GigaScience

BackgroundKillifish have emerged as valuable vertebrate model systems for investigating several disciplines including aging, regeneration, and developmental biology. Multi-omics datasets are increasingly being generated for killifish. However, their reuse remains limited due to computational challenges, largely due to the lack of accessible resources creating a bottleneck in widespread adoption of the killifish model. To address this, we developed Killiverse, a web resource for quick and intuitive exploration of multi-modal omics data dedicated to the model organism. ResultsKilliverse is an interactive, no-code, web-based platform designed for exploration of killifish multi-omics data. The platform aggregates a growing list of datasets including bulk transcriptomes, single-cell and single-nucleus transcriptomes, proteomes, and lipidomes processed through standardized pipelines and genome assemblies. Killiverse supports customized visualization and enables cross-study and cross-species analysis. It provides ortholog mapping to several established model organisms. By combining low-code software development with modern cloud technologies, the platform delivers a scalable browser-accessible application for the community. ConclusionsKilliverse enables rapid hypothesis development through the identification of patterns across studies and species. The ortholog maps allow the findings to be placed in a broader biological context. The platform represents an innovation in genomics data visualization that will serve as a template for future tool development. Killiverse is freely accessible at https://killiverse.org/.

BackgroundThe growing diversity of genomic and epigenomic assays has driven a parallel expansion in data formats, analysis workflows, and figure-generation tools. However, tools for analyzing data and assembling publication-quality figures are often specialized to a specific assay, dramatically limiting their interoperability and reproducibility. ResultsWe present the v1.0 release of ScriptManager, a Java-based framework for modular and reproducible analysis and visualization workflows of genomics and epigenomics data. Unlike existing tools specialized for individual assay types, ScriptManager provides a unified and extensible framework for cross-assay visualization and workflow reproducibility. The v1.0 release adds novel analytical modules, GUI session logging, automated unit and integration testing, tutorials, and expanded documentation. It also integrates with the broader reproducibility ecosystem through Singularity containers, Anaconda packaging, and Galaxy XML wrappers. We demonstrate ScriptManagers TagPileup scaling from local single-core execution to a 10,305-job analysis distributed across the Open Science Grid (OSG), with the full workload completing in <2 hours of wall-clock time. ConclusionsScriptManager v1.0 enhances workflow portability, transparency, and reproducibility across a diverse range of high-resolution genomic assays. By coupling a flexible module design with modern reproducibility standards, ScriptManager provides a bridge between exploratory data analysis and formal, publication-ready figure generation. These improvements enable researchers to build, share, and reproduce genomic analyses across diverse computational infrastructures with minimal configuration.

