1// Pre-loaded objects with their attributes 2const preLoadedObjects = [ 3 { 4 id: "pipeline1", 5 name: "ancient DNA", 6 attributes: { 7 readTypes: ["Short reads"], 8 multiSample: ["No"], 9 coAssemblyCoBinning: ["No"], 10 GUI: ["No"], 11 Cloud: ["No"], 12 workflowManager: [], 13 binRefinement: ["Yes"], 14 externalComputationalResources: ["No"], 15 executionOptions: [], 16 specialOptions: ["Ancient DNA identification"], 17 update: ["2024"], 18 license: [], 19 category: ["Special"], 20 citations: ["0"] 21 }, 22 description: "Pipeline tailored for reconstructing MAGs from ancient metagenomic DNA. It accounts for DNA damage, fragmentation, and contamination, enabling reliable recovery and authentication of ancient microbial genomes. Optimized for degraded samples, it supports damage profiling and taxonomic validation, making it ideal for paleogenomic microbiome studies.", 23 url: "https://www.biorxiv.org/content/10.1101/2024.09.18.613623v2.full", 24 details: "MAG recovery from ancient DNA can be challenging due to DNA intrinsic properties such as degradation, fragmentation, chemical damage, low-abundance and contamination. Nonetheless, a validated pipeline to manage this type of data is proposed by Standeven et al. (2024), where the MAGs are obtained by following the classic steps involving quality check, decontamination, assembly, binning, bin quality assessment and refinement, and taxonomic annotation. The main advantage of this pipeline is the integration of different bin software, and it can also authenticate the sequence provenance by estimating damage authentication of the host DNA (mainly human) via mapDamage2. Despite its validation to recover high-quality MAGs, this pipeline is only proposed and it has not been properly compiled in a single repository or container, and hence users should run the tools manually or leverage any of the other available pipelines in this suite.", 25 category: "Special" 26 }, 27 { 28 id: "pipeline2", 29 name: "Anvi'o", 30 attributes: { 31 readTypes: ["Short reads"], 32 multiSample: ["Yes"], 33 coAssemblyCoBinning: ["Yes"], 34 GUI: ["Yes"], 35 Cloud: ["No"], 36 workflowManager: [], 37 binRefinement: ["Yes"], 38 externalComputationalResources: ["No"], 39 executionOptions: ["Conda"], 40 specialOptions: ["Visualization module"], 41 update: ["2025"], 42 license: ["GNU GPL v3"], 43 category: ["Short-read centered"], 44 citations: ["678"] 45 }, 46 description: "Anvi'o is an advanced platform for metagenomic data analysis and MAG refinement, offering interactive tools for the manual curation, visualization, and annotation of genome bins. While it relies on external tools for initial processing, anvi'o excels at refining MAGs, exploring coverage and composition, and generating rich, interactive visualizations. It is ideal for users seeking high-resolution insights and customizable analysis beyond automated pipelines.", 47 url: "https://anvio.org/help/main/workflows/metagenomics/", 48 details: "Anviâo is a comprehensive modular platform for the analysis and visualization of microbial omics including, but not restricted to, metagenomics, metatranscriptomics and metapangenomics. Anviâo is developed to be highly customizable through exchangeable programs (tools) that perform specific tasks, empowering the user with a wide range of tools to explore. Being so, a metagenomics workflow is proposed by the developers of the platforms that begins with short-read quality cleaning, proceeds to read assembly to be used for read recruitment (mapping), and finalizes contig annotation (functions, Hidden Markov Models, and taxonomy). Optionally, the user can achieve read taxonomic profiling with KrakenUniq, and more recently binning tools have been made available such as MetaBAT2, CONCOCT, MaxBin2, BinSanity, as well as DASTool as a refinement alternative. Nonetheless, the user must run the analysis manually, requiring them to account with some experience regarding software installation, execution and debugging. Moreover, although Anviâo is in principle a command line tool, it incorporates a user-friendly graphical interface for data inspection and visualization that is commonly used for c
48ontig visualization.", 49 category: "Short-read centered" 50 }, 51 { 52 id: "pipeline3", 53 name: "Aviary", 54 attributes: { 55 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads", "Hybrid"], 56 multiSample: ["Yes"], 57 coAssemblyCoBinning: ["No"], 58 GUI: ["No"], 59 Cloud: ["Yes"], 60 workflowManager: ["Snakemake"], 61 binRefinement: ["Yes"], 62 externalComputationalResources: ["No"], 63 executionOptions: ["Conda"], 64 specialOptions: ["Genotype recovery"], 65 update: ["2025"], 66 license: ["GNU GPL v3"], 67 category: ["Hybrid"], 68 citations: ["Not found"] 69 }, 70 description: "Aviary is an end to end genome-centric metagenomics workflow. Novel and established methods are used assemble long read, short read or hybrid sequence datasets. Resulting contigs are binned using a large suite of primary metagenomic binners (including Rosella) and ensemble binning. Finalized bins are also assessed for quality using CheckM, and assigned taxonomic ranks using GTDB-tk.", 71 url: "https://rhysnewell.github.io/aviary/installation", 72 details: "Aviary is a modular, Snakemake-based pipeline, with Conda as package manager, designed for single or hybrid metagenomic assembly and MAG recovery, supporting both short and long-read input sequences. The workflow is distributed in 8 modules following a traditional workflow starting with quality and diversity assessment of the reads, followed by a discriminated assembly according to the type of input, MEGAHIT or metaSPAdes for short reads only or metaFlye in case of long reads solely. For hybrid assembly the process is divided into four stages: polishing with Racon and Pilon, metrics-based filtering, assembly and discard of low-quality bins and re-assembly with Unicycler. The pipeline proceeds with a subsequent assembly evaluation in terms of fragmentation, misassembly detection and diversity quantification, and a complementary module moves forward with a read mapping of the assembly and abundance statistics calculation. To continue with the workflow, the contigs are binned using up to 6 tools (MetaBAT2, Rosella, MetaBAT1, VAMB, MaxBin2 and CONCOCT) and refined afterwards with 5-time loop that includes CheckM2, Rosella Refine and DASTool. The pipeline ends with MAG recovery assessment via CoverM, CheckM2 and SingleM to proceed with MAG annotation through GTDB-Tk2, Prodigal and EggNOG. Variant calling, ANI analysis and genotype recovery with Lorikeet are interesting attributes offered by Aviary as a complement to the traditional genomic feature detection. Aviaryâs design presents a series of advantages that include the possibility of running modules, multi-sample handling and scalability across different computational infrastructures.", 73 category: "Hybrid" 74 }, 75 { 76 id: "pipeline4", 77 name: "BV-BRC", 78 attributes: { 79 readTypes: ["Short reads"], 80 multiSample: ["Yes"], 81 coAssemblyCoBinning: ["No"], 82 GUI: ["Yes"], 83 Cloud: ["No"], 84 workflowManager: [], 85 binRefinement: ["No"], 86 externalComputationalResources: ["Yes"], 87 executionOptions: [], 88 specialOptions: ["Taxonomic profiling", "Viral MAGs"], 89 update: ["2024"], 90 license: ["MIT License"], 91 category: ["Web-based"], 92 citations: ["783"] 93 }, 94 description: "BV-BRC offers a user-friendly, web-based service for metagenomic binning and MAG reconstruction, enabling researchers to process raw reads through quality control, assembly, binning, and annotation. Designed for accessibility, it supports multiple binning tools and provides taxonomic and functional insights via an intuitive interface, without requiring advanced computational infrastructure.", 95 url: "https://www.bv-brc.org/docs/tutorial/metagenomic_binning/metagenomic_binning.html", 96 details: "BV-BRC (Bacterial and Viral Bioinformatics Resource Center) is web-based platform that supports a broad spectrum of microbial genomics analyses, including genome-resolved metagenomics. This platform offers an intuitive interface to perform tailored quality control, assembly, binning, annotation, and downstream comparative analyses. For MAG building, BV-BRC has developed a specific metagenomic binning service, which offers genome assembly with metaSPAdes and MEGAHIT and a customized approach for genome binning based on kmer distribution and multi-genome functionality. Moreover, BV-BRC leverages PATRIC genomes to create reference bins as a starting point for annotation with RASTtk and/or VIGOR. Regarding technical features, BV-BRC runs entirely on a remote infrastru
96cture, allowing users to execute workflows without local installations or advanced computational setups. Aside from the features already mentioned, customizable analysis jobs, visualization tools and integrated comparative genomics tools are available, making BV-BRC a valuable resource for users seeking an accessible, reproducible, and data-rich environment for metagenomic studies.", 97 category: "Web-based" 98 }, 99 { 100 id: "pipeline5", 101 name: "DATMA", 102 attributes: { 103 readTypes: ["Short reads"], 104 multiSample: ["No"], 105 coAssemblyCoBinning: ["No"], 106 GUI: ["No"], 107 Cloud: ["No"], 108 workflowManager: ["COMP Superscalar"], 109 binRefinement: ["No"], 110 externalComputationalResources: ["No"], 111 executionOptions: [], 112 specialOptions: ["Reads grouped first and assembled in batches"], 113 update: ["2020"], 114 license: ["GNU GPL v3"], 115 category: ["Short-read centered"], 116 citations: ["4"] 117 }, 118 description: "DATMA (Distributed AuTomatic Metagenomic Assembly and annotation framework) is an automated and comprehensive bioinformatics pipeline designed for rapid analysis of metagenomic data, leveraging distributed computing for efficiency. It processes raw sequencing reads through quality control, 16S rRNA removal and classification, read binning using CLAME, de novo assembly with MEGAHIT.", 119 url: "https://peerj.com/articles/9762/", 120 details: "DATMA (Distributed AuTomatic Metagenomic Assembly and annotation framework) is a pipeline focused on speed and automation, leveraging distributed computing for efficiency. As a starting point, DATMA applies a quality filter with RAPPIFILT (customized tool developed for this pipeline), Trimmomatic and FastQC, and if the input sequences are paired-end, it merges them using FLASH and ForceMerge. Following this procedure, this pipeline identifies and removes 16S rDNA sequences based on RFAM (RNA sequence families), NCBI, RDP (Ribosomal Database Project) and SILVA to cluster the remaining sequences with CLAME. The clusters (or bins in definition of the traditional workflow) generated then are assembled in batches by metaSPAdes, Velvet, and MEGAHIT for a subsequent taxonomic annotation relying on BLAST and Kaiju, as well as ORF prediction with Prodigal and GeneMark. To conclude with the analysis a detailed HTML report is generated
