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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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Modular metagenomic analysis of pan-domain symbioses with MAGUS

Metagenomic analysis of deeply sequenced, eukaryotic-dominant symbiotic communities can be difficult for many metagenomic workflows. Here, we present MAGUS, a bioinformatic toolkit that uses a suite of custom bioinformatic methods for iterative genome assembly and filtering of pan-domain communities, where eukaryotes, bacteria, viruses, and functionally annotated gene catalogs are resolved and analyzed over a series of interconnected, modular software components. We evaluated MAGUS using deeply sequenced (median depth: 579 million reads) ten samples of hard corals, soft corals, and hydrozoans, which comprise complex, eukaryote-dominated symbiotic communities. We successfully resolved phylogenetically comparable host (N = 10), algal (N = 6), bacterial (N = 55), and viral (N = 160,925) genomes, as well as a gene catalog comprising 15,369,684 non-redundant genes (7.6% functionally annotated). MAGUS is available on GitHub (https://github.com/two-frontiers-project/2FP_MAGUS/).

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Long-range PCR amplification and nanopore sequencing of 8-10 kb mitochondrial fragments from environmental DNA

O_LILong-read sequencing data can provide increased taxonomic resolution and genomic linkage information that is otherwise impossible to obtain from short-read amplicon or shotgun sequencing. However, the use of long-read sequencing for environmental DNA (eDNA) analysis has thus far been limited by both the apparent rarity of long DNA molecules in eDNA samples and the lack of established bioinformatics workflows for long-read metabarcoding, particularly from mixed template samples. C_LIO_LIHere, we report nanopore sequencing of 8.0 - 9.5 kb mitochondrial fragments obtained from mesocosm and field eDNA samples, amplified with long-range PCR (LR-PCR) using primers designed to preferentially amplify teleost mitogenomes. C_LIO_LIWe recovered half-mitochondria from 13 fish species in field-collected samples (Puget Sound in Washington State, USA), as well as from approximately half of the fish species inhabiting the positive control mesocosm (the Seattle Aquarium). Among biological replicates, we observed consistent detection of read-abundant species, while there was greater stochasticity in the presence of rarer species. C_LIO_LIWe demonstrate that long fragments can be obtained from standard eDNA samples and successfully amplified and sequenced to obtain species identifications despite higher error rates characteristic of nanopore sequencing. We present both laboratory methods and an accessible bioinformatic pipeline for obtaining and analyzing LR-PCR amplified fragments from eDNA, providing a framework for future long-read metabarcoding studies. C_LI

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mtDNA-Network: a web tool to explore mitochondrial variant profiles in complex diseases

The mitochondrial genome (mtDNA) provides valuable insights into human evolution, population diversity, and disease etiology. Here, we present the mtDNA-network (https://apps.lghm.ufpa.br/mtdna/), an integrative bioinformatics database and tool for the visualization and analysis of mitochondrial variants (single-nucleotide variants and insertions/deletions). The mtDNA-network was upgraded to enable investigation of mtDNA in admixed Brazilian individuals with substantial contributions from uniparental Indigenous and African ancestries. We implement a bioinformatics pipeline to harmonize variant calling across 339 mtDNA samples. The dataset supports general genetic population analysis and variant mapping for complex diseases, including Parkinsons disease (104 cases and 75 controls), leprosy (33 cases and 37 controls), and somatic gastric cancer (40 cases and 50 controls). The mtDNA-network tool features an intuitive interface and analytical dashboards for transitions, transversions, heteroplasmy, and variant-disease networks, and serves as a strategic resource for advancing research in population genetics and precision medicine in underrepresented populations. We reinforce the critical need to expand non-European genomic representation in global databases to promote more equitable genomic diversity studies and clinically relevant discoveries.

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AstraKit: Customizable, reproducible workflows for biomedical research and precision medicine

