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At least 19 recordsLinked to original sources
PubMed2025

mettannotator: a comprehensive and scalable Nextflow annotation pipeline for prokaryotic assemblies.

SUMMARY: In recent years, there has been a surge in prokaryotic genome assemblies, coming from both isolated organisms and environmental samples. These assemblies often include novel species that are poorly represented in reference databases creating a need for a tool that can annotate both well-described and novel taxa, and can run at scale. Here, we present mettannotator-a comprehensive, scalable Nextflow pipeline for prokaryotic genome annotation that identifies coding and noncoding regions, predicts protein functions, including antimicrobial resistance, and delineates gene clusters. The pipeline summarizes these results in a GFF (General Feature Format) file that can be easily utilized in downstream analysis or visualized using common genome browsers. Here, we show how it works on 200 genomes from 29 prokaryotic phyla, including isolate genomes and known and novel metagenome-assembled genomes, and present metrics on its performance in comparison to other tools. AVAILABILITY AND IMPLEMENTATION: The pipeline is written in Nextflow and Python and published under an open source Apache 2.0 licence. Instructions and source code can be accessed at https://github.com/EBI-Metagenomics/mettannotator. The pipeline is also available on WorkflowHub: https://workflowhub.eu/workflows/1069.

Software↗
PubMed2026

NextLongIso: a comprehensive Nextflow pipeline for multi-dimensional long-read RNA-seq analysis.

SUMMARY: Long-read RNA sequencing technologies, including Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), enable direct characterization of full-length transcripts and transcriptome complexity. However, analysis of long-read RNA-seq data remains fragmented across multiple tools, limiting the ability to obtain a unified view of transcript structure, expression, and regulatory variation in long-read transcriptomes. We present NextLongIso, a scalable and reproducible Nextflow pipeline that enables coordinated analysis of multiple layers of transcript regulation. Rather than focusing solely on transcript reconstruction, NextLongIso integrates transcript discovery with downstream regulatory analyses to jointly characterize alternative splicing, isoform switching, transcript boundary dynamics (including alternative promoters and polyadenylation), and transposable element-associated transcription from both PacBio and ONT datasets. By eliminating complex cross-tool data harmonization, this unified framework facilitates the transition from transcript identification to functional interpretation of transcriptomic variation. AVAILABILITY AND IMPLEMENTATION: NextLongIso is implemented in Nextflow and is freely available at github: https://github.com/YidanSunResearchLab/nf-LongIso.git and Zenodo: https://doi.org/10.5281/zenodo.21049837.

Software↗
PubMed2026

Reducing haystacks to needles - ViralClust: A Nextflow pipeline to cluster viral sequences.

BACKGROUND: The rapid accumulation of viral genome sequences presents major challenges for downstream analysis tools, including tools for multiple sequence alignments, phylogeny, and genome/alignment visualization, due to computational constraints and sampling biases caused by outbreak-driven over-representation. Selecting representative genomes through clustering offers a principled alternative to random subsampling, yet choosing appropriate clustering strategies remains non-trivial and context-dependent. RESULTS: Here, we present ViralClust, a modular Nextflow pipeline for bias-aware representative selection from large viral genome datasets. ViralClust integrates five distinct clustering algorithms (CD-HIT-EST, SUMACLUST, VSEARCH, MMSeqs2, and HDBSCAN) within a unified workflow, enabling direct comparison of clustering outcomes and flexible adaptation to diverse biological questions, considering a balanced phylogenetic distribution of the selected sequences. We evaluated ViralClust on six RNA and DNA virus datasets ranging from 632 to 156,586 sequences and spanning genome lengths from 890 to 197,185 nucleotides. Across all datasets, clustering reduced dataset size by ~95 % or more while preserving genetic diversity across species, genera, and families, and effectively mitigating biases introduced by outbreaks, partial genomes, and sequence orientation artifacts. CONCLUSIONS: By supporting whole-genome clustering and scalable representative selection, ViralClust enables efficient and reproducible downstream analyses that would otherwise be computationally infeasible. Rather than offering a prescriptive, guided analysis engine, our framework functions as a flexible comparative collection of complementary strategies, allowing users to empirically evaluate trade-offs and choose the ideal method tailored to their specific analytical endpoints.