120with interactive Krona plots for taxonomic visualization; this report integrates the 16S rDNA annotation (RDP Classified) along with the annotated bins. As inferred from the described workflow, DATMA performs an inverted approach to generate bins by first grouping the reads using CLAME, and attempting to assemble only these groups individually afterwards. Further, this pipeline is wrapped by COMP Superscalar which facilitates the development and execution of parallel applications for distributed infrastructures such as clusters, cloud services and containerized platforms.", 121 category: "Short-read centered" 122 }, 123 { 124 id: "pipeline6", 125 name: "EasyMetagenome", 126 attributes: { 127 readTypes: ["Short reads"], 128 multiSample: ["Yes"], 129 coAssemblyCoBinning: ["Yes"], 130 GUI: ["No"], 131 Cloud: ["No"], 132 workflowManager: [], 133 binRefinement: ["Yes"], 134 externalComputationalResources: ["No"], 135 executionOptions: ["Conda"], 136 specialOptions: ["Taxonomic profiling"], 137 update: ["2024"], 138 license: ["GNU GPL v3"], 139 category: ["Short-read centered"], 140 citations: ["14"] 141 }, 142 description: "EasyMetagenome is a web-based pipeline for automated metagenomic analysis and MAG reconstruction, designed for ease of use without requiring coding skills. It performs quality control, assembly, binning, and annotation through an intuitive interface, making it accessible to non-specialists while supporting high-throughput, reproducible workflows for microbial community analysis.", 143 url: "https://onlinelibrary.wiley.com/doi/10.1002/imt2.70001", 144 details: "EasyMetagenome integrates a classical workflow starting with short reads to provide a de-replicated (dRep) set of bins and pangenome analysis that relies on an Anviâo module. The assembly is performed with MEGAHIT, a MetaWRAP module is in charge of the binning task, CheckM2 controls the quality of the bins, and GTDB-Tk2 finalizes the execution by taxonomically annotating them. Notably, this pipeline performs functional annotation (GhostKOALA, eggNOG, dbCAN3) and taxonomy assignment on the contigs after a pre-filtering step that generates a non-redundant gene set. EasyMetagenome uses Conda environments to assure reproducibility, the user can input multi-sample data, although it is not orchestrated by any workflow manager. As special remarks, it carries out a taxonomic profiling (MetaPhlAn, HUMAnN, Kraken2) of the post-filtered (KneadData) reads, and the functional annotation of the gene set is expanded to identify virulence factors (VFDB) and antibiotic resistant genes (CARD).", 145 category: "Short-read centered" 146 }, 147 { 148 id: "pipeline7", 149 name: "EasyNanoMeta", 150 attributes: { 151 readTypes: ["Oxford Nanopore (ONT) reads", "Hybrid"], 152 multiSample: ["Yes"], 153 coAssemblyCoBinning: ["No"], 154 GUI: ["No"], 155 Cloud: ["No"], 156 workflowManager: [], 157 binRefinement: ["No"], 158 externalComputationalResources: ["No"], 159 executionOptions: ["Conda", "Singularity"], 160 specialOptions: ["Taxonomic profiling"], 161 update: ["2024"], 162 license: ["GNU GPL v3"], 163 category: ["Long-read focused"], 164 citations: ["0"] 165 }, 166 description: "EasyNanoMeta is a pipeline designed for Nanopore long-read metagenomic analysis, facilitating both assembly-based and assembly-free strategies. It encompasses steps from quality control and host sequence removal to assembly, polishing, binning, and annotation, supporting hybrid assemblies with short reads. EasyNanoMeta is containerized via Singularity, ensuring reproducibility and ease of deployment across diverse computing environments.", 167 url: "https://github.com/P-kai/EasyNanoMeta", 168 details: "EasyNanoMeta is a specialized pipeline designed to process ONT long reads either solely or in combination with short reads (hybrid assembly). This pipeline relies on a dual approach that uses both assembly-based and assembly-free strategies. Particularly, EasyNanoMeta incorporates four assemblers (MetaFlye, OP
168ERA-MS, MetaSPAdes, MetaPlatanus), five binners (SemiBin2, MetaBAT2, MaxBin2, CONCOCT, VAMB) and a polishing tool (NextPolish) to assure the best possible outcome. Additionally, once the bins are obtained, it performs the common tasks such as functional annotation with Prokka, quality control with CheckM2, phylogeny inference with PhyloPhlan and taxonomic classification with GTDB-Tk2. For the assembly-free methodology, EasyNanoMeta provides a full report containing composition, diversity and correlation among the identified species with Kraken2 and Centrifuge. Regarding operational characteristics, this pipeline can be run automatically on a Singularity/Apptainer image that streamlines the setup process and minimizes dependency issues or experienced users can execute individual modules through shell scripts that rely on Conda environments.", 169 category: "Long-read focused" 170 }, 171 { 172 id: "pipeline8", 173 name: "Eukfinder", 174 attributes: { 175 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads"], 176 multiSample: ["No"], 177 coAssemblyCoBinning: ["No"], 178 GUI: ["No"], 179 Cloud: ["No"], 180 workflowManager: [], 181 binRefinement: ["No"], 182 externalComputationalResources: ["No"], 183 executionOptions: ["Conda"], 184 specialOptions: ["Eukaryotic MAGs"], 185 update: ["2025"], 186 license: ["MIT License"], 187 category: ["Special"], 188 citations: ["1"] 189 }, 190 description: "Eukfinder is a specialized bioinformatics pipeline designed to recover microbial eukaryotic genomes, including both nuclear and mitochondrial DNA, from whole-genome shotgun (WGS) metagenomic datasets. Recognizing the complexity and underrepresentation of eukaryotic genomes in metagenomics, Eukfinder offers two tailored workflows: one for Illumina short reads (Eukfinder_short) and another for assembled contigs or long-read data (Eukfinder_long).", 191 url: "https://github.com/RogerLab/Eukfinder", 192 details: "Eukfinder is a specialized pipeline designed to recover microbial eukaryotic genomes, including both nuclear and mitochondrial DNA. Considering the inherent complexity and underrepresentation of eukaryotic genomes in metagenomics, this tool is composed by two workflows: the first one for Illumina short reads (Eukfinder_short) and another one for assembled contigs or long-read data (Eukfinder_long). In the workflow for short reads, they are first classified into five major taxonomic groups using Centrifuge and PLAST, and afterwards 'Eukaryotic' and 'Unknown' reads are subsequently assembled and reclassified to refine candidate eukaryotic sequences. On the other hand, the long-read version focuses on classifying pre-assembled contigs before proceeding to genome binning and downstream analysis. The binning procedure is common to both approaches and it relies on MyCC output, Centrifuge, and PLAST results in customized and tailored integration of kmer analysis and contigs mapping to eukaryotic genomes. Given its specificity, Eukfinder represents a flexible solution for studying eukaryotic microbial communities in environmental metagenomics.", 193 category: "Special" 194 }, 195 { 196 id: "pipeline9", 197 name: "Galaxy", 198 attributes: { 199 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads", "Hybrid"], 200 multiSample: ["No"], 201 coAssemblyCoBinning: ["No"], 202 GUI: ["Yes"], 203 Cloud: ["No"], 204 workflowManager: [], 205 binRefinement: ["Yes"], 206 externalComputationalResources: ["Yes"], 207 executionOptions: [], 208 specialOptions: ["Taxonomic profiling"], 209 update: ["2024"], 210 license: ["Academic Free License v3.0"], 211 category: ["Web-based"], 212 citations: ["1168"] 213 }, 214 description: "Galaxy offers a flexible, web-based environment for running customizable metagenomics workflows, including pipelines for MAG reconstruction. Users can perform quality control, assembly, binning, and annotation using integrated tools such as MEGAHIT, MetaBAT2, and Prokka. With a graphical interface and built-in resource management, Galaxy enables accessible, reproducible analyses without coding.", 215 url: "https://usegalaxy.org/published/workflow?id=33d90e718ce500ef", 216 details: "Galaxy is a web-based platform and open-source project that empowers scientists all over the world to conduct bioinformatics analysis in a user-friendly and intuitive graphical interface that requires no programming skills. Galaxy offers a broad range of tools covering genomics, transcriptomics, metagenomics, among many others, where the user is free to select the softw
216are that best suits their needs. In addition, the users can share their workflows in the platform, and therefore users can just follow pre-established methodologies validated by a world-wide community. As a result, there are multiple pipelines designed for MAG reconstruction that feature common tools like MEGAHIT for assembly, MetaBAT2 or MaxBin2 for binning, and Prokka or GTDB-Tk2 for annotation and classification. Also, given Galaxyâs flexibility the traditional workflow can be expanded to include long reads, accomplish read-based taxonomic profiling or detect and classify viral sequences. Being so, Galaxy ensures reproducibility through automatic tracking of parameters and tool versions, and supports HPC and cloud deployment, making it scalable for projects of various sizes. Notwithstanding, the users may experience limitations in performance for large datasets and/or delays in result processing as Galaxyâs community of users grows every day with the subsequent demand for more computational resources.", 217 category: "Web-based" 218 }, 219 { 220 id: "pipeline10", 221 name: "GEN-ERA", 222 attributes: { 223 readTypes: ["Short reads", "Oxford Nanopore (ONT) reads"], 224 multiSample: ["Yes"], 225 coAssemblyCoBinning: ["No"], 226 GUI: ["No"], 227 Cloud: ["Yes"], 228 workflowManager: ["Nextflow"], 229 binRefinement: ["No"], 230 externalComputationalResources: ["No"], 231 executionOptions: ["Singularity"], 232 specialOptions: ["Metabolic modeling"], 233 update: ["2024"], 234 license: ["GNU GPL v3"], 235 category: ["Dual"], 236 citations: ["7"] 237 }, 238 description: "GEN-ERA Toolbox is a comprehensive suite of reproducible workflows tailored for microbial genomics and metagenomics. It encompasses pipelines for genome assembly, binning, quality assessment, and taxonomic annotation.", 239 url: "https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giad022/7111624", 240 details: "GEN-ERA suite is a collection of Nextflow pipelines aiming at supporting MAG reconstruction and annotation with as many methodologies as possible starting from either short or long reads. Specifically, this toolbox counts with more than 10 workflows specifically designed for tasks ranging from assembly and binning, quality assessment and decontamination, orthologous inference and maximum likelihood phylogenomic analyses, SSU rRNA phylogeny (constrained by ribosomal phylogenomic), Average Nucleotide Identity (ANI) clustering, taxonomic identification and metabolic modelling. Moreover, GEN-ERA incorporates specific tools designed to handle eukaryotic assembly annotation such as BRAKER2 and AMAW. Thus, GEN-ERA suits almost all requirements any user might demand given the variety of goals that can be achieved within a single software suite. From a technical point of view, operational GEN-ERA features, Nextflow-managed and Singularity-executed, ensures portability and reproducibility across environments.", 241 category: "Dual" 242 }, 243 { 244 id: "pipeline11", 245 name: "HiFi-MAG-Pipeline", 246 attributes: { 247 readTypes: ["Hi-Fi (PacBio) reads"], 248 multiSample: ["Yes"], 249 coAssemblyCoBinning: ["No"], 250 GUI: ["No"], 251 Cloud: ["Yes"], 252 workflowManager: ["Snakemake"], 253 binRefinement: ["Yes"], 254 externalComputationalResources: ["No"], 255 executionOptions: ["Conda"], 256 specialOptions: [], 257 update: ["2025"], 258 license: ["BSD-3-Clause-Clear License"], 259 category: ["Long-read focused"], 260 citations: ["8"] 261 }, 262 description: "HiFi-MAG-Pipeline is a Snakemake-based workflow developed by PacBio for reconstructing high-quality MAGs from HiFi long-read metagenomic assemblies. It employs a completeness-aware strategy to prioritize long, near-complete contigs, integrates binning tools like MetaBAT2 and SemiBin2, and performs quality assessment with CheckM2, facilitating the recovery of complete, circular MAGs.", 263 url: "https://github.com/PacificBiosciences/pb-metagenomics-tools/blob/master/docs/Tutorial-HiFi-MAG-Pipeline.md", 264 details: "Hi-Fi-MAG-Pipeline is a simple yet time-saving pipeline developed and maintained by Pacific Biosciences specially designed to build MAGs from Hi-Fi reads (long PacBio reads). It encompasses different binning tools (MetaBAT2 and SemiBin2) along with DASTool as refinement software; CheckM2 serves a quality control tool, where contigs above 500 kb are kept as single bins if they show a completeness above 93%, otherwise they are sent back to the binning module. This approach enhances the recovery of high-quality and single-contig MAGs, outperforming traditional binning methods. After MAG de-replication, taxonomic annotation is achieved with GTDB-Tk2, and a complete graphical report is compiled automatically. One important caveat about this workflow is represented by its lack of assembly step, and hence the user must prepare the assembly of the PacBio sequences beforehand using tools such as hifiasm in its meta version, metaFlye, OP