MotivationThe success of precision medicine and biomedical research depends on the availability of efficient software solutions for processing and interpreting genetic variants, interpreting multi-omics data, and integrating drug screen analyses. However, fragmented bioinformatics tools compel researchers and clinicians to resort to error-prone manual pipelines. ResultsWe present AstraKit, a unified KNIME workflow suite enabling end-to-end precision medicine analytics. AstraKit introduces three transformative innovations: 1) Dynamic variant interpretation with customizable annotation and filtering for disease-specific genomic contexts; 2) Multi-layered omics analyses integrating genomic, transcriptomic, and epigenetic data; and 3) Translational drug matching that correlates in vitro drug screens with clinical outcomes. Validated across oncology cohorts, AstraKit demonstrates concordance between experimental drug sensitivity and clinical outcomes, resolving discordances to uncover resistance mechanisms. By unifying variant analysis, multi-omics, and drug response modeling on a single customizable platform, AstraKit eliminates siloed workflows, accelerating biomarker validation and enabling clinicians to directly link molecular profiles to therapeutic decisions. As all AstraKit workflows are open-source and platform-independent, we provide a versatile comprehensive software suite for a multitude of tasks in bioinformatics and precision medicine. Availability and implementationThe KNIME workflows are available at KNIME Hub https://hub.knime.com/bioinf_goe/spaces/Public/AstraKit~lfVsGBY2HnPYc1h1/. The source code is available at https://gitlab.gwdg.de/MedBioinf/mtb/astrakit.

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PPGLomics: An Interactive Platform for Pheochromocytoma and Paraganglioma Transcriptomics

Pheochromocytoma and paraganglioma (PPGL) are rare neuroendocrine tumors with unique biological behavior and remarkably high heritability, yet dedicated bioinformatics resources for these diagnoses remain limited. Existing cancer multi-omics platforms are pan-cancer in scope, often lacking the disease-specific annotations, granularity, and cross-database harmonization required for meaningful stratification and hypothesis generation. Here we introduce PPGLomics, an interactive web-based platform designed for comprehensive PPGL transcriptomics analysis. PPGLomics v1.0 integrates two major datasets, the TCGA-PCPG cohort (n=160) spanning multiple molecular subtypes, and the A5 consortium SDHB cohort (n=91) with detailed clinicopathological and molecular annotations. The platform provides basic and clinical scientists, as well as a broad range of healthcare professionals, with tools for differential expression analysis, correlation analysis, survival analysis, and visualization, including boxplots, heatmaps, volcano plots, and Kaplan-Meier survival plots, enabling exploration of gene expression patterns across PPGL subtypes without requiring bioinformatics expertise. PPGLomics v1.0 is freely available at https://alkaissilab.shinyapps.io/PPGLomics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=51 SRC="FIGDIR/small/702561v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@3b98daorg.highwire.dtl.DTLVardef@b050f4org.highwire.dtl.DTLVardef@1930219org.highwire.dtl.DTLVardef@6edf18_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Modularity, ecology, and theoretical evolution of the ribozyme body plan

Ribozymes are relics of molecular life forms from the primitive earth that are embedded within modern genomes across all kingdoms of life. Despite significant knowledge from decades of bioinformatic and biochemical research, a gap remains in our understanding of the world in which ribozymes existed, their interactions, ecology, and possibly also evolution. The present study proposes a new theoretical basis for understanding these aspects of ribozyme biology by adopting a zoological frame of thought. Seven families of small self-cleaving ribozymes are each mapped to a primitive marine animal analog based on topological architecture, and classified into body plan grades paralleling cnidarian, ctenophore, and bilaterian organization. A formal notation describing ribozyme regions as bodies, cavities, and limbs enables systematic comparison with animal body plans and highlights reusability of parts across ribozyme groups, in turn enabling the construction of a connectivity network and a putative body plan-based evolutionary ordering. This ordering of body plans identifies systematic gaps corresponding to undiscovered ribozyme forms, one of which, a planktonic form of hammerhead, was bioinformatically found in 16.2% of all hammerhead sequences. Computational cross-cleavage analysis across all 49 pairwise interactions (including conspecific) suggests that the hammerhead was a generalist apex predator in the RNA world, while the hatchet was a vulnerable, filter-feeding or scavenger prey species. Conspecific analysis suggests that cannibalism was also a prevalent feeding strategy. Evolutionary avoidance signatures suggest ancient predator-prey coevolution. This theory emphasizes behavior, modularity, and ecological interactions as primary drivers of early ribozyme evolution, offering a new pathway for inferring ancient RNA forms independent of sequence-first assumptions.

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Integrated Artificial Intelligence and Quantum Chemistry Approach for the Rational Design of Novel Antibacterial Agents against Ralstonia solanacearum.