Bioinformatics↗
PubMed2025

HI-FEVER: a Nextflow pipeline for the high-throughput discovery and annotation of endogenous viral elements.

SUMMARY: Endogenous viral elements (EVEs) offer valuable insights into virus and host evolution, but their detection remains computationally and biologically challenging. We present HI-FEVER, a user-friendly Nextflow pipeline for the discovery of EVEs in eukaryotic host genomes. HI-FEVER is highly parallelizable and customizable, ensuring computational efficiency while allowing researchers to fine-tune parameters to their specific needs. Its output provides a comprehensive analysis of discovered EVEs, including detailed annotations which can provide evolutionary insights. HI-FEVER scales seamlessly to handle millions of viral protein queries across multiple host genomes on both laptops and high-performance computing nodes. AVAILABILITY AND IMPLEMENTATION: The HI-FEVER source code is available on GitHub at https://github.com/PaleovirologyLab/hi-fever. Minimal reference databases, test datasets and benchmarking results are hosted on the Open Science Framework at https://osf.io/y357r. A detailed wiki is available at https://github.com/PaleovirologyLab/hi-fever/wiki, including usage instructions, parameter descriptions, and guidance on interpreting outputs. The pipeline includes a Pixi environment compatible with Conda and Apptainer containerization, and Docker images. HI-FEVER has been tested on Linux, Windows (via WSL2), and macOS (Intel and ARM64).

Software↗
PubMed2026

Dogme: a nextflow pipeline for reprocessing nanopore RNA and DNA modifications.

MOTIVATION: Oxford Nanopore (ONT) sequencing allows for the direct detection of RNA and DNA modifications from unamplified nucleic acids, which is a significant advantage over other platforms. However, the rapid updates to ONT basecalling models and the evolving landscape of computational tools for modification detection bring about challenges for reproducible and standardized analyses. To address these challenges, we developed Dogme to automate basecalling, alignment, modification detection, and transcript quantification. Dogme automates the reprocessing of ONT POD5 files by integrating basecalling using Dorado, read mapping using minimap2 and subsequent analysis steps such as running modkit. The pipeline supports three major types of sequencing data-direct RNA (dRNA), complementary DNA (cDNA), and genomic DNA (gDNA). Dogme facilitates detection of diverse RNA modifications supported by Dorado such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), inosine, pseudouridine, 2'-O-methylation (Nm) and DNA methylation, while concurrently quantifying full-length transcript isoforms LR-Kallisto for transcript quantification for dRNA and cDNA. RESULTS: We applied Dogme to three separate mouse C2C12 myoblast replicates using direct RNA sequencing on MinION flow cells. We detected 96 603 m6A, 43 476 m5C, 8829 inosine, 10 055 pseudouridine, and 30 320 Nm sites in three biological replicates. The pipeline produced reproducible modification profiles and transcript expression levels across replicates, demonstrating its utility for integrative long-read transcriptomic and epigenomic analyses. AVAILABILITY AND IMPLEMENTATION: Dogme is implemented in Nextflow and is freely available under the MIT license at https://github.com/mortazavilab/dogme, with documentation provided for installation and usage.

RNA↗
PubMed2026

Nallo: a Nextflow pipeline for comprehensive human long-read genome analysis.

MOTIVATION: Long-read sequencing (LRS) is increasingly used for human medical research and clinical diagnostics due to its capacity to generate complete genome information. However, there is a lack of robust and easy-to-use pipelines for comprehensive LRS data analysis. RESULTS: Here we present Nallo, a Nextflow pipeline for analysis of PacBio and Oxford Nanopore data, with additional support for rare disease research projects. The pipeline detects a wide range of genetic variants, performs genome assembly, and reports CpG methylation. It also enables annotation and ranking of variants based on their predicted functional consequences. AVAILABILITY AND IMPLEMENTATION: Nallo is available from GitHub: https://github.com/genomic-medicine-sweden/nallo.