BackgroundNext-generation sequencing (NGS) projects generate increasingly complex metadata that are critical for reproducibility, interoperability, and compliance with FAIR principles. Nevertheless, metadata curation in multi-institutional settings often still relies on spreadsheets, manual data entry and curation, as well as non-standardized terminology. These practices frequently result in incomplete or inconsistent annotations, hinder metadata sharing, and delay submission to public repositories. ResultsWe developed Metadata Collector as a React/API/PostgreSQL web platform and deployed it on a Kubernetes cluster within a large German research consortium. The platform implements a flexible, machine-readable metadata model for experimental data and integrates customizable templates, controlled vocabularies designed to support future ontology integration, and a complete event-based versioning model. Since deployment, Metadata Collector has been used across 32 projects involving RNA-seq, scRNA-seq, ATAC-seq and multiomics datasets, representing over 700 annotated samples contributed by multiple consortium partners. The platform is designed for use by non-computational researchers as well as centralized facilities and can be integrated into existing research data management infrastructures. ConclusionsMetadata Collector embeds standardization early in the metadata lifecycle, ensuring consistent, FAIR-aligned, and reproducible metadata across distributed research groups. Its modular, open-source architecture supports both local and consortium-scale deployments and provides a foundation for future extensions, including multi-omics support and integration with laboratory information management systems and automated submission pipelines. AvailabilityOpen-source under MIT license: https://github.com/spang-lab/metadata-collector Graphical abstract of Metadata Collector O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/730314v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@c7e913org.highwire.dtl.DTLVardef@9701acorg.highwire.dtl.DTLVardef@1eed3eborg.highwire.dtl.DTLVardef@9af69a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Summary: The X chromosome comprises approximately 5% of the human genome and encodes over 800 protein-coding genes, many of which exhibit sex-differentiated expression patterns due to escape from X chromosome inactivation (XCI) mechanisms. Despite its relevance to sex differences in complex traits, the X chromosome is routinely excluded from genome-wide association studies due to analytical challenges, and when analyzed, the impact of escape from XCI or sex is limitedly explored. No dedicated, publicly accessible browser for X chromosome-wide association study (XWAS) summary statistics currently exists, creating a barrier to systematic investigation of X-linked contributions to human traits. Here, we present geneXplore, an interactive web browser based on the PheWeb2 implementation, tailored for XWAS summary statistics across 1,944 phenotypes while distinguishing random XCI (rXCI), escape from XCI (eXCI), and sex-stratified analyses. Users can explore results via interactive plots (Manhattan and Miami, PheWAS and LocusZoom), searchable tables and access to cross-database lookup, with full summary statistics available for download. Availability and Implementation: geneXplore is freely available at https://genexplore.wustl.edu/ with no registration required and will be maintained for a minimum of two years following publication. Source code is available at https://github.com/Belloy-Lab/geneXplore_XWAS_Browser under an MIT license.

Metagenomic assembly can be a computationally intensive step in microbiome analysis, with memory requirements that vary widely depending on input data characteristics. In workflow systems like Galaxy and large-scale platforms like MGnify, which run thousands of heterogeneous jobs, inaccurate memory allocation drives job failures and costly retries when underestimated, and reduces throughput when overestimated. Current approaches rely primarily on heuristic rules based on input file size or sample metadata, which often fail to generalize across diverse datasets. In this study, we present a machine learning-based framework for predicting memory requirements of metagenomic assembly using metaSPAdes. We analyzed 300 assembly jobs from diverse biomes and evaluated 18 predictive models using combinations of input file size, biome classification, and sequence-derived k-mer features. K-mer profiles were computed from raw sequencing data and summarized into statistical descriptors capturing sequence complexity and diversity. Model performance was assessed using both conventional regression metrics and a production-oriented cost function that accounts for retry policies and resource waste in high-performance computing environments. Our results show that machine learning models can outperform commonly used heuristics. In particular, models incorporating biome information achieved the best performance and can be tuned to favor conservative predictions that reduce job failure rates. Simpler models based solely on input file size also performed competitively, offering a practical alternative for systems with limited feature availability. When evaluated under realistic workload distributions, predictive approaches reduced total memory waste by several million gigabyte-hours per 1,000 jobs compared to static allocation strategies. These findings demonstrate that data-driven resource prediction can substantially improve efficiency in metagenomic workflows. The proposed framework is adaptable to different computational environments and provides a foundation for integrating predictive resource allocation into large-scale bioinformatics platforms beyond Galaxy.