Antimicrobial resistance (AMR) in plant pathogenic bacteria poses a serious threat to global agriculture, necessitating the development of novel antibacterial agents targeting virulence mechanisms. This study presents an integrated bioinformatics-driven framework for the rational design and computational validation of Solres, a newly designed small molecule targeting key virulence proteins in phytopathogenic bacteria. Approximately 10,000 active compounds from PubChem BioAssay (AID: 588726) were analyzed using structural clustering and scaffold mining to identify conserved molecular motifs associated with antibacterial activity. Guided by high-frequency substructures, Solres was designed de novo and screened for structural novelty against PubChem, ChEMBL, and WIPO databases. Drug-likeness evaluation using Lipinskis Rule of Five confirmed favorable physicochemical properties. Molecular docking was performed against essential virulence regulators, including PhcA, PhcR, HrpB, PehA, and Egl from Ralstonia solanacearum and Xanthomonas spp., with active sites predicted using CaspFold. Docking analyses revealed strong binding affinities and stable interactions with key catalytic and regulatory residues. Complex stability and conformational integrity were further validated through molecular dynamics simulations. Quantum chemical descriptors, including HOMO-LUMO energy gap and dipole moment, supported the electronic suitability and reactivity profile of Solres. Collectively, this study demonstrates the effective integration of cheminformatics, structural bioinformatics, molecular simulations, and quantum chemical analyses for plant-focused antibacterial discovery. The compound Solres represents a promising lead candidate for mitigating bacterial wilt disease and provides a computational framework for future experimental validation and sustainable crop protection strategies against AMR-driven phytopathogens.

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ISdetector: precise mapping of insertion sequences and associated structural variations from short-read sequencing data

MotivationInsertion sequences (ISs) are key drivers of genomic plasticity in bacteria and archaea. Determining their exact insertion coordinates is critical for understanding drug resistance, virulence, and pathogen epidemiology. However, accurately mapping ISs from high-throughput short-read sequencing data remains challenging due to the repetitive nature of these elements and accompanying structural variations, which frequently confound standard alignment-based algorithms. As whole-genome sequencing becomes the standard for population-level studies, there is a need for robust, scalable, and specialized pipelines to detect ISs. ResultsWe present ISdetector, a bioinformatics pipeline that detects precise insertion sites of specific ISs using an IS-clean reference strategy combined with clustering of IS-relevant signals from soft-clipped reads. Compared with existing tools, including ISMapper and MGEFinder, ISdetector demonstrates higher accuracy and robustness, achieving high F1 scores in both high-GC-content genomes (e.g., Mycobacterium tuberculosis, F1=0.91) and high-IS-burden genomes (e.g., Shigella sonnei, F1=0.85). Furthermore, ISdetector identifies IS movements accompanied by structural variations, such as large-scale deletions, which are often missed by existing methods. Implemented with multi-threading, ISdetector shows near-linear decreases in running time with increasing thread counts, making it highly scalable and efficient for processing large numbers of samples in population-level studies. AvailabilityISdetector is an open-source pipeline implemented in Python. It integrates standard bioinformatics tools, including BWA, SAMtools, and BLAST+, and uses the Biopython and Pysam libraries for data processing. The source code, documentation, and usage instructions are freely available at https://github.com/carolynzy/ISdetector. CONTACTZHOUYANG@CHINACDC.CN

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TattleTail: A Pyocin Prediction Tool

Tailocins are phage tail-like bacteriocins (PTLBs) thought to be remnants of prophages that have lost the ability to package viral genomes while retaining the ability to kill closely-related bacterial strains, thereby mediating bacterial competition. Tailocins produced by Pseudomonas aeruginosa are referred to as pyocins. Apart from their contribution to ecological fitness, they also have the potential to be harnessed as highly-specific antimicrobials to treat antibiotic resistant bacterial infections. Although pyocins lack the genetic components to package viral genomes, pyocin-encoding gene clusters share a high degree of genetic homology to phage tail genes, attributed to their shared ancestry. This poses a significant annotation-based challenge, as current prophage prediction tools, which rely on phage homology for prediction, can misclassify pyocins or tailocins as prophages. Pyocins unknowingly being misannotated as prophages is not only a bioinformatic issue, but can certainly confound experiments examining bacterial competition and prophage induction, if the experimental setup is based on this unintentional misannotation. In this study, we present "TattleTail", the first version of a bioinformatic tool designed to accurately identify tailocins in genome sequences, with a focus on identifying phage tail-derived pyocin-encoding gene clusters in P. aeruginosa in its first iteration. The tool leverages conserved pyocin gene cluster markers and accounts for the absence of canonical phage features, such as capsid, terminase and integrase genes, thereby distinguishing pyocins from intact and cryptic prophages. Validation in P. aeruginosa and non-P. aeruginosa genomes confirmed the presence of pyocin regions in all P. aeruginosa genomes, while none were detected in any non-P. aeruginosa genomes. Notably, TattleTail enabled the identification of representative pyocin-encoding gene clusters in clinical P. aeruginosa isolates. The identified pyocins in the clinical isolates were induced using mitomycin C, visualized via transmission electron microscopy, processed via tangential flow filtration and demonstrated bactericidal activity, thereby confirming TattleTail predictions. TattleTail aims to complement existing prophage prediction tools during genomic analyses involving phage-derived elements in bacterial genomes, allowing more accurate identification of these elements facilitated by robust discrimination between prophages and tailocins.