Humans↗
PubMed2026

Tractor workflow: a scalable Nextflow framework for local ancestry-aware genome-wide association studies.

MOTIVATION: The routine exclusion of admixed individuals from traditional genome-wide association studies (GWAS) due to concerns about spurious associations has limited multi-ancestry genetic discovery. Tractor addresses this issue by incorporating local ancestry into association testing, enabling the identification of ancestry-enriched signals and generating ancestry-specific summary statistics. However, adoption has been constrained by the complexity of prerequisite steps, including phasing and local ancestry inference, which require substantial bioinformatics expertise and introduce key analytical decision points. RESULTS: We developed a scalable, automated Nextflow workflow that integrates phasing, local ancestry inference, and Tractor association testing into a reproducible end-to-end pipeline. To demonstrate its utility, we applied the workflow to 32 blood biomarkers in 6245 two-way African-European admixed individuals from the UK Biobank. This pipeline performed efficiently at scale, replicating known associations and uncovering key ancestry-specific loci. These associations were largely driven by variants present on African ancestral tracts but absent from European tracts, underscoring the value of local ancestry-aware methods in uncovering previously masked genetic signals. AVAILABILITY AND IMPLEMENTATION: The workflow is modular, customizable, and compatible with commonly used phasing and local ancestry tools, minimizing manual intervention while preserving analytical flexibility. By lowering technical barriers to implementation, this framework facilitates broader adoption of local ancestry-aware GWAS, paving the way for expanded genetic discovery.

Humans↗
PubMed2026

dcHiChIP: a comprehensive Nextflow-based pipeline for multiscale analysis of chromatin architecture from HiChIP data.

MOTIVATION: Despite the growing use of HiChIP to investigate protein-directed chromatin architecture, a comprehensive and reproducible pipeline for analysing these datasets-from raw reads to multiscale 3D genome features-remains lacking. Existing tools often focus on isolated components, such as loop calling or matrix generation, but fall short in integrating structural annotation, functional enrichment, and spatial modeling within a unified framework. To address this gap, we developed dcHiChIP, a modular, scalable Nextflow-based workflow that streamlines the analysis of HiChIP data, enabling both routine processing and in-depth exploration of chromatin organization and regulatory interactions. RESULTS: dcHiChIP enables robust and reproducible analysis of HiChIP datasets across multiple scales of chromatin architecture. It accepts raw sequencing data as input and generates high-quality loop calls, domain annotations, and 3D genome models. It also performs functional annotation and motif enrichment analyses. Applied to benchmark CTCF HiChIP datasets, dcHiChIP identifies major chromatin architectural features such as TADs/CCDs, A/B compartments, and chromatin stripes, and offers efficient, end-to-end execution with support for batch processing and workflow resumability. AVAILABILITY: dcHiChIP is publicly available on GitHub at https://github.com/SFGLab/dcHiChIP, with documentation at https://sfglab.github.io/dcHiChIP/. The software version used in this study is archived at Zenodo: https://doi.org/10.5281/zenodo.22030542.

Chromatin↗
PubMed2025

Tractor Workflow Pipeline: A Scalable Nextflow Framework for Local Ancestry-Aware Genome-Wide Association Studies.

The routine exclusion of admixed individuals from traditional Genome-Wide Association Studies (GWAS) due to concerns about spurious associations has hindered genetic analyses involving multiple ancestries. Tractor GWAS addresses this issue by incorporating local ancestry into its analysis, empowering identification of ancestry-enriched hits and generating ancestry-specific summary statistics. However, Tractor requires accurate genomic phasing and local ancestry inference as prerequisite steps, which requires additional bioinformatics expertise and decision points regarding reference panel setup. To streamline, harmonize, and automate this process, we present a scalable Nextflow workflow that integrates all necessary steps, minimizing the need for manual intervention while remaining modular and customizable. The workflow supports multiple commonly used tools and offers flexibility in how Tractor is implemented. To demonstrate its utility, we applied this pipeline to analyze 32 blood biomarkers in 6,245 two-way AFR-EUR admixed individuals from the UK Biobank. This pipeline ran efficiently at scale, replicated known associations, and identified novel ancestry-specific loci. These novel associations were largely driven by variants present on African ancestral tracts but absent from European tracts, underscoring the value of local ancestry-aware methods in uncovering previously missed genetic signals. By enabling the efficient analysis of admixed individuals, our workflow facilitates Tractor use, paving the way for more broader genetic discovery.

