Human-Readable and Compact Identifier Specification for Biomedical Metadata Integration.

BACKGROUND: In biomedical research, subjects and biospecimens are commonly tracked using simple IDs or UUIDs, which guarantee uniqueness but convey no embedded semantic information. Contextual metadata (such as tissue type, diagnosis, or assay) is often stored separately, making integration, cohort selection, and downstream analysis cumbersome. While structured barcoding systems exist in large consortia (e.g., TCGA, GTEx) or domain-specific contexts (e.g., SPREC, GOLD), no unified, extensible framework currently spans both subjects and biosamples in a human- and machine-readable way. METHODS: We developed ClarID, a domain-agnostic specification that supports two identifier formats: (i) a human-readable form (e.g., 'CNAG_Test-HomSap-00001-LIV-TUM-RNA-C22.0-TRT-P1W' that encodes key metadata such as project, species, subject_id, tissue, assay, disease, timepoint and duration (from that event); and (ii) a compact version named 'stub' (e.g., 'CT01001LTR0N401T1W') optimized for filenames, pipelines, and labeling.ClarID is implemented through an open-source command-line tool, ClarID-Tools, which processes tabular metadata files (CSV/TSV) and uses a YAML-based codebook to generate, decode, and validate identifiers, as well as to create and read QR codes. The tool supports bulk and single-sample processing and allows easy integration with institutional workflows. RESULTS: To demonstrate ClarID's utility, we applied it to datasets from the Genomic Data Commons (GDC), generating interpretable identifiers for more than 113,000 clinical records (subjects) and 4,255 biospecimen records. All materials, including pre-processing scripts, input and encoded data, are publicly available and fully reproducible via the accompanying GitHub repository and Google Colab. CONCLUSIONS: ClarID fills a critical gap between opaque accession numbers and rich metadata schemas by embedding key context directly into structured identifiers. It enhances traceability, facilitates downstream analysis, and remains adaptable to project-specific needs through a configurable codebook. The accompanying ClarID-Tools software is freely available, together with full documentation and reproducible pipelines, at https://github.com/CNAG-Biomedical-Informatics/clarid-tools.

Biosample identifiers↗

PubMed2026

pmultiqc: An Open-Source, Lightweight, and Metadata-Oriented QC Reporting Library for MS Proteomics.

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, FragPipe, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the Sample and Data Relationship Format format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

Proteomics↗

PubMed2026

Meta2DB: curated shotgun metagenomic feature sets and metadata for health state prediction.

SUMMARY: Meta2DB is a curated metagenomic and metadata database that provides structurally consistent microbiome taxonomy feature count tables for 13 897 samples across 84 studies, 23 disease states, and 34 geographical locations. All samples were uniformly processed using a streamlined metagenomic classification pipeline that employs a unique and comprehensive reference database indexed to contain all sequences across all kingdoms of life that were present in the NCBI Nucleotide (nt) database retrieved on 4 January 2023. This pipeline leverages high-performance computing (HPC) resources at Lawrence Livermore National Laboratory and was used to process 50TB of publicly available raw metagenomic sequence data. Extensive metadata curation was carried out through a combination of manual curation and automated parsing, producing a consistent inter-study metadata table specifically structured to facilitate training of ML models for prediction of human health. AVAILABILITY: Data is available at https://gdo-meta2db.llnl.gov/ and https://zenodo.org/records/17315984.

Metadata↗

PubMed2026

Microbiome Datahub: an open-access platform integrating environmental metadata, taxonomy, and functional annotation for comprehensive metagenome-assembled genome datasets.