264ERA-MS, among others. Hi-Fi-MAG-Pipeline requires Conda as software manager, and it is orchestrated by Snakemake.", 265 category: "Long-read focused" 266 }, 267 { 268 id: "pipeline12", 269 name: "IDseq", 270 attributes: { 271 readTypes: ["Short reads", "Oxford Nanopore (ONT) reads"], 272 multiSample: ["No"], 273 coAssemblyCoBinning: ["No"], 274 GUI: ["Yes"], 275 Cloud: ["No"], 276 workflowManager: [], 277 binRefinement: ["No"], 278 externalComputationalResources: ["Yes"], 279 executionOptions: [], 280 specialOptions: ["Viral MAGs"], 281 update: ["2025"], 282 license: ["MIT license"], 283 category: ["Web-based"], 284 citations: ["347"] 285 }, 286 description: "IDseq is an open-source platform developed for metagenomic next-generation sequencing (mNGS) analysis. IDseq has a specific scope focused on pathogen detection, antibiotic resistance detection and infection control.", 287 url: "https://czid.org/", 288 details: "IDseq is an open-source, cloud-based platform developed for metagenomic next-generation sequencing (mNGS) analysis. IDseq has a specific scope focused on pathogen detection, antibiotic resistance detection and infection control. IDseq supports short-reads or long reads (ONT) to provide analyses that encompass host read removal, quality control, alignment, and taxonomic classification using a curated reference database based on NCBI nt and nr databases. Although IDseq is not primarily focused on MAG reconstruction, it is highly valuable in the initial stages of metagenomics data analysis projects. As interesting remarks, IDseqâs results are visualized through interactive dashboards that provide taxonomic trees, abundance plots, and detailed sample metrics thanks to its web-based interface that requires minimal bioinformatics expertise. Also, the users can find alternative pipelines for viral consensus genome recovery and antimicrobial resistance gene detection.", 289 category: "Web-based" 290 }, 291 { 292 id: "pipeline13", 293 name: "KBase", 294 attributes: { 295 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads", "Hybrid"], 296 multiSample: ["Yes"], 297 coAssemblyCoBinning: ["Yes"], 298 GUI: ["Yes"], 299 Cloud: ["No"], 300 workflowManager: [], 301 binRefinement: ["Yes"], 302 externalComputationalResources: ["Yes"], 303 executionOptions: [], 304 specialOptions: ["Taxonomic profiling", "Metabolic modeling"], 305 update: ["2024"], 306 license: ["MIT license"], 307 category: ["Web-based"], 308 citations: ["63"] 309 }, 310 description: "KBase (The Department of Energy Systems Biology Knowledgebase) is a collaborative, web-based platform that enables researchers to perform comprehensive metagenomics analyses through its interactive Narrative Interface. Users can build reproducible workflows for quality control, assembly (e.g., metaSPAdes, MEGAHIT), binning (e.g., MetaBAT2), annotation (e.g., RASTtk, DRAM), and metabolic modeling using ModelSEED.", 311 url: "https://www.nature.com/articles/s41596-022-00747-x", 312 details: "KBase (the Department of Energy Systems Biology Knowledgebase) is a collaborative, web-based platform that enables researchers to perform comprehensive metagenomics analyses through its customized interactive Narrative Interface. This platform allows users to build and share workflows (narratives) for genome assembly, comparative genomics, metagenomics, among others. Specifically, the metagenomics narrative offers running MAG-centered pipeline steps such as quality control, assembly (e.g., metaSPAdes, MEGAHIT), binning (i.e., MetaBAT2), annotation (e.g., RASTtk, DRAM), and metabolic modeling using ModelSEED. KBase platform offers automated data provenance, seamless integration with public databases, and interactive visualizations to interpret MAG quality, taxonomy, and metabolic pathways. The possibility of running analyses using external resources makes KBase a powerful and accessible environment for genome-resolved metagenomics, particularly valuable for users lacking access to HPC systems.", 313 category: "Web-based" 314 }, 315 { 316 id: "pipeline14", 317 name: "MAGNETO", 318 attributes: { 319 readTypes: ["Short reads"], 320 multiSample: ["Yes"], 321 coAssemblyCoBinning: ["Yes"], 322 GUI: ["No"], 323 Cloud: ["Yes"], 324 workflowManager: ["Snakemake"], 325 binRefinement: ["No"], 326 externalComputationalResources: ["No"], 327 executionOptions: ["Conda"], 328 specialOptions: ["Taxonomic profiling"], 329 update: ["2025"], 330 license: ["GNU GPL v3"], 331 category: ["Short-read centered"], 332 citations: ["13"] 333 }, 334 description: "MAGNETO is an automated workflow dedicated to MAG reconstruction, which includes a fully-automated coassembly step informed by optimal clustering of metagenomic distances, and implements complementary genome binning strategies, for improving MAG recovery.", 335 url: "https://journals.asm.org/doi/10.1128/msystems.00432-22", 336 details: "MAGNETO is an automated, modularized and scalable pipeline wrapped with Snakemake and executed with Conda. It is focused on allowing the user the selection of different assembly and/or binning strategies, involving several steps from read pre-processing until MAG annotation and gene catalog generation. The Pre-processing module leverages fastp, Bowtie2 and FastQ Screen, whilst the Assembly mode uses Simka and hierarchical agglomerative clustering to cluster the samples if the users pre-defines a co-assembly strategy; the reads are assembled using MEGAHIT. Furthermore, contig abundances are computed by alignment against the raw reads to be bin by MetaBAT2 afterwards. Quality estimation and dereplication are carried out with CheckM v1.0 and dRep, respectively. To end the workflow, a gene catalog is produced for both the contigs and the MAGs by running Prodigal, Linclust and CD-HIT, and the MAGs are annotated with GTDB-Tk2, Mummer and EggNOGmapper. As a special feature, MAGNETO can provide a read-based taxonomy abundance with mOTU profiler. MAGNETO exhibits all the advantages Snakemake wrapping, and executed with Conda, represents such as multi-sample handling, scalability across different c
336omputing infrastructures and checkpoint control for workflow restarting.", 337 category: "Short-read centered" 338 }, 339 { 340 id: "pipeline15", 341 name: "metaGEM", 342 attributes: { 343 readTypes: ["Short reads"], 344 multiSample: ["Yes"], 345 coAssemblyCoBinning: ["No"], 346 GUI: ["No"], 347 Cloud: ["Yes"], 348 workflowManager: ["Snakemake"], 349 binRefinement: ["Yes"], 350 externalComputationalResources: ["No"], 351 executionOptions: ["Conda"], 352 specialOptions: ["Metabolic modeling", "Eukaryotic MAGs"], 353 update: ["2023"], 354 license: ["MIT License"], 355 category: ["Short-read centered"], 356 citations: ["99"] 357 }, 358 description: "metaGEM pipeline takes metagenome-assembled genomes (MAGs) as input and aims to generate genome-scale metabolic models (GEMs) for each reconstructed genome. It typically involves steps such as gene prediction on the MAGs, followed by functional annotation of the predicted genes using databases like KEGG, MetaCyc, and EggNOG.", 359 url: "https://academic.oup.com/nar/article/49/21/e126/6382386", 360 details: "metaGEM represents a traditional end-to-end pipeline designed to reconstruct MAGs from metagenomics raw reads; however, its main feature relies on an integrated module that provides genome scale metabolic models (GEMs). The workflow starts with the read quality cleaning using fastp for a subsequent assembly with MEGAHIT and a contig coverage estimation with BWA. The bins are then obtained via three different tools (MetaBAT2, MaxBin2 and CONCOCT) along a posterior refining by the metaWRAP refinement module. As a result, the bins or MAGs are used as input for CarveMe (Genome Scale Metabolic Models), and SMETANA is called for metabolic interaction predictions and MEMOTE is in charge of generating quality reports. The resulting GEMs can then be used for various downstream analyses, such as predicting metabolic interactions within the community, simulating growth under different conditions, and identifying key metabolic pathways. The pipeline ends with MAG characterization through Prokka and Roary (functional annotation and pangenome analysis), GRiD (growth rate estimation), GTDB-Tk2 (taxonomic annotation) and BWA (genome abundance). As additional features, metaGEM identifies eukaryotic MAGs via EukRep and evaluates contamination with EukCC. Also, this pipeline produces taxonomic abundance profiles from the filtered reads using mOTUS2. Naturally, this pipeline exhibits the benefits Snakemake orchestration provides.", 361 category: "Short-read centered" 362 }, 363 { 364 id: "pipeline16", 365 name: "MetaGenePipe", 366 attributes: { 367 readTypes: ["Short reads"], 368 multiSample: ["Yes"], 369 coAssemblyCoBinning: ["Yes"], 370 GUI: ["No"], 371 Cloud: ["Yes"], 372 workflowManager: ["Workflow Definition Language (WDL)"], 373 binRefinement: ["No"], 374 externalComputationalResources: ["No"], 375 executionOptions: ["Singularity"], 376 specialOptions: [], 377 update: ["2023"], 378 license: ["Apache License 2.0"], 379 category: ["Short-read centered"], 380 citations: ["1"] 381 }, 382 description: "MetaGenePipe is a portable, scalable pipeline for functional and taxonomic analysis of metagenomic contigs. Written in WDL and containerized with Singularity, it supports both co-assembly and single-sample workflows. The pipeline integrates tools for assembly, gene prediction, and annotation, facilitating reproducible characterization of prokaryotic communities from shotgun metagenomic data.", 383 url: "https://joss.theoj.org/papers/10.21105/joss.04851", 384 details: "MetaGenePipe is a pipeline developed with Workflow Definition Language (WDL), self-executed within a Singularity container, whose primary goal is performing a contig-based functional and taxonomic analysis from short read sequences. It is composed of 4 subworkflows, where the operation starts with the quality control workflow, the subsequent one assembles the reads with MEGAHIT to map them back against the short reads within the third subworkflow. Meanwhile, the last subworkflow is in charge of gene prediction and functional annotation based on two main strategies: alignment with the Swiss-Prot database and Hidden Markov Models search in KOfam database. Although MetaGenePipe does not include binning software to provide MAGs as main output, its versatility that allows an analysis adapted for eukaryotic and viral analyses with minimal modifications, and its uncommon workflow manager within the pipelines considered in this review, makes MetaGenePipe an interesting alternative for users with advanced computational infrastru