BackgroundOxford Nanopore Technologies (ONT) sequencing is increasingly used for whole-genome sequencing (WGS) across a wide range of applications. However, the platform has evolved rapidly through updates to flow cell chemistry and basecalling algorithms, altering the characteristics of the resulting sequencing data. Read simulators provide synthetic datasets with known ground truth, enabling controlled development and evaluation of methods. However, many existing simulators were developed for earlier versions of ONT sequencing or use generic long-read assumptions, and their realism for contemporary ONT data is unclear. ResultsWe benchmarked six ONT-compatible read simulators (Badread, LongISLND, lrsim, NanoSim, PBSIM3 and SimLoRD) using a microbial genome reference and ONT R10.4.1 reads as the empirical standard. Each tool was configured to maximise realism, including training on empirical reads when supported. We compared simulated and real datasets with respect to read length, read accuracy, FASTQ quality scores and sequence error profiles. No simulator reproduced all metrics of the real data well. PBSIM3 most closely reproduced read length, read accuracy and FASTQ quality scores, making it a strong simulator for broad read-level realism. However, it did not capture important features of the real error profile, including context-dependent substitution rates and homopolymer-length errors. Badread and LongISLND better reproduced some aspects of the error profile, but showed other departures from the real data. ConclusionPBSIM3 is a good general-purpose choice for many ONT WGS simulation tasks because it reproduced several key read-level properties well. However, Badread or LongISLND may be preferable for applications where error structure is more important. No evaluated tool was realistic across all tested metrics, highlighting a gap for improved long-read simulators.

MotivationExisting pangenome file formats are designed for batch processing. Graphs must be loaded into memory, and alignment files must be read sequentially. Indexed file formats that can be used directly from disk would be more appropriate for interactive applications. ResultsWe propose GBZ-base and GAF-base -- SQLite-backed file formats comparable to GBZ and GAF. GBZ-base supports efficient extraction of local subgraphs, and GAF-base lets us extract all alignments to the subgraph. Additionally, GAF-base is smaller than any other file format for sequence-to-graph alignments. Availability and implementationFrom https://github.com/jltsiren/gbz-base and https://crates.io/crates/gbz-base under the MIT license.

SummaryBatch effects and other unwanted technical sources of variation remain a persistent challenge in the integrative analysis of high-dimensional-omics data. Although established methods such as ComBat effectively mitigate batch-associated signal, their impact on biologically meaningful variation is frequently evaluated in an ad hoc and non-quantitative manner. This is particularly problematic in heterogeneous disease contexts, such as breast cancer transcriptomics, where technical and biological sources of variation may be partially confounded. We present BatchVaria, an R package that implements a variance-aware framework for batch correction and post-adjustment evaluation. BatchVaria integrates variance component modelling, batch adjustment, and systematic re-profiling within a unified analysis container, enabling iterative quantification and reassessment of technical and biological variance contributions while preserving analytical provenance. By supporting multiple variance profiling engines and structured storage of intermediate results, BatchVaria facilitates transparent and reproducible evaluation of batch correction strategies. We demonstrate the utility of BatchVaria using a publicly available breast cancer transcriptomic dataset with known covariate-driven structure, illustrating how iterative variance profiling can guide responsible batch correction without erosion of subtype-associated biological signal.

Performance bottlenecks in widely used genomics and bioinformatics software present a substantial and growing burden as biological datasets continue to increase in size and number. Relieving these bottlenecks relies largely on expert manual optimization and therefore remains difficult to scale. Here we present AutoZyme, an agentic framework for scientific software optimization. Given a target function, AutoZyme builds benchmarks, identifies bottlenecks, and iteratively tests code changes, retaining only those that improve runtime while preserving output. We evaluated AutoZyme on 45 functions, improving runtime without substantial memory increases in over 95% of cases considered. Across 38 functions from Seurat, Scanpy and related packages in genomics and bioinformatics, AutoZyme reduced runtime by a median of 8.52-fold, with the largest reductions exceeding 676-fold. The optimized functions are distributed through AutoZyme-Library as drop-in replacements for existing analysis pipelines. We also release AutoZyme as a reusable framework for optimizing additional user-specified packages and functions.