BACKGROUND: Metagenome-assembled genomes (MAGs) provide crucial insights into the genomic diversity of uncultured microbes. However, MAG datasets deposited in public repositories such as INSDC are often difficult to reuse due to heterogeneous quality, inconsistent taxonomic and functional annotations, and insufficiently curated environmental metadata. While secondary MAG databases such as MGnify, IMG/M, and SPIRE provide standardized resources, they reconstruct MAGs de novo from public metagenomic reads and therefore do not represent the original MAGs reported in publications. RESULTS: To address this gap, we developed Microbiome Datahub, an open-access platform that systematically aggregates and re-annotates original MAGs from INSDC. We collected 214,427 MAGs, predicted genes by DFAST, performed quality assessment with CheckM, standardized taxonomic assignments with GTDB-Tk, inferred 27 phenotypic traits using Bac2Feature, assigned proteins to MBGD ortholog clusters and KEGG Orthology IDs using PZLAST, and annotated environmental metadata with the Metagenome and Microbes Environmental Ontology. Across these MAGs, the average completeness was 80.5% and contamination 1.8%; notably, the most frequent values were >95% completeness and <1% contamination, indicating that the majority of MAGs are of high quality. Comparative analyses showed that Microbiome Datahub provides phylogenetically and environmentally diverse MAGs: while the majority originated from vertebrate gut environments, a substantial number were also recovered from other habitats such as groundwater, including nearly 10,000 MAGs from the Patescibacteria. Inference of 27 phenotypic traits, including optimum growth temperature, further revealed ecological differentiation across phyla. Protein clustering revealed 56 million identity 40% clusters, with the majority unique compared with MGnify and GlobDB, and ~19% of proteins unassigned to MBGD ortholog clusters, underscoring their novelty. CONCLUSIONS: Microbiome Datahub integrates MAG genome sequences, gene and protein predictions, quality metrics, environmental and taxonomic annotations, ortholog cluster assignments, and phenotype predictions, all accessible via a web interface, API, and bulk downloads. By combining original MAGs with curated metadata and functional annotations, Microbiome Datahub constitutes a comprehensive and reusable resource that will accelerate microbiome and microbial genomics research. Video Abstract.

Metagenome↗

PubMed2026

MetaServe: a lightweight, metadata-aware governance and delivery layer for pre-publication research omics data.

BACKGROUND: Institutional research teams and core facilities routinely manage pre-publication omics datasets that span heterogeneous file types, nested project structures, and multiple downstream uses. Public repositories mainly support post-publication dissemination, while workflow systems and enterprise data platforms do not directly provide a lightweight governance and delivery layer for internal research assets. RESULTS: We present MetaServe, an open-source governance and delivery layer for pre-publication research assets in institutional multi-omics settings. MetaServe registers and delivers heterogeneous assets, including sequencing files, processed matrices, imaging data, analysis-ready objects, tabular files, and documents, without requiring repository-grade standardization. Its metadata-aware design combines file-type recognition, partial automatic extraction for selected formats, manually supplied project and biological annotations, and indexed faceted retrieval. MetaServe supports authenticated web download, viewer-oriented handoff for compatible services such as cellxgene, and path-manifest export for downstream workflows under shared-storage assumptions. The current implementation combines role-based controls, explicit file-level sharing, path-constrained delivery, and operational traceability to support controlled institutional access. MetaServe has been deployed at the Chinese Institutes for Medical Research (CIMR) as part of an institutional multi-omics data-management system. CONCLUSIONS: MetaServe provides a practical layer between institutional storage and downstream analytical platforms for pre-publication research data. Its contribution is the integration of lightweight metadata-aware registration, permission-aware retrieval, and controlled delivery for heterogeneous institutional omics assets. Rather than replacing workflow engines, public repositories, or enterprise-scale research data platforms, MetaServe offers a deployable governance layer for core facilities and collaborative teams that need structured discovery and traceable delivery before public deposition or manuscript release.

Metadata↗

PubMed2026

The SARS-CoV-2 Integrated Genomic Epidemiology Database (IGED): Linking viral genomes with patient-level metadata to advance statewide genomic surveillance in California.

In July 2021, the California Code of Regulations Title 17 required all laboratories performing SARS‑CoV‑2 whole genome sequencing (WGS) to report their sequencing results to the California Department of Public Health (CDPH). These viral genomic data and patient metadata were compiled into the Integrated Genomic Epidemiology Database (IGED). Linking anonymized viral sequences with patient‑level information enabled monitoring of infectiousness, pathogenicity, transmission dynamics, evolution, and vaccine evasion among emerging SARS‑CoV‑2 lineages. Laboratories performing SARS-CoV-2 WGS transmitted sequencing results to CDPH through Electronic Laboratory Reporting (ELR) and non-ELR pathways. CDPH applied uniform reporting requirements but allowed flexibility in specific data formats to accommodate diverse data systems. To preserve data quality and interoperability across heterogeneous sources, CDPH implemented standardization, validation, and deduplication protocols. Snowflake, a cloud‑based data storage and analytics platform, and Posit Connect, a cloud deployment and automation platform, supported the management, processing, and integration of data within the IGED. The IGED established links between SARS‑CoV‑2 WGS data and epidemiologic metadata for 801,418 sequences, representing 81.7% of all sequences reported in California. Lineages reported to the IGED showed strong concordance with lineage proportions in GISAID. Sequences reported to the IGED had average turnaround times longer than one month, and the majority of sequencing was performed in Southern California and Los Angeles. The IGED enhanced genomic surveillance through predictive modeling and monitoring concerning evolutionary trends such as recombination and saltations in persistent infections. Development of the IGED highlighted the need for standardized data requirements, sustained funding for sequencing, incentives for data submission, and interdisciplinary collaboration to build an effective genomic surveillance system. This framework for linking genomic and epidemiologic data has not only generated critical insights for SARS‑CoV‑2 but also provided the foundation for CDPH and other public health organizations to develop similar IGED‑like systems for other priority pathogens as genomic surveillance expands.