384ctures. Additionally, MetaGenePipe is designed to handle a co-assembly strategy in case the user requires this feature.", 385 category: "Short-read centered" 386 }, 387 { 388 id: "pipeline17", 389 name: "Metagenome-Atlas", 390 attributes: { 391 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads", "Hybrid"], 392 multiSample: ["Yes"], 393 coAssemblyCoBinning: ["Yes"], 394 GUI: ["No"], 395 Cloud: ["Yes"], 396 workflowManager: ["Snakemake"], 397 binRefinement: ["Yes"], 398 externalComputationalResources: ["No"], 399 executionOptions: ["Conda"], 400 specialOptions: [], 401 update: ["2024"], 402 license: ["BSD-3-Clause-Clear License"], 403 category: ["Hybrid"], 404 citations: ["159"] 405 }, 406 description: "Metagenome-Atlas is an end-to-end, Snakemake-based pipeline designed for the reconstruction and annotation of MAGs from metagenomic data. It supports Illumina short reads and provides modular workflows covering all major steps.", 407 url: "https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03585-4", 408 details: "Metagenome-Atlas is an end-to-end, Snakemake-based and Conda-executed pipeline supporting Illumina short reads and providing a modular workflow. It is divided into four modules, namely Quality Control, Assembly, Genomic Binning and Annotation. The initial module removes host, common contaminants and PCR duplicates, and if necessary, trims low-quality sequences according to user pre-specified parameters. The Assembly module corrects sequence errors based on kmer coverage, merges paired-end sequences, assembles them using MEGAHIT and/or metaSPAdes along with a contig-length filtering. The following module uses MetaBAT2, MaxBin2, and optionally VAMB and SemiBin2 to bin the contigs; CheckM2, BUSCO and GUNC are run to measure the bin quality, as well as DASTool and dRep for bin refinement and dereplication. For the last module, Metagenome-Atlas taxonomically and functionally annotates the MAGs using GTDB-Tk2 and DRAM, respectively, and it finally produces a gene catalog through mapping the predicted coding sequences using EggNOG mapper. Among the main advantages of Metagenome-Atlas, it is possible to describe the possibility of running individual modules and its energetic supporting community and developers. Moreover, the Snakemake wrapper allows for flexibility, multi-sample handling, and adaptability to medium to large projects running on local servers or High-Performance Cluster (HPC) environments.", 409 category: "Hybrid" 410 }, 411 { 412 id: "pipeline18", 413 name: "Metagenomics-Toolkit", 414 attributes: { 415 readTypes: ["Short reads", "Oxford Nanopore (ONT) reads"], 416 multiSample: ["Yes"], 417 coAssemblyCoBinning: ["No"], 418 GUI: ["No"], 419 Cloud: ["Yes"], 420 workflowManager: ["Nextflow"], 421 binRefinement: ["Yes"], 422 externalComputationalResources: ["No"], 423 executionOptions: ["Conda"], 424 specialOptions: ["Plasmid assembly", "Metabolic modeling", "Adaptable resource allocation", "Visualization module"], 425 update: ["2025"], 426 license: ["GNU AGPL v3"], 427 category: ["Dual"], 428 citations: ["0"] 429 }, 430 description: "Metagenomics-Toolkit is a scalable and adaptable workflow for metagenomic analysis, optimized through machine learning to adjust memory usage during assembly, reducing the need for high-memory hardware. Built with Nextflow and Docker, it supports both short and ONT long reads. The pipeline includes standard MAG processing steps namely, quality control, assembly, binning and annotation, and adds modules for output aggregation, plasmid identification, recovery of unassembled taxa, and analysis of microbial interactions through dereplication, co-occurrence, and metabolic modeling.", 431 url: "https://academic.oup.com/nargab/article/7/3/lqaf093/8204052", 432 details: "Metagenomics-Toolkit is a workflow designed to increase scalability of task execution, enabling optimal resource allocation from its machine learning-optimized assembly step. This optimized assembly tailors the peak RAM value requested by a metagenome assembler to match actual requirements, thereby minimizing the dependency on dedicated high-memory hardware. Metagenomics-Toolkit is wrapped by Nextflow and powered with Docker containerization technology, and it can take either short or Oxford Nanopore (ONT) long reads as input. As a result, this pipeline is highly scalable and adaptable across computational infrastru
432ctures with a backbone workflow that relies on the traditional MAG-aimed steps such as quality control, assembly, binning, and annotation, plus an aggregation module that captures the output from each sample to âpolishâ the final MAGs. Regarding special features offered by Metagenomics-Toolkit, it offers plasmid identification based on various tools, the recovery of unassembled microbial community members, and the discovery of microbial interdependencies through a combination of dereplication, co-occurrence, and genome-scale metabolic modeling.", 433 category: "Dual" 434 }, 435 { 436 id: "pipeline19", 437 name: "Metaphor", 438 attributes: { 439 readTypes: ["Short reads"], 440 multiSample: ["Yes"], 441 coAssemblyCoBinning: ["Yes"], 442 GUI: ["No"], 443 Cloud: ["Yes"], 444 workflowManager: ["Snakemake"], 445 binRefinement: ["Yes"], 446 externalComputationalResources: ["No"], 447 executionOptions: ["Conda"], 448 specialOptions: ["Visualization module"], 449 update: ["2024"], 450 license: ["MIT License"], 451 category: ["Short-read"], 452 citations: ["13"] 453 }, 454 description: "Metaphor is a bioinformatics pipeline designed for the comprehensive analysis of metagenomic data, emphasizing functional annotation and metabolic pathway reconstruction. It processes raw sequencing reads through quality control, assembly, and gene prediction.", 455 url: "https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giad055/7233990", 456 details: "Metaphor is a classic metagenomics pipeline aiming at MAG reconstruction and annotation wrapped by Snakemake and leveraging Conda as package manager. The pipeline is triggered by the user with a .csv file pointing to the sequence directories and a .yaml file with the pipeline configuration. A quality control will be carried out then with FastQC and fastp, with a posterior assembly with MEGAHIT, contig evaluation with MetaQUAST and mapping against the input sequences using Minimap2 and Samtools; the contigs are binned (VAMB, MetaBAT2, CONCOCT) and refined (DASTool). Metaphor execution finalizes with bin annotation through Prodigal, Diamond, and the NCBI COG database. Complementary to Snakemake orchestration capabilities, Metaphor provides a series of plots depicting runtime and memory with the goal of identifying computational bottlenecks during the analyses.", 457 category: "Short-read" 458 }, 459 { 460 id: "pipeline20", 461 name: "metagWGS", 462 attributes: { 463 readTypes: ["Short reads", "Hi-Fi (PacBio) reads"], 464 multiSample: ["Yes"], 465 coAssemblyCoBinning: ["Yes"], 466 GUI: ["No"], 467 Cloud: ["Yes"], 468 workflowManager: ["Nextflow"], 469 binRefinement: ["Yes"], 470 externalComputationalResources: ["No"], 471 executionOptions: ["Singularity"], 472 specialOptions: ["Taxonomic profiling"], 473 update: ["2024"], 474 license: ["GNU GPL v3"], 475 category: ["Dual"], 476 citations: ["2"] 477 }, 478 description: "metagWGS is a bioinformatics pipeline designed for the analysis of whole-genome shotgun (WGS) metagenomic data, focusing on microbial community profiling and functional analysis. It typically encompasses steps for quality control of raw reads, assembly of metagenomic sequences, gene prediction on the assembled contigs, taxonomic classification of both reads and contigs, and functional annotation of the predicted genes.", 479 url: "https://www.biorxiv.org/content/10.1101/2024.09.13.612854v1", 480 details: "metagWGS is one of the most recently released pipelines whose main differential is related with the possibility to assemble either short reads or long sequences (PacBio). This Nextflow pipeline is built off Singularity with consequent benefits this kind of setup brings. It incorporates a wide variety of tools as it must ensure a proper workflow for both types of sequencing technologies in a traditional end-to-e
480nd framework divided into 8 steps. The first step aims at cleaning and performing quality control with proper tools according to the input, while the second step allows the assembly of the sequences using either metaSPAdes/MEGAHIT for short sequences and hifiasm/metaFlye for PacBio reads. Following with the process, this pipeline filters the contigs and performs structural annotation during steps 3 and 4, respectively; step 5 is designed to estimate contig abundance by mapping them against the reads. Afterwards, a complete subworkflow for functional annotation is undergone with EggNOG mapper at its core (step 6), and contig taxonomic affiliation is achieved through home-made scripts (step 7) to conclude with step 8, where the contigs are binned with MaxBin2, MetaBAT2 and CONCOCT. Remarkably, metagWGS utilizes BINETTE, a state-of-the-art binning refinement tool designed to construct high-quality MAGs from the output of multiple binning tools. As a special remark, metagWGS performs read taxonomic profiling via Kaiju, as well as contig annotation that includes an in-house algorithm and mapping against the reads.", 481 category: "Dual" 482 }, 483 { 484 id: "pipeline21", 485 name: "MetaWRAP", 486 attributes: { 487 readTypes: ["Short reads"], 488 multiSample: ["No"], 489 coAssemblyCoBinning: ["Yes"], 490 GUI: ["No"], 491 Cloud: ["No"], 492 workflowManager: [], 493 binRefinement: ["Yes"], 494 externalComputationalResources: ["No"], 495 executionOptions: ["Conda", "Docker"], 496 specialOptions: ["Taxonomic profiling"], 497 update: ["2020"], 498 license: ["MIT License"], 499 category: ["Short-read"], 500 citations: ["1917"] 501 }, 502 description: "MetaWRAP is a versatile and modular pipeline tailored for metagenomic data analysis and MAG recovery, with strong support for Illumina short-read data. Built primarily as a command-line framework with a focus on flexibility and user control, MetaWRAP consists of individual modules that can be run independently or combined into custom workflows.", 503 url: "https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-018-0541-1", 504 details: "MetaWRAP is a popular and customizable pipeline built primarily as a command-line framework with a focus on flexibility and user control. MetaWRAP consists of individual modules that can be run independently or combined into custom workflows. Its core functionalities encompass read QC and cleaning (FastQC, Trim Galore and BMTagger), assembly (MEGAHIT, metaSPAdes, BWA and MetaQUAST), and a binning suite that incorporates MetaBAT2, MaxBin2, and CONCOCT. MetaWRAP also includes a native refinement module that produces hybrid bin sets to explore over the different variants of each bin (original and hybridized bin sets) to determine the âbest binâ according to the user pre-specified quality values based on completeness and contamination (CheckM v1.0). This module is frequently executed in independent metagenomics analysis, and even some pipelines described in this review incorporate it within their workflows. If decided by the user, MetaWRAP offers the possibility of bin re-assembling guided by their previous versions, improving the overall bin quality. For MAG taxonomic and functional analysis, MetaWRAP relies on Prokka and Taxator-tk (combined with NCBI databases), and it provides visualization modules for summarizing results. Analogous to MAGNETO, MetaWRAP can produce read-based taxonomic profiles in parallel. Although MetaWRAP does not integrate full pipeline automation, its high modularity and straightforward design have promoted a wide supporting community. Nonetheless, at the moment of writing this report, MetaWRAP is not maintained by the developers, with the subsequent lack of tool updates.", 505 category: "Short-read centered" 506 }, 507 { 508 id: "pipeline22", 509 name: "MGnify", 510 attributes: { 511 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads", "Hybrid"], 512 multiSample: ["Yes"], 513 coAssemblyCoBinning: ["Yes"], 514 GUI: ["Yes"], 515 Cloud: ["No"], 516 workflowManager: [], 517 binRefinement: ["No"], 518 externalComputationalResources: ["Yes"], 519 executionOptions: [], 520 