Chromatin-based genomics data are essential for understanding genome regulation and the mechanisms underlying epigenetic memory. Recent methods such as ChOR-seq and SCAR-seq assess histone modifications and chromatin-associated proteins during and after replication, capturing chromatin states that contribute to memory across cell divisions. Current tools for chromatin data analysis lack scalability and reproducibility across computing infrastructures, offer limited parameters, and are applicable only to a few sequencing techniques, ignoring the information from nascent chromatin assays. To address these challenges, we developed CREPAS, a Nextflow pipeline for analyzing nascent and parental chromatin sequencing data, including ChIP-seq, ChOR-seq, SCAR-seq, OK-seq, ATAC-seq, CUT&RUN, and CUT&Tag, and derivative protocols. CREPAS provides an end-to-end solution, from quality control to advanced analyses, including downsampling, peak calling, annotation, and visualization. By harnessing quantitative assays such as qChIP-seq and qChOR-seq, the normalization methods in CREPAS allow to compare the restoration kinetics of individual marks or proteins across replication timepoints. Moreover, the pipeline includes calculations such as fork directionality and partitioning using OK-seq and SCAR-seq data, linking replication dynamics to epigenetic inheritance. CREPAS is a valuable resource that enhances the efficiency and reproducibility of nascent chromatin sequencing data analyses, enabling the study of chromatin replication and propagation of epigenetic states. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/732899v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@29ad26org.highwire.dtl.DTLVardef@26b9acorg.highwire.dtl.DTLVardef@67dcb8org.highwire.dtl.DTLVardef@cbc299_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundLarge language model (LLM) agents increasingly automate bioinformatics analyses, but most existing bioinformatics tools were built for standalone use by human experts. An agent driving such a tool must reason about its installation, configuration, and execution from documentation for human, spending many turns, tokens, and tool calls per result. How a method is exposed to an agent can therefore matter as much as the method itself. By designing agentic interfaces for these tools, agent can reduce such overhead and improve the reliability of agent-driven analyses. FindingsTo test this design, we re-architected Clair3, a widely used deep-learning-based long-read variant caller, into a client-server system, Clair3-Connect. The client performs all genomics related processing and holds the identifiable data. The server runs only neural-network inference, and the client sends only feature tensors to the server, while sample identifiers and genomic context remain on the client. The client exposes schema-defined agent-facing tools that an agent invokes through single structured calls. On an APOE diplotyping task, all 60 agent runs were correct. The agentic tools used 12K tokens in 3 turns, 6.8 to 14 times fewer tokens than the shell-driven baselines (81K-163K tokens), at about a quarter the wall-clock time and far more stably (4% versus 35% token usage variation). Dropping the pileup and phasing stages to keep the client light left SNP F1 within 0.1-0.3 points of standard Clair3 by 50x coverage, while mutual TLS and AES-256-GCM encryption added 7.2% to end-to-end runtime. ConclusionsRecasting an established algorithm as developer-built, agentic tools behind a secure client-server boundary makes it more efficient, reliable, and easier to deploy for an LLM agent than a third-party wrapper, which cannot recover the defaults and conventions only its developers know. Agentic interfaces should be a first-class deliverable of bioinformatics tool development.

BackgroundThe exponential growth of public metagenomic datasets offers an unprecedented opportunity to explore microbial diversity. However, analyzing this vast amount of data presents significant computational challenges. While shotgun metagenomics provides deep functional and taxonomic resolution, its high cost still limits its application. On the other hand, 16S rRNA gene sequencing remains a cost-effective and widely used alternative, but tools are needed to maximize its discovery potential. Traditional clustering is not scalable, obstructing the creation of a comprehensive and continuously updated catalog of microbial life from 16S data. MethodsWe developed a reproducible and scalable Snakemake pipeline for the incremental clustering of 16S rRNA amplicons. The workflow begins by constructing a reference database from bacterial and archaeal genomes. It then processes 16S rRNA samples sequentially. For each new sample, sequences are first mapped against the existing cluster centroids. Sequences that match known centroids are assigned accordingly, while unmapped sequences are clustered independently to form novel operational taxonomic units (OTUs). These new centroids are then merged with the existing database, allowing it to grow dynamically without the need for computationally prohibitive all-at-once re-clustering. ResultsOur pipeline enables the efficient and continuous expansion of a 16S rRNA cluster database. By processing a large corpus of public 16S rRNA samples, we generated a comprehensive atlas of tens of thousands of OTUs. A significant fraction of these clusters, particularly at the genus and family levels, were classified as unknown. ConclusionsThis work provides a powerful, open-source tool for large-scale analysis of 16S rRNA samples. The incremental clustering strategy overcomes the scalability limitations of traditional methods, allowing researchers to leverage public data and discover novel microbes in their own microbiome samples.