Journal Article↗

PubMed2026

An integrated global resource of wetland microbiomes linking environmental metadata, community profiles, and genome-resolved metabolic traits.

Wetlands are biogeochemical hotspots pivotal to global carbon and nutrient cycling, yet genome-resolved studies across diverse wetland types remain limited. To address this, we constructed a global wetland metagenomic dataset, integrating environmental metadata, community profiles, and genome-resolved metabolic traits. This dataset comprises 1,962 samples-including 129 newly sequenced field-collected samples-from lakes, rivers, paddies, marshes, and coastal wetlands, spanning water, soil, and sediment habitats. We generated comprehensive taxonomic profiles for all 1,962 samples, and used 251 samples to reconstruct 5,704 sample-specific metagenome-assembled genomes (MAGs). These MAGs were subsequently dereplicated to establish a normalized, non-redundant catalog of 4,164 representative genomes. We further mapped gene repertoires to 549 KEGG modules to decode the metabolic potential of all 5,704 MAGs. This dataset depicts an overview of microbial genomic diversity across global wetlands and provides a comprehensive resource for understanding the metabolic capabilities, ecology, and evolution of wetland microbiomes.

Wetlands↗

PubMed2025

Consistently processed RNA sequencing data from 50 sources enriched for pediatric data.

Larger cohorts improve the power of tumor gene expression analysis, but the signal is muddied if datasets are processed using different methods or have inaccurate metadata. Here we present five compendia containing consistently processed gene expression data derived from 16,446 diverse RNA sequencing datasets. To create the compendia, we obtained access to RNA sequence data from repositories containing public data as well as clinical partners with access to non-published data. We then assessed the quality, quantified gene expression, harmonized clinical metadata, and released the expression values and metadata without access restrictions. These datasets have been used for diverse projects ranging from identifying similarities between tumor types to assessing how well cell lines recapitulate tumors. They have also been used for n-of-1 analysis to identify genes with unusual expression patterns in a single sample and to infer molecular diagnosis. The comparison to new data is enabled by our dockerized, freely available pipeline. The compendia have been cited in at least 20 publications.

Humans↗

PubMed2026

In genomes we trust: Assessing genomic reliability within the family Nectriaceae.

Reliable evolutionary inference increasingly depends on public genome resources, and the effects of uneven assembly quality, incomplete metadata, and biased taxonomic sampling remain poorly quantified. Using the species-rich fungal lineage Nectriaceae as a model system, we analysed 1530 genome sequence assemblies to assess metadata completeness, sampling representation, and genome quality. One-third of the assemblies lacked essential metadata, sequencing was heavily skewed toward a few agriculturally important lineages, and sampling of many genera was limited or nonexistent. BUSCO and QUAST metrics revealed substantial heterogeneity in assembly quality, with widespread fragmentation and numerous assemblies falling outside expected quality thresholds. From 763 single-copy orthologs identified in 576 higher-quality genomes, we reconstructed a phylogenomic backbone and quantified gene- and site-level concordance across the tree. Although major clades were broadly recovered, extensive gene-tree discordance and a polyphyletic Fusarium nisikadoi species complex revealed unresolved boundaries and conflict among loci. These results show how data quality, incomplete sampling, and discordant genomic histories can constrain phylogenomic resolution, and provide a general framework for improving comparative genomic resources and large-scale evolutionary inference.

Gene-tree discordance↗

PubMed2026

GenBank mining reveals novel insights into Rhizobium phylogeny: Identical 16S rRNA sequences are mainly uncoupled from species designation, host plant, and geographic origin: How this search suggested the definition of a direct 'microbial h-index'.