specialOptions: ["Taxonomic profiling"], 521 update: ["2025"], 522 license: ["Apache License 2.0"], 523 category: ["Web-based"], 524 citations: ["286"] 525 }, 526 description: "MGnify provides a web-based pipeline for metagenomic assembly and binning, enabling users to analyze raw sequencing reads through quality control, assembly, binning, and annotation. The platform supports user-submitted data and offers an intuitive interface, making it accessible for researchers without extensive computational resources.", 527 url: "https://docs.mgnify.org/src/docs/analysis.html", 528 details: "MGnify is a web-based platform hosted by EMBL-EBI with an automatized service for submitting and annotating microbiome-derived sequence data. It counts with a standardized pipeline that receives raw reads to perform functional and taxonomic annotation with an extensive series of tools encompassing mOTUs2, InterProScan, KEGG annotation (hmmscan), EggNOG mapper and/or antiSMASH. Optionally, MGnify offers the possibility for read assembly through metaSPAdes with a prior contamination removal to continue with the annotation. In the recent years, MGnify has evolved to accept and process long reads from PacBio and ONT with the pipeline MGnify-lr that carries out read pre-filtering, assembly with Flye and re-mapping against the initial sequences. Furthermore, users can contribute to the resource MGnify Genomes which stores a genome catalogues each user can create with their own MAGs. Once the MAGs are submitted to this space, they are automatically analyzed with a pipeline that establishes overall quality and annotates them. Given that MGnify is a service controlled by EMBL-EBI, the user is only requested to submit the data and make it publicly available before the analysis to ENA. As a result, MGnify is a powerful computational resource and user-friendly as the user interacts with the platform to upload the data through its web interface, taking the burden off the user. However, MGnify's reliance on predefined workflows may limit flexibility for users seeking to customize specific steps or parameters in the analysis, while at the same time heavy use by multiple users may delay result delivery.", 529 category: "Web-based" 530 }, 531 { 532 id: "pipeline23", 533 name: "MOSHPIT", 534 attributes: { 535 readTypes: ["Short reads"], 536 multiSample: ["Yes"], 537 coAssemblyCoBinning: ["No"], 538 GUI: ["No"], 539 Cloud: ["No"], 540 workflowManager: [], 541 binRefinement: ["Yes"], 542 externalComputationalResources: ["No"], 543 executionOptions: ["Conda"], 544 specialOptions: ["Taxonomic profiling"], 545 update: ["2025"], 546 license: ["BSD-3-Clause-Clear License"], 547 category: ["Short-read centered"], 548 citations: ["1"] 549 }, 550 description: "MOSHPIT (MOdular SHotgun metagenome Pipelines with Integrated provenance Tracking) is a toolkit of plugins for whole metagenome assembly, annotation, and analysis built on the microbiome multi-omics data science framework QI
550IME 2. MOSHPIT enables flexible, modular, fully reproducible workflows for read-based or assembly-based analysis of metagenome data.", 551 url: "https://moshpit.qiime2.org/en/stable/intro.html", 552 details: "According to its documentation, MOSHPIT (MOdular SHotgun metagenome Pipelines with Integrated provenance Tracking) is a toolkit of plugins for whole metagenome assembly, annotation, and analysis built on the microbiome multi-omics data science framework QIIME2. MOSHPIT enables flexible, modular, fully reproducible workflows for read-based or assembly-based analysis of metagenome data. The core components of MOSHPIT include q2-assembly, which provides functionalities for genome assembly and quality control, and q2-annotate, which supports contig binning, taxonomic classification, and functional annotation. Additional plugins, such as q2-viromics and q2-amrfinderplus, extend capabilities to viral sequence detection and antimicrobial resistance gene annotation, respectively. In technical terms, MOSHPIT must be run locally or on an HPC environment with the possibility to execute the processes in parallel by the explicit declaration of partitions, a native QIIME2 functionality. Further, the entire QIIME2 ecosystem relies on Conda, and hence this a sine-qua-non requisite to perform MAG reconstruction with MOSHPIT.", 553 category: "Short-read centered" 554 }, 555 { 556 id: "pipeline24", 557 name: "MUFFIN", 558 attributes: { 559 readTypes: ["Hybrid"], 560 multiSample: ["Yes"], 561 coAssemblyCoBinning: ["No"], 562 GUI: ["No"], 563 Cloud: ["Yes"], 564 workflowManager: ["Nextflow"], 565 binRefinement: ["Yes"], 566 externalComputationalResources: ["No"], 567 executionOptions: ["Conda", "Docker", "Singularity"], 568 specialOptions: ["Metatranscriptome support"], 569 update: ["2022"], 570 license: ["GNU GPL v3"], 571 category: ["Hybrid"], 572 citations: ["34"] 573 }, 574 description: "MUFFIN is a reproducible and user-friendly metagenomic pipeline, built with Nextflow, that excels in hybrid assembly by integrating both short-read (Illumina) and long-read (nanopore) sequencing data. The pipeline progresses through assembly and binning, employing differential coverage binning to improve the quality and completeness of metagenome-assembled genomes (MAGs).", 575 url: "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008716", 576 details: "MUFFIN is a reproducible pipeline built with Nextflow designed for hybrid assembly by integrating short-read (Illumina) and long-read (nanopore) sequencing data. MUFFIN begins its workflow with a quality control of the reads (fastp and Filtlong) to progress through hybrid assembly (metaSPAdes or metaFlye with polishing) and differential binning (CONCOCT, MetaBAT2, and MaxBin2). After bin refining with the MetaWRAP refinement module, a hybrid reassembly is pursued with Unicycler. The pipeline ends with bin classification through CheckM v1.1 and sourmash (combined with GTDB), and with bin annotation with EggNOG and a KEGG parser, providing high-quality, annotated MAGs and insights into the metabolic potential of the microbial community. Optionally, the user can provide metatranscriptomics data to perform a de novo transcript assembly (Trinity), quantification (Salmon) and annotation (EggNOG). Additionally, given its modularity design, the workflow can start as well with user-provided bins, differential reads or only RNA-seq data. MUFFIN can be executed with either Conda or Docker, and its native Nextflow features confer to it the possibility to restart the pipeline in case of failing, run on different computing infrastructures, multi-sample handling, among others.", 577 category: "Hybrid" 578 }, 579 { 580 id: "pipeline25", 581 name: "NanoPhase", 582 attributes: { 583 readTypes: ["Oxford Nanopore (ONT) reads", "Hybrid"], 584 multiSample: ["No"], 585 coAssemblyCoBinning: ["No"], 586 GUI: ["No"], 587 Cloud: ["No"], 588 workflowManager: [], 589 binRefinement: ["Yes"], 590 externalComputationalResources: ["No"], 591 executionOptions: ["Conda"], 592 specialOptions: [], 593 update: ["2023"], 594 license: ["MIT License"], 595 category: ["Long-read focused"], 596 citations: ["73"] 597 }, 598 description: "NanoPhase is a pipeline designed for reconstructing high-quality MAGs from complex metagenomes using Nanopore long reads or hybrid sequencing strategies. It integrates assembly, binning, polishing, and annotation steps, facilitating the recovery of complete genomes and mobile genetic elements from diverse microbial communities.", 599 url: "https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01415-8", 600 details: "NanoPhase is a pipeline that enables building high-quality MAGs from ONT long reads, optionally enhanced with short read-based MAG polishing. The backbone of the pipeline is represented by an assembly with metaFlye followed by contig binning with MetaBAT2 and MaxBin2, and bin refinement with a MetaWRAP module. To estimate abundance and coverage, the contigs are mapped against the reads, and several polishing rounds with Racon and medaka, complete the workflow to generate high-accuracy final bins; If the user decides to include short reads in the analysis, these are used for polishing with Pilon.
600Complementary, MetaQuast and CheckM v1.0 are in charge of MAG quality control, IDEEL evaluates the fraction of predicted full-length proteins in each MAG, full-length proteins are detected via alignment with UniProtKB, and Prokka serves as functional annotation software. Remarkably, NanoPhase allows prophage and active prophage identification within the reconstructed MAGs with VIBRANT and PropagAtE. Among pipeline technical specifications, this pipeline requires Conda as package manager and it offers parallelized execution with GNU Parallel to speed up the analysis.", 601 category: "Long-read focused" 602 }, 603 { 604 id: "pipeline26", 605 name: "nf-core/mag", 606 attributes: { 607 readTypes: ["Short reads", "Hybrid", "Oxford Nanopore (ONT) reads", "Hi-Fi (PacBio) reads"], 608 multiSample: ["Yes"], 609 coAssemblyCoBinning: ["Yes"], 610 GUI: ["No"], 611 Cloud: ["Yes"], 612 workflowManager: ["Nextflow"], 613 binRefinement: ["Yes"], 614 externalComputationalResources: ["No"], 615 executionOptions: ["Conda", "Docker", "Singularity", "Other"], 616 specialOptions: ["Ancient DNA identification"], 617 update: ["2025"], 618 license: ["MIT License"], 619 category: ["Hybrid"], 620 citations: ["57"] 621 }, 622 description: "nf-core/mag is a robust and reproducible pipeline developed within the nf-core framework for the assembly and binning of metagenomes, supporting both short-read and hybrid data. Built using Nextflow, it leverages modular design and containerization (Docker/Singularity), ensuring portability across different computing environments, including HPC and cloud systems.", 623 url: "https://nf-co.re/mag", 624 details: "nf-core/mag is a Nextflow pipeline developed following the nf-core guidelines that ensures robustness and reproducibility. It supports both short-read and long-read sequences, as well as hybrid datasets, and it leverages a modular design, containerization (Docker, Singularity, among others) and package managers (Conda) to confer portability across different computing environments, including HPC and cloud systems. Beyond these important features, as part of the workflow orchestration, nf-core/mag can handle multi-sample input, it can be restarted if it is interrupted at any point thanks to its native checkpoint control and different assembly/binning modes can be selected. This pipeline encompasses tools for quality control of the reads (Porechop, Filtlong, NanoPack2, fastp), host removal (Bowtie2), adapter trimming (AdapterRemoval), and several assemblers (MEGAHIT, metaSPAdes, Flye, metaMDBG, hybridSPAdes). In addition, it offers three binning software options (MetaBAT2, MaxBin2 and CONCOCT) along with an optional refinement tool (DASTool). nf-core/mag checks assembly and bin quality through several tools that include CheckM2, MetaQUAST, BUSCO and GUNC, and for genome annotation, it uses GTDB-Tk2 or CAT (taxonomic) and Prokka or MetaEuk (functional). As special features, this pipeline can carry out a taxonomic annotation of the sequences (Kraken2 and Centrifuge), validates the presence of typical ancient DNA damages (PyDamage), attempts MAG domain classification with Tiara and identifies viruses after assembly with geNomad. After workflow execution, nf-core/mag generates detailed multi-sample summaries through MultiQC, and it creates HTML reports to track resource usage. Finally, the nf-core framework is actively maintained and updated as it relies on a numerous and enthusiastic developing community.", 625 category: "Hybrid" 626 }, 627 { 628 id: "pipeline27", 629 name: "ngs-preprocess-MpGAp-Bacannot", 630 attributes: { 631 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads", "Hybrid"], 632 multiSample: ["Yes"], 633 coAssemblyCoBinning: ["No"], 634 GUI: ["No"], 635 Cloud: ["Yes"], 636 workflowManager: ["Nextflow"], 637 binRefinement: ["No"], 638 externalComputationalResources: ["No"], 639 executionOptions: ["Conda"], 640 specialOptions: ["Plasmid assembly", "Antimicrobial resistance gene prediction", "Virulence factor annotation"], 641 update: ["2025"], 642 license: ["GNU GPL v3"], 643 category: ["Hybrid"], 644 citations: ["2"] 645 }, 646 description: "These pipelines consist of three parts: quality control, de novo genome assembly, and bacterial genome annotation. In particular, the genome annotation pipeline provides a comprehensive overview of the genome, including standard gene prediction and functional inference, as well as predictions relevant to clinical applications.", 647 url: "https://f1000research.com/articles/12-1205/v1", 648 details: "Ngs-preprocess, MpGAP and Bacannot are a series of Nextflow-based and container-powered pipelines designed to achieve a wide variety of specific tasks. ngs-preprocess performs several quality-control steps required for Next-Generation Sequencing (NGS) data asse