Durum wheat (Triticum turgidum subsp. durum) is a globally important crop for pasta and couscous production. Chili and Mahmoudi are historically significant Tunisian landraces valued for exceptional grain quality, high protein content, and adaptation to arid Mediterranean climates. Yet no high-quality reference genome assemblies were available for either variety before this work. We assembled both genomes using publicly available PacBio HiFi long reads and Illumina Hi-C proximity ligation data deposited under NCBI BioProject PRJNA1420514. HiFi reads were assembled with hifiasm v0.25.0 in primary mode, and Hi-C scaffolding was performed with YAHS v1.2a.2 after read alignment with BWA-MEM. Assembly quality was assessed with QUAST v5.3.0 and BUSCO v5.8.0 (embryophyta_odb10 lineage). The Chili assembly spans 10.84 Gbp across 3,472 scaffolds with a scaffold N50 of 844.9 Mbp and BUSCO completeness of 99.4%. The Mahmoudi assembly spans 10.70 Gbp across 3,258 scaffolds with a scaffold N50 of 2,072 Mbp and BUSCO completeness of 99.3%. Merqury v1.3 confirmed high base accuracy (QV 68.0 for Chili, QV 68.3 for Mahmoudi) and k-mer completeness (>98% for both). Independent validation with wfmash confirmed 98.6% mean alignment identity across 11,172 chromosome-to-reference alignments. Post-assembly characterization of GC profiling, centromere architecture, ribosomal DNA arrays, and structural variation revealed extensive genome-level detail. Both assemblies substantially exceed the contiguity of existing durum wheat references and represent the first chromosome-scale-contiguity genomic resources for North African durum wheat landraces. The Mahmoudi accession carried a putative 2B-3B homeologous fusion on chromosome 3B (4,710 Mbp), a structural novelty in an ancient Tunisian landrace. Assembly and Pseudomolecules available from Zenodo (10.5281/zenodo.20366290). The workflow was executed reproducibly on the public Galaxy Europe platform, demonstrating that reference-quality plant genome assembly is achievable without local HPC infrastructure. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=179 HEIGHT=200 SRC="FIGDIR/small/727644v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@159c9d4org.highwire.dtl.DTLVardef@1d1a0deorg.highwire.dtl.DTLVardef@19853dborg.highwire.dtl.DTLVardef@1a96018_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIFirst chromosome-scale-contiguity assemblies for Chili and Mahmoudi landraces C_LIO_LIChili genome: 10.84 Gbp, scaffold N50 844.9 Mbp, BUSCO 99.4% C_LIO_LIMahmoudi genome: 10.70 Gbp, scaffold N50 2,072 Mbp, BUSCO 99.3% C_LIO_LIMerqury QV ~68 confirms base accuracy, >98% k-mer completeness C_LIO_LI>140-fold scaffold N50 improvement over Svevo v1 reference C_LI

A plethora of studies have identified shared molecular mechanisms involved in tumor development across humans and other mammalian species. While these two-species analyses advance understanding of human disease, extending them across many species would provide evolutionary insight into molecular mechanisms driving human cancers. However, this expansion requires knowledge transfer and harmonization across species. Genomic differences between species, including variation in genome annotation quality, have historically hindered multi-species large-scale atlas creation. To overcome these challenges, we present Paipu, a comprehensive pipeline designed to streamline querying, preprocessing, harmonization, and retrieval of large-scale RNA-seq data and associated metadata from the NCBI Sequence Read Archive (SRA). Paipu facilitates multi-species analysis by creating a harmonized atlas from user-defined search terms and species. It consists of three components: reference genome preparation, SRA metadata retrieval, and RNA-seq data processing. We apply Paipu to 188 cancer-related terms in 239 non-human mammalian species, creating a harmonized atlas of 3,484 RNA-seq samples spanning 17 species and 35 cancers. This pan-mammalian pan-cancer atlas enables myriad comparative genomics analyses that leverage genetic variation to better understand rare human cancers. As such, Paipu serves as a resource for cross-species cancer genomics and supports atlas creation for any set of species and search terms. Graphical Abstract