16S rDNA is the historical gold standard for bacterial identification, particularly in metabarcoding approaches reliant on sequence similarity thresholds. We analyzed 6,660 Rhizobium 16S rRNA gene sequences from GenBank to examine the relationship between sequence identity and three metadata: species name, host plant, and geographic origin. Using an iterative BLAST-based pipeline, we detected 116,069 pairwise matches and assessed concordance among sequences (average length 1,328 bp) sharing 100% identity. For those in which the organism name, host plant and country of isolation were present in the record, surprisingly, 66.59% of identical sequence pairs showed full discordance across all three metadata, while only 1.40% shared the same name, host, and country. The most widespread sequence, detected 371 times, was associated with over 56 different host plants across 25 countries and bore multiple species name designations. These results highlight a striking mismatch between the 16S barcode and the taxonomic, ecological, and phenotypic variability it is assumed to reflect, likely arising from the slow evolution of rRNA genes contrasted with the mobility of ecologically relevant genes via horizontal transfer on plasmids, transposons, and phages. Our findings further challenge the limitations of relying on 16S rRNA alone for fine-scale taxonomic and metadata-based inference in capturing the true functional and ecological diversity of bacteria, endorsing the critical importance of polyphasic taxonomic approaches that integrate genomic, phenotypic, and ecological data. An interesting byproduct of the analysis was to realize the possibility of treating these data as if they were 'citations.' The more one finds the same query sequence, the more that sequence can be considered biologically 'cited', i.e., re-proposed elsewhere in the world. Thus, one can also analyze the h-index of such a ranking. In our Rhizobium dataset, we calculated an h-index = 201, meaning the sequence ranked 201st had 202 identical homologues in GenBank. Although the research effort on given species is directly connected with it, this number provides a quantitative indicator of a taxon's sequence recurrence and distribution within public databases, independent of nomenclatural inconsistencies, offering a novel framework for assessing bacterial representation across global datasets.

RNA, Ribosomal, 16S↗

PubMed2026

Network-based integration of metabolomics data from large-scale repositories.

INTRODUCTION: Public metabolomics data repositories such as MetaboLights and Metabolomics Workbench host rapidly growing volumes of raw data, processed results, and metadata. As data deposition becomes a prerequisite for funding and publication, there is an increasing need for tools that enable integration and joint reanalysis of datasets across studies to maximise reuse and reproducibility. OBJECTIVES: This study aims to enable large-scale integrative meta-analysis of public metabolomics data, exploiting harmonised metabolite annotations to identify robust multi-study metabolite and pathway signatures and to provide global visual overviews of repository content. METHODS: We developed a network-based integration framework operating at both the study (dataset) level and the metabolite or pathway level. Metabolite-level meta-networks integrate studies with shared biological context using co-occurrences of differential metabolites represented as bipartite graphs. Study-level networks compare observed metabolites for overall repository exploration. Networks can be explored interactively using a dedicated Python Dash app available at https://github.com/EloisaRL/Metabolomic-data-analysis-app/tree/main . RESULTS: As an example, the approach was applied to six COVID-19 plasma datasets from MetaboLights generated using LC-MS and NMR. Ten metabolites were identified as differential in at least three studies, including consistently up-regulated pyroglutamic acid, in agreement with the literature. Pathway-level networks provided an overview of shared biological processes across studies. A global network of 1,181 studies in Metabolomics Workbench demonstrated clustering by assay coverage and associated metadata, as expected. CONCLUSION: Network-based integration of harmonised metabolomics data enables robust cross-study analyses and highlights the critical importance of standardised annotation pipelines. Such approaches enhance the reuse, reproducibility, and impact of public metabolomics datasets, accelerating biological discovery.

Metabolomics↗

PubMed2026

scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository.

Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata. Here, we introduce scBaseCount, a database that leverages an AI agent to automate discovery and metadata extraction and standardize data processing. Built by mining all 10x Genomics datasets, scBaseCount is the largest public repository of single-cell gene expression data, comprising over 502 million cells across 27 organisms and 75 tissues. It offers an unbiased view of the data landscape within the SRA and enables the training of more performant computational models through access to broader phenotypic diversity. Uniform processing enables measurement of both intronic and exonic reads and non-coding gene expression and improves alignment across experiments. Moreover, scBaseCount provides a blueprint for how AI can be leveraged to autonomously curate biological data repositories.

Single-Cell Analysis↗

PubMed2026

DeeDeeExperiment: building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor.