648ssment, while MPGAP supports de novo genome assembly from Illumina, PacBio, and ONT reads, enabling short-read, long-read, and hybrid assemblies using tools like metaSPAdes, metaFlye, Canu, and Unicycler, followed by polishing and quality assessment. Meanwhile, Bacannot provides an annotation workflow that incorporates gene prediction, rRNA detection, sequence typing, KEGG-based metabolic reconstruction, and secondary metabolite identification, integrating tools such as Prokka, Bakta, Barrnap, MLST, KofamScan, KEGGDecoder, and antiSMASH. As an additional analytical procedure, Bacannot incorporates additional support for methylation analysis via Nanopolish. Noticeably, this set of pipelines do not include at any point neither contig binning nor bin quality assessment; however, the smooth interconnection among the pipelines makes them an interesting option for metagenome assembly and annotation, boosted by the native benefits conferred by Nextflow and container technology.", 649 category: "Hybrid" 650 }, 651 { 652 id: "pipeline28", 653 name: "SnakeMAGs", 654 attributes: { 655 readTypes: ["Short reads"], 656 multiSample: ["Yes"], 657 coAssemblyCoBinning: ["No"], 658 GUI: ["No"], 659 Cloud: ["Yes"], 660 workflowManager: ["Snakemake"], 661 binRefinement: ["No"], 662 externalComputationalResources: ["No"], 663 executionOptions: ["Conda"], 664 specialOptions: [], 665 update: ["2024"],
666 license: ["CeCILL Free Software License Agreement v2.1"], 667 category: ["Short-read centered"], 668 citations: ["6"] 669 }, 670 description: "SnakeMAGs is a Snakemake-powered, fully automated pipeline designed for the end-to-end reconstruction and annotation of MAGs from metagenomic datasets, with native support for short-read Illumina data. SnakeMAGs emphasizes reproducibility, scalability, and transparency, offering out-of-the-box compatibility with HPC systems and Conda environments.", 671 url: "https://f1000research.com/articles/11-1522", 672 details: "SnakeMAGs is a simple yet useful pipeline that as its name indicates is controlled by a Snakemake wrapper with Conda as software administrator. It integrates basic modules starting with quality control with Illumina-utils and Trimmomatic, and if required, host removal with Bowtie2. Afterwards, the reads are assembled through MEGAHIT, the contigs are binned by MetaBAT2, a quality assessment is carried out with CheckM v1.1 and GUNC, MAG abundances are obtained using CoverM, and finally the taxonomic classification is performed using GTDB-Tk2. Similar to others pipelines governed by Snakemake, SnakeMAGs eases automation, reproducibility, scalability and workflow management.", 673 category: "Short-read centered" 674 }, 675 { 676 id: "pipeline29", 677 name: "SqueezeMeta", 678 attributes: { 679 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads", "Hybrid"], 680 multiSample: ["Yes"], 681 coAssemblyCoBinning: ["Yes"], 682 GUI: ["No"], 683 Cloud: ["No"], 684 workflowManager: [], 685 binRefinement: ["Yes"], 686 externalComputationalResources: ["No"], 687 executionOptions: ["Conda"], 688 specialOptions: ["Taxonomic profiling", "Metatranscriptome support", "Visualization module"], 689 update: ["2025"], 690 license: ["GNU GPL v3"], 691 category: ["Hybrid"], 692 citations: ["400"] 693 }, 694 description: "SqueezeMeta is a comprehensive and user-friendly bioinformatics pipeline designed for the assembly, annotation, and analysis of metagenomic and metatranscriptomic datasets. It streamlines the entire process, starting from raw reads and progressing through quality control, assembly (using multiple assemblers), gene prediction, taxonomic and functional annotation.", 695 url: "https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.03349/full", 696 details: "SqueezeMeta is a fully automatic pipeline written in Perl scripts that relies on Conda for software execution. As special features, this pipeline can handle short and long reads (ONT and Hi-Fi) in both single or hybrid approaches, supports for de-novo metatranscriptome assembly and hybrid metagenomics/metatranscriptomics analysis, carries out taxonomic annotation of unassembled reads, and empowers the user with a GUI application for downstream analysis. Also, SqueezeMetaâs flexibility enables different assembly modes such as sequential (samples assembled individually), co-assembly (samples assembled ensemble), merged (samples assembled individually with a posterior pooling) and seqmerge (similar to merged with a guided pooling based on assembly similarity). This pipeline follows the traditional workflow by applying quality filtering and trimming with Trimmomatic, then the reads are assembled by MEGAHIT and SPAdes (rnaSPAdes, Canu and metaFlye are run if transcriptomics or long read data are provided) to be binned afterwards with MaxBin2, MetaBAT2 and CONCOCT; DASTool is in charge of bin refinement. MAG Quality checks are established through CheckM2, and optionally taxonomic classification is achieved by GTDB-Tk2. To complement MAG annotation with KEGG and MetaCyc, SqueezeMeta analyzes the assembly by performing a homology searching against taxonomic and functional databases, an Hmmer search against Pfam database, and an estimation of taxa and function abundances. An important remark of this pipeline is its numerous and helpful developing and maintaining community.", 697 category: "Hybrid" 698 }, 699 { 700 id: "pipeline30", 701 name: "Sunbeam", 702 attributes: { 703 readTypes: ["Short reads"], 704 multiSample: ["Yes"], 705 coAssemblyCoBinning: ["No"], 706 GUI: ["No"], 707 Cloud: ["No"], 708 workflowManager: ["Snakemake"], 709 binRefinement: ["No"], 710 externalComputationalResources: ["No"], 711 executionOptions: ["Conda", "Docker"], 712 specialOptions: ["Taxonomic profiling"], 713 update: ["2024"], 714 license: ["GNU GPL v3"], 715 category: ["Short-read centered"], 716 citations: ["184"] 717 }, 718 description: "Sunbeam is a modular and extensible bioinformatics pipeline built using Snakemake for the analysis of metagenomic sequencing experiments. It automates various steps, starting from raw reads and including quality control (adapter trimming, host read removal, quality filtering), taxonomic assignment of reads (using Kraken), de novo assembly of reads
718into contigs (using Megahit), and contig annotation (using BLAST and Diamond).", 719 url: "https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-019-0658-x", 720 details: "Sunbeam is a modular pipeline orchestrated by Snakemake with Conda as dependency manager; this configuration makes Sunbeam analysis reliable, reproducible and scalable. The main feature Sunbeam depicts is its modularized and extensible design that allows users to build off the core functionality. The execution backbone of Sunbeam is represented by an initial quality control that encloses adapter trimming, host read removal and low-complexity filtering (Trimmomatic, FastQC, BWA and Komplexity), followed the assembly of reads into contigs with MEGAHIT along with their corresponding annotation with Prodigal, BLAST and Diamond (with nucleotide or protein databases). As complementary procedures, Sunbeam maps the reads to reference genomes (user pre-specified) and delivers a taxonomic assignment of the clean reads using Kraken v1.0. Its modularization and ready-to-use templates to create new modules have enabled the development of additional extensions for assigning metagenomic reads to a full bacterial phylogeny, single genome assembly, among others.", 721 category: "Short-read centered" 722 }, 723 { 724 id: "pipeline31", 725 name: "VEBA", 726 attributes: { 727 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads"], 728 multiSample: ["Yes"], 729 coAssemblyCoBinning: ["Yes"], 730 GUI: ["No"], 731 Cloud: ["No"], 732 workflowManager: ["GenoPype"], 733 binRefinement: ["No"], 734 externalComputationalResources: ["No"], 735 executionOptions: ["Conda"], 736 specialOptions: ["Eukaryotic MAGs", "Viral MAGs", "Antimicrobial resistance gene prediction", "Virulence factor annotation"], 737 update: ["2024"], 738 license: ["GNU AGPL v3"], 739 category: ["Dual"], 740 citations: ["23"] 741 }, 742 description: "VEBA is a modular, end-to-end metagenomics pipeline designed to recover and analyze genomes from prokaryotic, microeukaryotic, and viral organisms. It employs an iterative binning strategy and supports both single- and multi-sample workflows, facilitating comprehensive genome recovery across diverse microbial communities. VEBA integrates quality assessment, taxonomic classification, and dereplication, providing a unified framework for genome-resolved metagenomic analyses.", 743 url: "https://academic.oup.com/nar/article/52/14/e63/7697622", 744 details: "VEBA (Viral Eukaryotic Bacterial Archaeal) is a Conda-executed pipeline designed that enables the recovery and classification of genomes from all domains of life including archaeas, prokaryotes, microeukaryotes, and viruses. It starts with a common short read-preprocessing and assembly from which the process is bifurcated for prokaryotic and viral binning; unbinned contigs from the viral module are reincorporated into the prokaryotic contig set. Residual contigs from the prokaryotic module are then considered for eukaryotic MAG generation to proceed with the annotation and classification covering the genomes obtained in each module. Hence, several databases are considered at this step such as KOfam, Pfam and NCBI non-redundant. Also, a joint phylogeny is obtained based on MAG-gene models and lineage marker detection. An interesting approach VEBA follows is represented by the module coverage.py that collects all the unbinned contigs, from viral, eukaryotic and prokaryotic steps, to pursue a pseudo-coassembly, where iteratively the reference fasta (built from the contigs) and the sorted BAM files used as a final pass through prokaryotic and eukaryotic binning modules. Notably, it automates the detection of candidate phyla radiation (CPR) bacteria and integrates a consensus microeukaryotic database to optimize gene modeling and taxonomic classification.", 745 category: "Dual" 746 }, 747 { 748 id: "pipeline32", 749 name: "BugBuster", 750 attributes: { 751 readTypes: ["Short reads"], 752 multiSample: ["Yes"], 753 coAssemblyCoBinning: ["No"], 754 GUI: ["No"], 755 Cloud: ["Yes"], 756 workflowManager: ["Nextflow"], 757 binRefinement: ["Yes"], 758 externalComputationalResources: ["No"], 759 executionOptions: ["Docker"], 760 specialOptions: ["Taxonomic profiling", "Antimicrobial resistance gene prediction"], 761 update: ["2024"], 762 license: [], 763 category: ["Short-read centered"], 764 citations: ["0"] 765 }, 766 description: "BugBuster, a fully automated workflow for metagenomic data processing that covers all stages of analysis, from initial quality control to resistome detection and characterization. BugBuster was developed in Nextflow using the DSL2 syntax, offering a modular and flexible structure.", 767 url: "https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbaf152/8174904", 768 details: "BugBuster is an automatic, modular, and reproducible Nextflow (DSL2) workflow with specialized modules for taxonomic profiling and resistome characterization. Its workflow encompasses the following steps: initial reads processing for quality filtering and host contamination removal (Bowtie2); taxonomic profiling at the read level using tools like Kraken2/Bracken or Sourmash; and antibiotic resistance gene (ARG) prediction from reads using KARGA and KARGVA. The assembly is carried out with MEGAHIT, followed by taxonomic and functional annotation of contigs using BLAST, BlobTools, DeepARG, and MetaCerberus. Afterwards, the contigs are binned with tools such as MetaBAT2, SemiBin2 and COMEBin, and refined them with a MetaWRAP-native module;