BackgroundScratch assays are widely used to study wound closure in vitro, but quantitative image analysis remains constrained by manual variability, proprietary workflows, and tools requiring programming expertise. We developed InVitroGap, a Python-based application with a browser-accessible interface for automated quantification of scratch assay closure from sequential microscopy images. MethodsRCC-ER and Renca cells were seeded in 96-well ImageLock plates and scratched using a WoundMaker device for uniform linear wounds or a 200 {micro}L pipette tip for crisscross wounds. Phase-contrast time-lapse images acquired at 0, 24, and 48 h with an IncuCyte SX5 system were independently analyzed using IncuCyte 2023A Rev2 and InVitroGap. The InVitroGap pipeline combines Gaussian smoothing, gradient-based texture mapping, adaptive percentile thresholding, and morphological post-processing to quantify wound confluence and relative wound density (RWD). Agreement was evaluated using paired comparisons, Pearson and Spearman correlations, Bland-Altman analysis, and mean absolute error (MAE). ResultsInVitroGap measurements closely tracked IncuCyte outputs across both cell lines, with no significant between-method differences (p > 0.05), strong pooled correlations (R{superscript 2} = 0.964 for RWD; R{superscript 2} = 0.983 for wound confluence), and small mean biases (absolute bias [&le;] 1.64%). The tool successfully processed crisscross wounds from brightfield image series, and a complete four-timepoint series was analyzed in approximately 10 seconds, with robust performance across distinct cell morphologies and wound geometries. ConclusionsInVitroGap provides a transparent, computationally efficient, and platform-independent alternative for scratch assay analysis, delivering performance comparable to commercial systems while remaining freely accessible at https://invitrogap.vercel.app/. HighlightsO_LIOpen-source Python tool for automated, platform-independent in vitro scratch assay analysis C_LIO_LITexture-based adaptive pipelines enable robust wound segmentation across cell types C_LIO_LIQuantifies wound confluence and relative wound density from time-lapse images C_LIO_LIStrong agreement with IncuCyte measurements in the tested datasets C_LI

Cancer incidence varies substantially across geographic regions and demographic groups, yet translating large-scale surveillance datasets into accessible, interpretable visualizations remains a challenge for researchers and public health professionals without computational expertise. We developed OncoContour, an interactive web-based platform that enables geographic visualization and demographic analysis of cancer incidence data through a browser-accessible interface. To demonstrate its capabilities, we analyzed publicly available cancer incidence data from the United States Cancer Statistics database via CDC WONDER, covering five major cancer types across four northeastern U.S. metropolitan statistical areas from 2017 through 2022, supplemented by demographic data from the U.S. Census Bureau American Community Survey. OncoContour integrates population distribution heatmaps, per-capita cancer incidence heatmaps, interactive multi-city temporal trend charts, structured cancer data tables, and demographic visualizations covering race, ethnicity, age, and sex distributions into a single dynamically generated HTML report. The platform is implemented in Python using Flask, Folium, Plotly, and Matplotlib, and is containerized using Docker for reproducible local deployment. Across all four metropolitan areas, breast and prostate cancers accounted for the highest incidence counts over the study period, while a decline in reported cases observed in 2020 is consistent with documented disruptions to cancer screening during the COVID-19 pandemic. By integrating geospatial mapping, temporal analysis, and demographic visualization within a unified, no-code interface, OncoContour aims to support cancer surveillance, epidemiological investigation, and targeted public health planning. OncoContour is freely available at https://github.com/alperuzun/oncocontour_docker.