Journal Article↗
PubMed2026

CoSAG-nf: A Scalable Nextflow Pipeline for Co-assembly, Optimization, and Interactive Visualization of High-Throughput Single-Cell Genomes.

MOTIVATION: Single-cell amplified genomes (SAGs) are crucial for resolving intra-population microbial heterogeneity and accurately understanding the metabolic potential of microbial dark matter populations. However, SAGs generated through multiple displacement amplification (MDA) of genomic DNA from single cells with single-copy chromosomes are highly fragmented and prone to contamination, severely hindering high-quality genome reconstruction and functional analysis, which greatly limits their scientific utility. Co-assembly of related SAGs can substantially improve genome quality, but to our knowledge no automated pipeline exists for high-throughput processing, forcing manual implementation of complex workflows that scale poorly to modern dataset sizes. RESULTS: We present CoSAG-nf, an automated high-throughput co-assembly and optimization pipeline for SAGs, implemented following the nf-core framework standards. The pipeline performs alignment-free clustering using sourmash MinHash signatures, then employs iterative tetranucleotide frequency profiling to identify and exclude outlier SAGs from co-assembly groups. CheckM2 quality assessment guides dynamic selection of optimal SAG combinations to optimize genome completeness and minimize contamination. Fully containerized, CoSAG-nf ensures reproducibility and scalability for the high-throughput processing of large-scale SAG datasets across diverse computing environments, including HPC and cloud platforms. The pipeline generates comprehensive HTML reports with quality metrics and taxonomic annotations, providing an end-to-end solution for automated high-throughput single-cell genome reconstruction. AVAILABILITY: CoSAG-nf is freely available under the MIT License at: https://github.com/linfengxu/CoSAG-nf. Archival code repository snapshots are published at zenodo with doi: https://doi.org/10.5281/zenodo.21525244. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗
PubMed2026

Community-driven updates for comprehensive long-read metagenomics and enhanced binning in nf-core/mag v5.

SUMMARY: nf-core/mag is a reproducible Nextflow pipeline for best-practice metagenomic de novo assembly and binning within the nf-core framework. Here we present a major update that adds support for long-read-only assembly and bin refinement, includes five new binning tools, expands taxonomic classification to viruses and eukaryotes, and improves bin quality evaluation with new tools and latest databases. Through sustained community-driven development spanning seven years and four primary curator teams, nf-core/mag remains actively developed as an open source workflow for metagenomic analysis, benefiting from contributions from across the broader metagenomics, nf-core, and Nextflow ecosystem. AVAILABILITY AND IMPLEMENTATION: The source code of nf-core/mag v5 is available on GitHub (https://github.com/nf-core/mag) under the open source MIT license, with v5.5.0 source code archived on Zenodo (https://zenodo.org/records/21735731). Documentation is viewable on the nf-core website (https://nf-co.re/mag).

Metagenomics↗
PubMed2026

PMBB Geno-Pheno Toolkit: A suite of scalable, reproducible pipelines for cross-biobank association analyses.

Electronic health record (EHR)-linked biobanks generate unprecedented genomic and phenotypic datasets, but their scientific utility is constrained by data fragmentation across institutional silos and incompatible computing infrastructures, forcing researchers to rewrite ad-hoc scripts for each new environment. We present the PMBB Geno-Pheno Toolkit, a suite of modular Nextflow pipelines for biobank-scale association analyses. This note focuses on the toolkit's SAIGE family of pipelines - supporting genome-wide (GWAS), exome-wide (ExWAS), and phenome-wide (PheWAS) association testing - together with the companion GWAMA and ExWAS meta-analysis pipelines that enable cross-biobank replication. All components are containerized (Docker/Apptainer) and orchestrated with Nextflow, allowing the same workflows to run unmodified on local HPC clusters, cloud platforms, and the All of Us Research Workbench. Complementary toolkit pipelines for PLINK-based GWAS, polygenic scoring, LD-based clumping, and phenotype harmonization are also available and briefly noted.