768 the quality is assessed with CheckM2, and the MAGs are taxonomically affiliated with GTDB-Tk2. BugBuster is fully containerized (Docker) aiming at ensuring ease of installation, high reproducibility, and deployment across various computational environments. Moreover, BugBuster stands out given its inclusion of specific tools to characterize and quantify genes associated with antibiotic resistance.", 769 category: "Short-read centered" 770 }, 771 { 772 id: "pipeline33", 773 name: "MG-TK", 774 attributes: { 775 readTypes: ["Short reads"], 776 multiSample: ["Yes"], 777 coAssemblyCoBinning: ["Yes"], 778 GUI: ["No"], 779 Cloud: ["No"], 780 workflowManager: [], 781 binRefinement: ["No"], 782 externalComputationalResources: ["No"], 783 executionOptions: ["Conda"], 784 specialOptions: ["Taxonomic profiling", "Strain delineation"], 785 update: ["2025"], 786 license: ["GNU GPL v2"], 787 category: ["Dual"], 788 citations: ["99"] 789 }, 790 description: "MG-TK is a bioinformatics pipeline designed to assemble metagenomes, profile miTags, taxonomy, and functions, and to build MAGs. It creates gene catalogs, generates abundance matrices, and annotates genes. MG-TK can also merge MAGs into metagenomic species (MGS), construct inter- and intra-species phylogenies, and perform population genetic analyses. It supports automated phylogenetic tree building and is implemented in Perl, R, and Python.", 791 url: "https://github.com/hildebra/mg-tk", 792 details: "MG-TK (Metagenomic Toolkit) performs read assembly (SPAdes, MEGAHIT, Flye, metaMDBG) and binning (MetaBAT2, SemiBin, MetaDecoder), gene prediction, and clustering into nonredundant gene catalogs, followed by abundance estimation and functional annotation. It is structured around three main phases: processing raw sequences, building a gene catalog, and reconstructing species from MAGs with downstream phylogenetic analyses. It produces a wide range of outputs, including assemblies, MAGs, gene predictions, SNP calls and mapping outputs. A special remark MG-TK exhibits is its ability to generate detailed abundance matrices for both taxonomic and functional features, with hierarchical summaries available at multiple levels. The taxonomic profiles are reported using GTDB lineages, while functional annotations are provided for major databases such as KEGG, SEED, CAZy, eggNOG31, and TCDB. MG-TK also estimates completeness of functional modules, such as KEGG pathways, and links genes to multiple annotations for deeper exploration by the user. Beyond gene catalogs, MG-TK integrates MAG/MGS (Metagenomics Species) information, associating MAGs with their metagenomic species and providing detailed gene content, including representative MAGs for each species. Additionally, MG-TK can provide assembly-independent profiles via a wide variety of tools including riboFinder, MetaPhlAn33 and mOTUs.", 793 category: "Dual" 794 }, 795 { 796 id: "pipeline34", 797 name: "MAGO", 798 attributes: { 799 readTypes: ["Short reads"], 800 multiSample: ["No"], 801 coAssemblyCoBinning: ["No"], 802 GUI: ["No"], 803 Cloud: ["No"], 804 workflowManager: [], 805 binRefinement: ["Yes"], 806 externalComputationalResources: ["No"], 807 executionOptions: ["Singularity, Docker"], 808 specialOptions: ["Phylogenetic tree generation", "Pangenome analysis", "ANI calculation for MAG de-replication"], 809 update: ["2020"],
810 license: ["Creative Commons BY 4.0"], 811 category: ["Short-read centered"], 812 citations: ["21"] 813 }, 814 description: "MAGO is an end-to-end metagenomics pipeline packaged for execution. It streamlines the entire workflow, handling error checking and efficient resource distribution. The pipeline includes read quality control and host removal, followed by assembly. MAGO performs binning using multiple tools and refines bins with DASTool. CheckM is used for MAG quality assessment, after which high-quality MAGs are annotated and classified taxonomically. MAGO also supports phylogenetic tree generation, pangenome analysis , and ANI calculation for MAG de-replication.", 815 url: "https://academic.oup.com/mbe/article/37/2/593/5601623", 816 details: "MAGO is an end-to-end pipeline designed to run over a single execution from a container image (Singularity or Docker); a third option is available as a Virtual Machine (VM). This configuration allows MAGO to offer a streamlined implementation of the entire metagenomics pipeline, including error checking, and computational resource distribution. The tool workflow follows the traditional design with read quality control (fastp, FastQ), followed by the assembly step with MEGAHIT, metaSPAdes and/or IBDA-UD. MAGO performs binning through multiple algorithms (MetaBAT, MaxBin2, CONCOCT and BinSanity with multiple configurations). MAG completeness and contamination of MAGs are estimated with CheckM. To conclude the execution, MAGO annotates the MAGs with Prokka, and performs taxonomic classification and phylogenetic placement using GTDB-Tk. Moreover, to expand its capabilities, the developers included the possibility of generating phylogenetic trees through ezTree, analyzing the pangenome with Roary and measuring ANI with FastANI as an approximation to de-replicate the MAG set.", 817 category: "Short-read centered" 818 }, 819 { 820 id: "pipeline35", 821 name: "IMG/M", 822 attributes: { 823 readTypes: [], 824 multiSample: ["No"], 825 coAssemblyCoBinning: ["No"], 826 GUI: ["Yes"], 827 Cloud: ["No"], 828 workflowManager: [], 829 binRefinement: ["No"], 830 externalComputationalResources: ["Yes"], 831 executionOptions: [], 832 specialOptions: ["Eukaryotic MAGs"], 833 update: ["2025"], 834 license: ["IMG Expert Review Submission Agreement"], 835 category: ["Web-based"], 836 citations: ["268"] 837 }, 838 description: "IMG/M (Integrated Microbial Genomes & Microbiomes) is a JGI platform for annotation and comparative analysis of microbial genomes, metagenomes, and MAGs. It provides taxonomic and functional annotation (KEGG, COG, Pfam, CAZy), pathway exploration, and comparative tools. Submitted datasets are private during embargo but must eventually become public.", 839 url: "https://academic.oup.com/nar/article/51/D1/D723/6830671", 840 details: "IMG/M (Integrated Microbial Genomes & Microbiomes) developed by the DOE (the United States Department Of Energy) Joint Genome Institute for the annotation and comparative analysis of microbial genomes and metagenomes. IMG/M is designed primarily to host and annotate genomes, offering a pipeline, running on their servers, that takes contigs to bin them via SemiBin2, with subsequent quality control by CheckM. The taxonomic annotation is given by GTDB-Tk, and functional annotation is supported using resources such as KEGG, COGs, Pfam and TIGRFAMs, enabling pathway reconstruction and metabolic profiling; as inferred from this workflow description, the users need to perform the assemble step elsewhere. This platform also incorporates comparative tools to allow exploration of gene content, pathway coverage, phylogenetic profiles, and functional similarities across datasets. It is important to mention that datasets submitted to IMG/M are initially private but must eventually become public. IMG/M enforces an embargo period, after which annotated data are released and cannot be withdrawn, although updates are allowed.",
841 category: "Web-based" 842 }, 843 { 844 id: "pipeline36", 845 name: "WGSA2+/LoRA", 846 attributes: { 847 readTypes: ["Short reads", "Hi-Fi (PacBio) reads", "Oxford Nanopore (ONT) reads"], 848 multiSample: ["Yes"], 849 coAssemblyCoBinning: ["No"], 850 GUI: ["Yes"], 851 Cloud: ["Yes"], 852 workflowManager: ["AWS environment"], 853 binRefinement: ["No"], 854 externalComputationalResources: ["Yes"], 855 executionOptions: [], 856 specialOptions: ["Visualization module", "Metatranscriptome support", "Antimicrobial resistance gene prediction"], 857 update: ["2025"], 858 license: ["CC0 1.0 Universal"], 859 category: ["Web-based"], 860 citations: ["138"] 861 }, 862 description: "WGSA2+ and LoRA are Nepheleâs automated pipelines for short- and long-read metagenomic analysis. They integrate tools such as Kraken2, metaSPAdes, and metaFlye to perform quality control, assembly, taxonomic classification, and functional annotation, providing comprehensive and standardized insights into microbial community structure and metabolic potential through an intuitive cloud-based interface.", 863 url: "https://nephele.niaid.nih.gov/user-guide/about-pipelines#analyze-shotgun", 864 details: "The Nephele suite offers two independent metagenomics analysis pipelines: WGSA2+ for short-read data and LoRA to handle PacBio or ONT reads. Briefly, WGSA2+ performs quality control and host removal using tools such as fastp and Kraken2, assembles reads with metaSPAdes, and optionally bins contigs into MAGs with MetaBAT2, assessing MAG quality with CheckM. Taxonomic classification is achieved through Kraken2, whilst eggNOG-mapper is in charge of functional annotation. LoRA, on its side, uses metaFlye for assembly, integrates the same binning and functional annotation tools, and expands the classification module with inclusion of GTDB-Tk2 and CheckM2. Both pipelines are able to generate taxonomic profiles, functional summaries and detect antibiotic resistance genes through Nepheleâs user-friendly cloud interface; WGSA2+ supports metatranscriptome assembly from RNA-seq data. As a result, WGSA2+/LoRA represent a great option for users who are experienced at command line tool execution or with limited local computing resources. However, Nepheleâs platform usage is limited as it relies on AWS for software execution, and therefore users receive a fixed number of use codes , and in case of intensive resource demands, they can request extended access.", 865 category: "Web-based" 866 }, 867 { 868 id: "pipeline37", 869 name: "JAMS", 870 attributes: { 871 readTypes: ["Short reads"], 872 multiSample: ["No"], 873 coAssemblyCoBinning: ["No"], 874 GUI: ["No"], 875 Cloud: ["No"], 876 workflowManager: [], 877 binRefinement: ["No"], 878 externalComputationalResources: ["No"], 879 executionOptions: ["Conda"], 880 specialOptions: ["Direct sample comparison"], 881 update: ["2025"], 882 license: ["GNU GPL v3"], 883 category: ["Short-read centered"], 884 citations: ["7"] 885 }, 886 description: "JAMS (Just a Microbiology System) is an integrated framework for taxonomic and functional microbiome analysis. Combining tools like Kraken2, Prokka, and InterProScan, it performs single and cross-sample analyses. JAMS offers automated, reproducible workflows optimized for HPC environments.", 887 url: "https://www.biorxiv.org/content/10.1101/2023.03.03.531026v1.full", 888 details: "JAMS (Just a Microbiology System) is an integrated framework originally designed to perform the analysis on the NIHâs Biowulf system. JAMs is divided into two