Biological research often requires information about species traits. Manual literature collation can be time-consuming and miss parts of the literature. To address this gap, we developed trAIt, a publicly available software for the retrieval of characteristics of species from scientific literature catalogued in the Europe PubMed Central (PubMed) database. trAIt provides a graphical user interface (GUI) in which users specify species and characteristics of interest. Leveraging a large language model (LLM), trAIt retrieves relevant papers, combines their content through a consensus-based summarization model, and outputs a species-by-characteristic table. For a case study involving frog species, trAIt recovered 47.1% of trait-species combinations in 2.75 hours, while an expert curator independently recovered 62.4% over months. The consensus-based summarization substantially aids accuracy compared to single-source extraction. Across three case studies of vertebrate taxa, an expert confirmed the accuracy of 70.9% of trait-species entries recovered by trAIt. We observed considerable variation across taxa in trAIts accuracy, which is possibly due to heterogeneity in open-access literature availability and inconsistencies in species and trait terminology. In sum, our analysis suggests that LLM-based tools can accelerate biological data synthesis but should be used to support domain experts research, rather than replace their judgment.

Although vaccine-preventable, measles virus (MeV) continues to pose a significant public health challenge, with a substantial resurgence of cases worldwide. As whole-genome sequencing (WGS) becomes increasingly affordable and routinely adopted in public health laboratories, reliable and accessible analysis of next-generation sequencing (NGS) data is critical for outbreak investigation and molecular surveillance. Here, we present MeaSeq, a fast, user-friendly, open-source bioinformatics pipeline for MeV analysis using Illumina or Oxford Nanopore Technologies (ONT) NGS data. MeaSeq performs quality control assessments, consensus genome assembly and variant detection, optional genotype-specific reference selection, Distinct Sequence Identifier (DSId) assignment via user-provided databases or hashing, sub-consensus variant visualization, genome quality assessment, and standardized HTML reporting. We compared the performance of MeaSeq on NGS data generated from multiple sequencing platforms and targeted enrichment strategies against gold-standard Sanger data, reference genomes, and publicly available comparative data. This validation demonstrates that MeaSeq provides an accurate, reproducible, and accessible solution for routine MeV WGS analysis, supporting genomic surveillance and outbreak response workflows in public health and research settings. Impact StatementThe recent surge in measles cases worldwide, causing several countries to lose their measles elimination status, underscores the urgent need for effective and accessible genomic surveillance. Our manuscript introduces MeaSeq, a comprehensive and open-source bioinformatics pipeline specifically designed for analyzing MeV NGS data. MeaSeq includes MeV specific analyses such as genotype prediction from sequencing reads with optional genotype-specific reference selection; DSId assignment; quality control checks such as genome rule-of-six divisibility and gene CDS validation; subconsensus nucleotide analysis with mixed-site highlighting; and genomic plotting. By leveraging NGS technology, our pipeline can facilitate the identification of transmission chains and may provide critical insights into the dynamics of MeV outbreaks. This information is essential for public health officials and researchers to implement targeted interventions and optimize vaccine strategies. Additionally, the open-source nature of MeaSeq fosters collaboration and innovation within the scientific measles community along with providing access to a wider range of researchers. Data SummaryThe MeaSeq pipeline code is available on GitHub (https://github.com/phac-nml/measeq). Comparative datasets of publicly available WGS data were accessed through the NCBI Sequence Read Archive under the following BioProjects: PRJNA869081 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA869081) PRJNA480551 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA480551) PRJNA1017431 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1017431) PRJNA1241325 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1241325) PRJNA1174053 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1174053) PRJNA1293457 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1293457) PRJNA843031 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA843031) Whole-genome sequences were included in the validation analysis if they consisted of paired-end data (Illumina) and achieved [&ge;]95% genome completeness following trimming of the 5' and 3' untranslated regions (UTRs). This criterion ensured sufficient genome coverage for robust validation while allowing for limited missing data arising from regions of low sequencing depth or amplicon dropout. A complete list of sequences included in the validation, along with their accession numbers, is provided in Supplementary Table 1.