Journal Article↗
PubMed2026

A novel reusable transcriptome-wide association study workflow used to map key genes linked to important cattle traits.

Transcriptome-wide association studies (TWAS) are a powerful approach for studying the genes underlying complex traits by directly integrating GWAS and gene expression datasets. In cattle, they have been previously applied to identify genes driving fertility, milk production, and health. However, these studies have also highlighted several challenges, from difficulties in reproducing these complex analyses to limitations from poor genotype calls, especially when called directly from RNA sequencing data. To address these and other challenges, for the H2020 BovReg Project, we have developed a streamlined, species-agnostic, and reusable Nextflow TWAS workflow to integrate transcriptomic and GWAS summary statistic datasets. Our workflow first generates accurate genotype calls and gene expression prediction models from transcriptomic datasets and then applies these tools to impute gene expression levels into GWAS cohorts, enabling the association of genes with traits of interest. We explore optimal strategies for calling genetic variants directly from transcriptomic data and illustrate that using imputation approaches specifically designed for low-pass sequencing data can improve variant calling over previously adopted methods. We demonstrate the utility of our TWAS workflow by applying it to both novel and publicly available GWAS cohorts for cattle, detecting novel gene-trait associations for complex traits. Using a new transcriptome annotation of the cattle genome generated for the BovReg project we also illustrate how previously un-assayable associations can be detected. The results and the workflow we present, provide a new resource for the community and contribute to a better understanding of the molecular drivers of complex traits in cattle with the goal of eventually leveraging this information in future breeding decisions.

Animals↗
PubMed2026

CBIcall: a configuration-driven framework for variant calling in large sequencing cohorts.

MOTIVATION: Variant calling for next-generation sequencing (NGS) data relies on a diverse ecosystem of tools and workflows. Large-scale collaborative studies increasingly adopt federated analysis, where each institution processes sensitive data locally using standardized pipelines. Deploying identical pipelines across multiple centers remains challenging because heterogeneous software environments and computing policies can cause workflow divergence and inconsistent results. RESULTS: We developed CBIcall, a workflow backend-flexible, configuration-driven framework that runs standardized variant-calling pipelines from raw FASTQ files to analysis-ready VCFs. Users define each analysis in a single YAML parameters file, which CBIcall resolves against a controlled workflow registry and resource catalog. The execution driver validates parameters and checks compatibility among pipelines, analysis modes, workflow backends, genome builds, tool versions, and resource bundles. CBIcall supports reproducibility auditing by comparing executions using recorded provenance and output fingerprints. CBIcall dispatches validated workflows natively through Bash, Cromwell, Nextflow and Snakemake backends and provides production-ready pipelines for germline WES, WGS (single-sample or cohort joint genotyping following GATK Best Practices), and mitochondrial DNA analysis. We evaluated analytical performance using public benchmark datasets and validated reproducibility across four computing environments. We further deployed CBIcall in the EU HEREDITARY project, where it processed 1102 samples with both WES and mtDNA pipelines on an institutional HPC system, supporting its suitability for reproducible cohort-scale genomic analyses. AVAILABILITY AND IMPLEMENTATION: CBIcall is open source (GPLv3) and distributed with ready-to-run pipelines; full dependency and installation documentation is available at https://github.com/CNAG-Biomedical-Informatics/cbicall.

Journal Article↗
PubMed2025

nf-UnO pipeline: A metagenomic co-assembly pipeline for novel pathogen detection from mNGS outbreak sets.

SUMMARY: nf-UnO is a pipeline implemented in Nextflow to identify novel pathogens from metagenomic shotgun sequencing of epidemiologically related foodborne outbreak specimens. nf-UnO uses MIDAS2, metagenomic co-assembly, multiple binning programs, and read mapping to metagenomically assembled genomes to detect potential etiological agents found in common across outbreak specimens. AVAILABILITY AND IMPLEMENTATION: https://github.com/uel3/nf-UnO.