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Claw4Science: A Dataset and Platform for the OpenClaw Scientific Agent Ecosystem

Large language models have enabled a new class of scientific software in the form of AI agents that can execute research workflows across bioinformatics, drug discovery, and related domains. Among these systems, OpenClaw introduced a skill-based design that allows workflows to be expressed as structured Markdown files, lowering the barrier to contribution and enabling rapid ecosystem growth. However, this growth has led to fragmentation. Projects are distributed across independent repositories, skills vary widely in quality, naming is inconsistent, and there is no unified way to discover or compare available tools. In this work, we construct the first curated dataset of the OpenClaw scientific ecosystem. The dataset includes 91 projects organized by functional role and 2,230 skills spanning 34 scientific categories. Based on this dataset, we perform a systematic analysis of the structure, distribution, and emerging patterns of scientific agent development. To make this ecosystem accessible in practice, we further build Claw4Science, a public platform at https://claw4science.org, which is built on top of our dataset. The platform organizes projects and aggregates distributed skill repositories into a unified interface, with a focus on bioinformatics and scientific workflows, providing a practical entry point for navigating the ecosystem. Our results show that the OpenClaw ecosystem reflects a shift from isolated systems to a more modular and shareable model of scientific computation. At the same time, challenges in evaluation, reproducibility, and governance remain open. We argue that our dataset provides a foundation for future benchmark development and standardized infrastructure for scientific AI agents.

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REBEL, Reproducible Environment Builder for Explicit Library resolution

BackgroundAchieving FAIR-compliant computational research in bioinformatics is systematically undermined by two compounding challenges that existing tools leave unresolved: long-term reproducibility and accessibility. Standard package managers re-download dependencies from live repositories at every build, making environments vulnerable to library disappearance and version drift, and pinning a package version does not pin the versions of its transitive dependencies, causing divergences between builds performed at different points in time. Compounding this, packages from repositories such as CRAN, Bioconductor, and PyPI frequently omit critical system-level dependencies from their installation metadata, leaving users to manually discover which underlying library is missing or which version is required. Beyond these technical failures, constructing a truly reproducible environment demands expertise in containerization making reproducibility in practice a privilege and not a standard. FindingsWe present REBEL (Reproducible Environment Builder for Explicit Library Resolution), a framework that addresses both challenges through three dependency inference heuristics: (i) Deep Inspection of source code, (ii) Fuzzy Matching against a manually curated knowledge base, and (iii) Conservative Dependency Locking. The resolved dependency stack is then archived into a self-contained local store, enabling offline and deterministic rebuilds at any future time. We compared the installation of 1,000 randomly sampled CRAN packages in isolated Docker containers versus the standard package manager and REBEL resolved 149 of 328 standard installation failures (45.4%). Moreover through its DockerBuilder component, REBEL further generates fully reproducible Docker images from a plain text requirements file, making deterministic environment construction accessible without expertise in containerization. ConclusionsREBEL provides a practical foundation for FAIR-compliant, long-term reproducible bioinformatics analyses, making deterministic environment construction accessible to researchers regardless of their technical background. REBEL is freely available at https://github.com/Rebel-Project-Core

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DNAharvester: A Nextflow Pipeline for Analysing Highly Degraded DNA from Ancient and Historical Specimens