Spatial transcriptomic studies of infectious diseases still rely on fragmented data analysis processes. Here, we developed STID, a standardized framework for spatial transcriptomic analysis of infectious diseases that leverages the Seurat ecosystem and incorporates Python-based modules. STID provides an extensible infection-specific data structure and supports a full suite of analyses, such as pathogen background correction, infection-associated spot and niche identification, single-sample niche characterization, and multi-sample comparative and temporal analyses. Moreover, STID is broadly applicable to spatial transcriptomic data from infectious diseases caused by bacteria, viruses, and parasites, and enables systematic characterization of the structural features, cellular composition, molecular functions, and host-pathogen interactions within pathogen-infected and/or host-responsive niches. Overall, STID provides an accessible, reproducible, and extensible framework for analyzing infection-associated spatial transcriptomic data and for dissecting host-pathogen interactions in their native spatial microenvironments. MotivationSpatial transcriptomics technologies have emerged as powerful approaches for dissecting the structural and functional features of spatial microenvironments. However, the current general-purpose tools remain fundamentally inadequate for resolving the spatial heterogeneity of infectious disease samples, where the intricacies of host-pathogen interactions render spatial microenvironments both challenging to dissect and largely inaccessible. Tools tailored to infectious diseases are critically lacking, including those for reducing pathogen-derived background noise, identifying and isolating infection{square}associated spots or niches, dissecting host-pathogen interactions, and supporting systematic multi-sample analyses. We therefore developed STID, a unified framework that integrates standardized workflows and addresses the analytical bottlenecks in spatial transcriptomic analysis of infectious diseases. HighlightsO_LISTID standardizes spatial transcriptomic analysis in infectious diseases C_LIO_LISTID improves pathogen-infected spot detection by correcting pathogen background C_LIO_LISTID distinguishes pathogen-infected and host-responsive niches C_LIO_LISTID supports multi-sample comparative and temporal analyses of niches C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/727492v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@167d351org.highwire.dtl.DTLVardef@1628848org.highwire.dtl.DTLVardef@1e157aforg.highwire.dtl.DTLVardef@143ca1b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Transcriptome assembly enables the recovery of expressed genes and isoforms, but the optimal strategy for reconstructing transcriptomes from long-read sequencing remains unresolved. In particular, establishing best practices for generating accurate gene models and selecting representative isoforms is essential for comparative genomics, as for orthology inference typically only the longest isoform per gene model is included. Here, we systematically compare clustering and de novo assembly methods using PacBio Iso-Seq data from 17 animal lineages spanning seven phyla, most of them non-model species, with the goal of investigating which methodology is more adequate to select one isoform per gene model, in the absence of specific pipelines to do so. We evaluate four approaches: isoseq cluster, CD-HIT, RNA-Bloom2 and isONform. We benchmark them with short-reads using Trinity, assessing assembly quality with BUSCO completeness, short-read mapping rates, coding sequence recovery, and longest isoform prediction. Our results show that CD-HIT clustering at high similarity thresholds ([&ge;]99%) yields the most complete and coding-rich long-read transcriptomes, rivaling Trinity while avoiding its high redundancy. Consensus-based methods such as isoseq cluster and isONform recover fewer single-copy orthologs (mirrored in a lower BUSCO score) and achieve lower mapping rates, while RNA-Bloom2 provide intermediate performance with reduced duplication. Together, these findings establish, to date, CD-HIT as a robust and practical strategy for transcriptome reconstruction from long-read data when genomic references are unavailable. By benchmarking de novo methods across a taxonomically broad dataset, this work defines the realistic capabilities of long-read transcriptome reconstruction in the absence of a reference genome and provides practical guidance for deriving high-quality gene models and selecting representative isoforms for orthology inference in non-model species.