Metagenomics↗
PubMed2025

scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing.

MOTIVATION: Recent advancements in long-read single-cell RNA sequencing (scRNA-seq) have facilitated the quantification of full-length transcripts and isoforms at the single-cell level. Historically, long-read data would need to be complemented with short-read single-cell data in order to overcome the higher sequencing errors to correctly identify cellular barcodes and unique molecular identifiers. Improvements in Oxford Nanopore sequencing, and development of novel computational methods have removed this requirement. Though these methods now exist, the limited availability of modular and portable workflows remains a challenge. RESULTS: Here, we present, nf-core/scnanoseq, a secondary analysis pipeline for long-read single-cell and single-nuclei RNA that delivers gene and transcript-level quantification. The scnanoseq pipeline is implemented using Nextflow and is built upon the nf-core framework, enabling portability across computational environments, scalability and reproducibility of results across pipeline runs. The nf-core/scnanoseq workflow follows best practices for analyzing single-cell and single-nuclei data, performing barcode detection and correction, genome and transcriptome read alignment, unique molecular identifier deduplication, gene and transcript quantification, and extensive quality control reporting. AVAILABILITY AND IMPLEMENTATION: The source code, and detailed documentation are freely available at https://github.com/nf-core/scnanoseq and https://nf-co.re/scnanoseq under the MIT License. Documentation for the version of nf-core/scnanoseq used for this paper, including default parameters and descriptions of output files are available at https://nf-co.re/scnanoseq/1.1.0.

Single-Cell Analysis↗
PubMed2026

CholeraSeq: a comprehensive genomic pipeline for cholera surveillance and near real-time outbreak investigation.

SUMMARY: Next Generation Sequencing is widely deployed in cholera-endemic regions, yet an end-to-end reproducible pipeline that unifies read QC, filtering, reference mapping, variant calling/annotation, recombination screening, and extraction of parsimony informative sites/variant codons, phylogenetic inference for downstream phylodynamic and epidemiological analyses have been lacking, slowing outbreak investigation and public health response. CholeraSeq is a high-throughput genomics pipeline for cholera genomic surveillance. It ingests consensus genomes, short read sequence data, draft assemblies, and scales seamlessly from local to cloud environments. To accelerate epidemiological context placement of new outbreak strains, we provide a curated ready-to-use core genome alignment compiled from public data, enabling flexible, fast, integration of new samples for outbreak investigations. AVAILABILITY AND IMPLEMENTATION: CholeraSeq is freely available on the GitHub platform https://github.com/CERI-KRISP/CholeraSeq. CholeraSeq is implemented in Nextflow with a modular design building upon the nf-core community standards.

Cholera↗
PubMed2026

Managing workflow executions with WESkit.

SUMMARY: In biomedical research, managing computational workflows across numerous projects-with varying parameters, tools, and environments-creates major challenges in scalability, reproducibility, and collaboration. Here, we present WESkit, an implementation of the Global Alliance for Genomics and Health (GA4GH) Workflow Execution Service (WES) interface, designed to streamline the execution, monitoring, and documentation of data processing workflows. It addresses the complexities involved in managing numerous executions with varying parameters across diverse research projects. Supporting both Snakemake and Nextflow, the system enables consistent automation and centralized monitoring, which benefits research groups aiming for long-term reproducibility and scalable collaboration. Its suitability for larger teams and service units is further enhanced by seamless integration into cloud environments, contributing to the GA4GH cloud framework. AVAILABILITY AND IMPLEMENTATION: The software WESkit is available under MIT license at the GitLab repository (https://gitlab.com/one-touch-pipeline/weskit). The WESkit main repository is archived at Software Heritage (https://archive.softwareheritage.org/browse/origin/directory/?origin_url=https://gitlab.com/one-touch-pipeline/weskit/api.git) and can be found using "one-touch-pipeline/weskit" term in the search section.

Workflow↗