Ancient DNA (aDNA) research has advanced rapidly with the development of high-throughput sequencing, enabling genome-wide analyses of large collections of prehistoric specimens. However, analysing palaeontological and archaeological material with highly degraded DNA constitutes a major bioinformatic challenge. DNA from such samples is characterised by short fragment lengths, low endogenous content, post-mortem damage, and cross-species contamination, which can increase spurious mapping and reference bias, affecting downstream population genetic inferences. We present DNAharvester, a modular and reproducible pipeline designed specifically for processing highly degraded DNA from ancient and historical specimens. DNAharvester integrates metagenomic filtering, competitive mapping, adaptive aligner selection (incorporating BWA-aln, BWA-mem, and Bowtie2), and systematic evaluation of reference bias and spurious mapping. By incorporating flexible mapping and filtering strategies, the pipeline can be adapted to varying sample preservation, focusing on maximising authentic data recovery. DNAharvester features subworkflows for iterative assembly of mitogenomes, identification of genomic repeats and CpG sites, taxonomic classification, microbial/pathogen screening, genetic sex determination, and variant calling. To accommodate varying sequencing depths, the pipeline supports diploid variant calling, genotype likelihood estimation, and pseudo-haploid random allele calling. Implemented in Nextflow, DNAharvester provides a highly scalable, containerised framework that enhances reproducibility, portability, and robustness in aDNA analyses. We validated the pipeline using simulated and empirical datasets, demonstrating its ability to systematically mitigate complex background contamination while preserving authentic genomic signals. By streamlining complex bioinformatic tasks through simple configuration files, DNAharvester establishes a standardised approach for analysing aDNA datasets and makes genomic analyses of ancient remains accessible to the broader research community.

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SNPWay: streamlined SNP-to-function and pathway over-representation analysis

MotivationPost-GWAS interpretation frequently requires translating variant lists (e.g., lead SNPs, clumped loci, credible sets, or curated panels) into pathway and functional hypotheses. In practice, obtaining pathway and functional over-representation results from SNP inputs often requires stitching together multiple tools for variant annotation, regulatory annotation, gene identifier handling, and statistical testing. This integration burden can reduce reproducibility and restrict end-to-end analysis to groups with dedicated bioinformatics support. SummaryWe present SNPWay, a web server and R package that performs end-to-end SNP-to-function and pathway over-representation analysis in a single standardized workflow. SNPWay accepts rsIDs, VCF files, or hg19/GRCh37 genomic coordinates. It queries Annotation Query (AnnoQ) to obtain SNP-to-gene mappings from ANNOVAR, SnpEff, and VEP under both Ensembl and RefSeq gene models, and incorporates enhancer-gene links via PEREGRINE to augment mappings for noncoding variants. SNPWay aggregates mapped genes into a single, non-redundant, combined gene list and submits it to PANTHER for over-representation testing against the Homo sapiens reference list, returning over-represented pathways and functional categories (e.g., Gene Ontology) with direct links for interactive exploration in PANTHER. SNPWays modular architecture is designed for extensibility, enabling incorporation of additional analysis methods in future releases. A step-by-step walkthrough is provided in Supplementary Data. Availability and implementationWeb server: https://snpway.annoq.org/. R package and source code: https://github.com/USCbiostats/Annoq_Overrepr_Workflow. Documentation: https://snpway.annoq.org/about. Examples: The website contains Sample files for the input formats, also provided in Supplementary Data. SNPWay is free to use with no mandatory login. Contacthuaiyumi@usc.edu Supplementary informationSupplementary data are available at Bioinformatics online

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Lense: Optimizing data preprocessing in single-cell omics using LLMs

Data preprocessing is critical for single-cell omics analyses, but default pipelines often underperform on diverse datasets, especially from emerging platforms like spatial transcriptomics. We introduce Lense, a language-model-guided method that automatically selects optimal preprocessing by comparing plots that visualize low-dimensional representations across pipeline variants. Integrated with Seurat, Lense streamlines analysis and improves preprocessing robustness without requiring manual tuning. Biographical NoteJingyun Liu is a Masters student in the Department of Biostatistics and Bioinformatics at Duke University. Dr. Zhicheng Ji is a tenure-track Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke University. His research focuses on artificial intelligence and statistical modeling for single-cell genomics, spatial genomics, and biomedical imaging.

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A novel vaccine and drug targets for global eradication of bovine tuberculosis: Holistic frameworks for construction of a potent vaccine and identification of drug targets