SUMMARY: Modern omics experiments now involve multiple conditions and complex designs, producing an increasingly large set of differential expression and functional enrichment analysis results. However, no standardized data structure exists to store and contextualize these results together with their metadata, leaving researchers with an unmanageable and potentially non-reproducible collection of results that are difficult to navigate and/or share. Here we introduce DeeDeeExperiment, a new S4 class for managing and storing omics data analysis results, implemented within the Bioconductor ecosystem, which promotes interoperability, reproducibility and good documentation. This class extends the widely used SingleCellExperiment object by introducing dedicated slots for Differential Expression (DEA) and Functional Enrichment Analysis (FEA) results, allowing users to organize, store, and retrieve information on multiple contrasts and associated metadata within a single data object, ultimately streamlining the management and interpretation of many omics datasets. AVAILABILITY AND IMPLEMENTATION: DeeDeeExperiment is available on Bioconductor under the MIT license (https://bioconductor.org/packages/DeeDeeExperiment), with its development version also available on Github (https://github.com/imbeimainz/DeeDeeExperiment).

Software↗

PubMed2026

The need for standardization and improved open (meta)data practices in metaproteomics.

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices. Video Abstract.

Proteomics↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2026

Effect of repeated mass drug administration on the transmission of yaws: a retrospective genomic epidemiology study.

BACKGROUND: Yaws, a neglected tropical disease caused by Treponema pallidum subspecies pertenue (T p pertenue), has evaded eradication, in part due to a high proportion of asymptomatic cases. Repeated mass drug administration (MDA), whereby an entire population is repeatedly treated irrespective of disease, could provide a solution. Here, we aimed to investigate the effect of MDA on the genomic epidemiology of T p pertenue. METHODS: We conducted a retrospective genomic epidemiology study on samples collected during a cluster-randomised trial of mass administration of azithromycin for yaws eradication in the Namatanai District of Papua New Guinea. Participants were in 38 wards (administrative units encompassing several villages) in three local-level government areas (LLGs). The experimental group received an initial round of MDA followed by two further rounds 6 months and 12 months after the first round. The control group received one round of MDA followed by two rounds of treatment targeting clinical cases and contacts only, on the same schedule as the MDA in the experimental group. A follow-up survey on both groups was done 18 months after the first MDA round. Swab samples were collected at each round from ulcerative and nodular skin lesions, and blood was collected by finger-prick for serological testing at 18 months. Metadata on ulcer size (cm) and duration (days) were recorded at each round, and treponemal and non-treponemal antibodies were recorded at 18 months. Samples from swabs positive for T p pertenue underwent library preparation and whole-genome sequencing. We examined the phylogenetic relationships between genomes, linking them with geospatial and patient metadata to understand the impact of MDA on T p pertenue diversity and transmission. FINDINGS: Swabs collected from 297 individuals with active yaws from April 30, 2018, to Nov 2, 2019, yielded 222 good-quality Tp pertenue genomes. We identified 20 sublineages of T p pertenue in the control group and 21 in the experimental group at the beginning of the study. At the end of the study, there were 13 sublineages in the control group and three in the experimental group, of which two persisted in both groups. Three sublineages not detected at baseline were observed in the control group after commencing MDA. The two sublineages that persisted in both groups had non-synonymous mutations in penicillin-binding proteins. One of these sublineages evolved macrolide resistance in three individuals and was associated with lowered treponemal antibody (p=0·0036) and longer ulcer duration (p=0·015). Despite the study taking place within a small island, sublineages were geographically clustered, with pairs of samples from the same ward (odds ratio 7·1, 95% CI 5·7-8·8; p<0·0001) or neighbouring wards (4·3, 3·3-5·4; p<0·0001) more likely to share the same sublineages compared with pairs from different LLGs. Additionally, older individuals were more likely to share sublineages than were younger individuals (1·5, 1·2-1·9; p<0·0001). INTERPRETATION: Repeated MDA was successful in reducing and maintaining the genetic diversity of T p pertenue at a low level but was associated with the development of macrolide resistance. Yaws re-emergence after MDA was attributed to multiple sublineages, of which the majority were detected in the population before MDA. Participants within the same ward were more likely to share sublineages than those that were more widely geographically separated, suggesting that re-emergence was driven by local transmission. These findings could inform future yaws elimination strategies. FUNDING: European Research Council, EU, Provincial Deputation of Barcelona, Barberà Solidària Foundation, Wellcome, and Fundació "la Caixa".

Adolescent↗