Bovine tuberculosis (TB), caused by Mycobacterium bovis, has become a global concern over the last two decades. Bovine TB primarily affects cattle, but other domestic livestock are also affected and it is more common in less developed and developing countries. The significant loss of livestock leads to trade restrictions and economic crises. Zoonotic potential of bovine TB raises health concerns for the public. Currently, no effective treatment is available and animal slaughtering is usually undertaken to reduce the burden of it in the environment. Antibiotic therapy can be used on animals living in captivity, but it is not reliable for herd or free-grazing animals. The BCG vaccine is another option available for treating the disease, but it shows limited efficacy in cattle. The prevention of bovine TB is a long-term goal that can only be accomplished by developing a more effective vaccine than BCG and designing new drugs. In this research, we propose therapeutic drug targets and vaccine for treating bovine TB. The conceptual framework for vaccine developed in this study uses a number of bioinformatics approaches to identify potential vaccine candidates and construct an in-silico epitope-based vaccine. Our holistic framework identified potential therapeutic candidates by directly analysing the proteome of TB bacterial strains. Specifically, we performed a comparative proteomic analysis of 11 Mycobacterium bovis strains to cover the diversity and identify conserved proteins among those strains for developing the bovine TB vaccine. An extensive reverse vaccinology and immunoinformatics analysis provided 26 highly immunogenic, non-toxic and non-allergenic epitopes (CTL epitopes-8, HTL epitopes-2 and B-cell epitopes-16) for Mycobacterium bovis required for three-dimensional structure construction of TB vaccine. The constructed epitope-based vaccine showed a potent interaction inside the host, thus generating efficient cell-mediated and humoral immune responses. Next, a framework based on a novel subtractive proteomic approach was developed for identifying bovine TB drug targets. We performed this approach on the 11 Mycobacterium bovis strains and identified nine drug targets that are conserved, essential, antigenic and have unique metabolic pathways in Mycobacterium bovis. These drug targets could further help investigate therapeutic drugs for the treatment of bovine TB. Several bioinformatics prediction tools were used together to ensure checks and balances, aiming to reduce the chance of errors and provide accurate results. The vaccine and drug targets developed in this study can be tested experimentally with confidence for further validation as therapeutics with the potential to eradicate bovine TB globally. The strategies implemented in the study are generic and can be used for other zoonotic infectious diseases. This study would be a game changer in the field of bovine tuberculosis treatment.

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Evaluation of MeaSeq: comprehensive analysis and reporting of measles virus whole genome sequences.

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 [≥]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.

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GenesetDiseaseDrugNetwork (GDDN): a web server for disease enrichment and drug prioritization

SummaryOmics technologies profile thousands of genetic and molecular features to provide a comprehensive and quantitative measure of the cellular state. Transcriptomics and proteomics have, especially, guided discoveries of the most important biomarkers and therapeutic targets. By virtue of ongoing developments in single-cell and spatial technologies, fields of targeted therapeutics and personalized medicine are rapidly advancing. However, downstream functional analysis and disease association still remain daunting tasks in bioinformatics. We address these challenges with the GenesetDiseaseDrugNetwork (GDDN) web server. GDDN facilitates functional discovery by connecting gene-sets to enriched diseases and their corresponding therapeutics in a single step. Using a ranking system that incorporates regulatory impact, specificity, and potency, GDDN effectively prioritizes drugs with the highest clinical relevance. Our platform facilitates the interpretation of omics outputs into disease associations and personalized drug identification. Availability and ImplementationThe GDDN web server is implemented in R Shiny and is freely accessible at https://cbdm-01.zdv.uni-mainz.de/shiny/piyusmor/GDDN/, supporting all major web browsers. Contactpiyusmor@uni-mainz.de Supplementary informationSupplementary data is available at Bioinformatics online.

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gTranslate: rapid and accurate translation table prediction for prokaryotic genomes

BackgroundBioinformatic tools often require the prediction of protein-coding genes to make inferences about prokaryotic genomes. Typically, the genetic code used for translating genes to proteins must be specified by the user based on the taxonomic classification of a genome assembly or, for some widely used tools, established using a heuristic rule based on gene coding densities. Manual specification is at best inconvenient, but more challenging is that many bioinformatic tools are applied before taxonomic classifications have been established making specifying the translation table impractical. MethodsHere we provide a computationally efficient tool, gTranslate, that uses an ensemble of five machine learning methods to accurately predict translation tables for prokaryotic genomes. The feature vector used by gTranslate takes advantage of differences in gene coding densities when predicting genes under different translation tables along with features that consider the number and ratio of UGA stop codon reassignments to tryptophan or glycine. ResultsWe demonstrate that gTranslate correctly predicts the translation table of prokaryotic genomes >99.99% of the time (i.e. <1 error per 10,000 genomes) and outperforms a more computationally expensive prediction method and a coding density heuristic used by popular bioinformatic tools. Using gTranslate, we identify a basal lineage of Ca. Stammera capleta that uses the standard bacterial genetic code instead of the UGA stop codon to tryptophan reassignment common to other members of this species. We also identify the first instances of UGA-to-tryptophan reassignment in the Patescibacteriota making this the first bacterial phylum with members capable of using translation tables 4, 11, and 25.

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