main modules: JAMSα, which performs single sample analyses, and JAMSβ, which focuses on cross-sample comparisons. JAMSα (the pipeline) integrates tools such as Bowtie2 for host removal, MEGAHIT or SPAdes for read assembly, Kraken2 for taxonomic classification, and Prokka and InterProScan for gene and protein domain prediction, respectively; JAMSβ uses R-based packages for visualization and statistical analysis. This workflow is executed within Conda environments, and its main advantage relies on the ease to establish comparisons across samples. However, this pipeline does not support binning tools nor genome-quality, and currently, it exhibits restricted deployment flexibility due to optimization for the NIHâs Biowulf system, although JAMS is open source and can be installed on any UNIX-based machine.", 889 category: "Short-read centered" 890 }, 891 { 892 id: "pipeline38", 893 name: "SPIRE", 894 attributes: { 895 readTypes: ["Short reads"], 896 multiSample: ["Yes"], 897 coAssemblyCoBinning: ["No"], 898 GUI: ["No"], 899 Cloud: ["Yes"], 900 workflowManager: ["Nextflow"], 901 binRefinement: ["No"], 902 externalComputationalResources: ["No"], 903 executionOptions: [], 904 specialOptions: ["Antimicrobial resistance gene prediction", "Virulence factor annotation"], 905 update: ["2025"], 906 license: ["MIT License"], 907 category: ["Short-read centered"], 908 citations: ["41"] 909 }, 910 description: "The SPIRE projectâs Nextflow-based pipeline enables large-scale, reproducible metagenomic analysis by integrating tools like NGless, MEGAHIT, Prodigal, MetaBAT2, CheckM2, GTDB-Tk, and eggNOG-mapper. It automates quality control, assembly, annotation, and genome reconstruction, delivering standardized, high-throughput insights into microbial diversity, function, and genomic organization across global datasets.", 911 url: "https://academic.oup.com/nar/article/52/D1/D777/7332059", 912 details: "The SPIRE project employs a Nextflow-based pipeline that has been used to process and annotate more than 100,000 metagenomes belonging to more than 700 studies. The workflow incorporates tools such as NGLess for read trimming and decontamination, MEGAHIT for assembly, Prodigal for gene prediction and barrnap for RNA detection. Moreover, contig binning is carried out with MetaBAT2 with a complementary genome quality assessment using CheckM2 and GUNC, and the workflow ends with taxonomic classification (GTDB-Tk2) and functional annotation (eggNOG-mapper, abricate, RGI and Macrel). Among the advantages SPIRE offers, the possibility to perform antimicrobial resistance gene prediction and the annotation of virulence factors stand out, as well as its scalability, reproducibility across high-performance and cloud environments, and standardized processing, enabling consistent comparisons across global datasets. Nonetheless, at the moment of writing this report, this pipeline is aiming to be executed at online platforms like CloWM as it is lacking defined environments or container images, and the input data should be already hosted at the sequencing archives such as ENA, DDBJ or SRA.", 913 category: "Short-read centered" 914 }, 915 { 916 id: "pipeline39", 917 name: "EURYALE (MEDUSA)", 918 attributes: { 919 readTypes: ["Short reads"], 920 multiSample: ["Yes"], 921 coAssemblyCoBinning: ["No"], 922 GUI: ["No"], 923 Cloud: ["Yes"], 924 workflowManager: ["Nextflow"], 925 binRefinement: ["No"], 926 externalComputationalResources: ["No"], 927 executionOptions: ["Conda", "Docker", "Singularity"], 928 specialOptions: [], 929 update: ["2024"], 930 license: ["MIT License"], 931 category: ["Short-read centered"], 932 citations: ["7"] 933 }, 934 description: "EURYALE is a Nextflow-based reimplementation of the MEDUSA pipeline for metagenomic analysis. It integrates tools like FastQC, fastp, Bowtie2, Kaiju, Kraken2, and DIAMOND for quality control, classification, and annotation. Containerized and modular, it ensures scalable, reproducible, and high-throughput workflows for c
934omprehensive microbial community characterization.", 935 url: "https://ieeexplore.ieee.org/document/10702116", 936 details: "EURYALE is a Nextflow-based reimplementation of the MEDUSA pipeline. It provides a modular and containerized workflow using Nextflow DSL2, with software execution through Docker, Conda or Singularity, which ensures portability, reproducibility, and scalability. The workflow of this pipeline starts with read quality control with FastQC, trimming and merging using fastp, and optional host decontamination with Bowtie2; MultiQC provides a full report containing visualizations regarding sequence preprocessing. Optionally, clean sequences can be assembled using MEGAHIT with a posterior taxonomic classification carried out by Kaiju or Kraken2, while functional annotation relies on a DIAMOND-based alignment to reference databases (NCBi nr by default). It is worthy to mention the flexibility EURYALE offers given its customizable database selection for both taxonomic and functional annotation.", 937 category: "Short-read centered" 938 }, 939 { 940 id: "pipeline40", 941 name: "nIMP3", 942 attributes: { 943 readTypes: ["Short reads"], 944 multiSample: ["Yes"], 945 coAssemblyCoBinning: ["No"], 946 GUI: ["No"], 947 Cloud: ["Yes"], 948 workflowManager: ["Nextflow"], 949 binRefinement: ["No"], 950 externalComputationalResources: ["No"], 951 executionOptions: ["Docker", "Singularity"], 952 specialOptions: ["Metatranscriptome support", "Taxonomic profiling"], 953 update: ["2024"], 954 license: ["MIT License"], 955 category: ["Short-read centered"], 956 citations: ["150"] 957 }, 958 description: "nIMP3 is a Nextflow-based reimplementation of the IMP pipeline for integrated metagenomic and metatranscriptomic analysis. It performs quality control, assembly, gene prediction, annotation, and expression quantification. Containerized and modular, it enables reproducible, scalable multi-omics workflows without genome binning.", 959 url: "https://github.com/grp-bork/nIMP3", 960 details: "nIMP3 is a Nextflow-based reimplementation of the IMP (Integrated Meta-omic Pipeline) workflow that assembles metagenomics (MG) and metatranscriptomics (MT) datasets together. nIMP3 handles preprocessed and contaminant-free MT and MG reads (FastQC, SortMeRNA, BBTools), and jointly assembles them in a hybrid and iterative process using MEGAHIT. Additionally, nIMP3 performs taxonomic profiling with mOTUs and Kraken2, as well as functional profiling with gffquant. Unlike the original IMP pipeline, nMP3 does not include a binning module, and thus it cannot recover MAGs. Nonetheless, nIMP3 offers a lighter, reproducible, and integrative pipeline for multi-omics metagenome/metatranscriptome processing.", 961 category: "Short-read centered" 962 }, 963 { 964 id: "pipeline41", 965 name: "Mapler", 966 attributes: { 967 readTypes: ["Hi-Fi (PacBio) reads"], 968 multiSample: ["Yes"], 969 coAssemblyCoBinning: ["No"], 970 GUI: ["No"], 971 Cloud: ["Yes"], 972 workflowManager: ["Snakemake"], 973 binRefinement: ["No"], 974 externalComputationalResources: ["No"], 975 executionOptions: ["Conda"], 976 specialOptions: ["Visualization module"], 977 update: ["2025"], 978 license: ["GNU AGPL v3"], 979 category: ["Long-read focused"], 980 citations: ["0"] 981 }, 982 description: "Mapler is a Snakemake-based pipeline for evaluating PacBio HiFi metagenome assemblies. It integrates tools like metaMDBG, hifiasm-meta, MetaBAT2, GTDB-Tk, CheckM2, and Minimap2 to assess assembly quality, completeness, and taxonomic coverage, combining read-to-contig alignment metrics and bin evaluation to reveal assembly performance and unassembled diversity.", 983 url: "https://academic.oup.com/bioinformatics/article/41/6/btaf334/8157874", 984 details: "Mapler is a pipeline specifically designed to handle PacBio HiFi long reads. Mapler workflow is orchestrated by Snakemake along with Conda for package management, enabling scalable execution on local or cluster systems. Regarding the specific tools encompassed by Mapler, state-of-the-art assemblers such as metaMDBG, hifiasm-meta, metaFlye and OPE
984RA-MS are available, with MetaBAT2 as the binning tool. Later on the workflow, each bin is classified taxonomically via GTDB-Tk or Kraken, and genome quality is evaluated using CheckM2 standards. Mapler aligns reads back to contigs with Minimap to compute novel metrics including the aligned read percentage and aligned base percentage, stratified across quality categories. It is important to mention that Mapler accepts assemblies and bins as input to skip part of the process, and it includes a parallel analysis, where assembled versus unassembled reads are contrasted by evaluating k-mer distributions (KAT), read quality (FastQC), and taxonomic composition (Kraken2 + Krona). As a result, by combining classic bin-based metrics with read-to-contig alignment statistics, Mapler assists in estimating how much of the sequence diversity remains uncaptured.", 985 category: "Long-read focused" 986 }, 987 { 988 id: "pipeline42", 989 name: "ont-assembly-snake/score-assemblies", 990 attributes: { 991 readTypes: ["Short reads", "Oxford Nanopore (ONT) reads"], 992 multiSample: ["Yes"], 993 coAssemblyCoBinning: ["No"], 994 GUI: ["No"], 995 Cloud: ["Yes"], 996 workflowManager: ["Snakemake"], 997 binRefinement: ["No"], 998 externalComputationalResources: ["No"], 999 executionOptions: ["Conda"], 1000 specialOptions: ["Visualization module"], 1001 update: ["2024"], 1002 license: ["MIT License"], 1003 category: ["Long-read focused"], 1004 citations: ["1"] 1005 }, 1006 description: "ont-assembly-snake and score-assemblies are complementary Snakemake pipelines for Oxford Nanopore bacterial genome assembly and evaluation. They integrate tools like Flye, Raven, Racon, Medaka, QUAST, BUSCO, and DIAMOND for assembly, polishing, and quality assessment, offering modular, reproducible, and containerized workflows for generating and benchmarking high-quality bacterial genome assemblies.", 1007 url: "https://gigabytejournal.com/articles/116", 1008 details: "ont-assembly-snake and score-assemblies are two complementary Snakemake workflows specifically designed to handle ONT sequencing data. Regarding their workflows, ont-assembly-snake orchestrates read filtering (Filtlong, Rasusa), long-read assembly (Raven, Flye, Miniasm, Canu) or hybrid (Unicycler), and iterative polishing (Racon, Medaka, Pilon, Polypolish, POLCA, Homopolish, Proovframe). Meanwhile, score-assemblies takes the output from ont-assembly-snake to perform quality assessment and annotation through a wide variety of tools: QUAST for contiguity and alignment comparisons, BUSCO for core gene completeness, DNAdiff/NucDiff for genomic differences, Pomoxis for Q-score and error estimates, and an IDEEL module that predicts ORFs (Prodigal) and aligns them to reference proteins (DIAMOND) for assessing gene prediction quality. Optionally, annotation can be carried out with Bakta, and score-assemblies will produce summary tables, plots, and an HTML report. The software is executed through Conda in both pipelines, and its Snakemake wrapping ensures modular reproducibility and flexibility in choosing assembly or evaluation strategies. These pipelines can be used in metagenomics scenarios, although their performance and scalability in large metagenomic or eukaryotic contexts may be more constrained as they were thought primarily for bacterial genome assembly (modest size).", 1009 category: "Long-read focused" 1010 }, 1011 { 1012 id: "pipeline43", 1013 name: "Bactopia", 1014 attributes: { 1015 readTypes: ["Short reads", "Oxford Nanopore (ONT) reads", "Hybrid"], 1016 multiSample: ["Yes"], 1017 coAssemblyCoBinning: ["No"], 1018 GUI: ["No"], 1019 Cloud: ["Yes"], 1020 workflowManager: ["Nextflow"], 1021 binRefinement: ["No"], 1022 externalComputationalResources: ["No"], 1023 executionOptions: ["Conda", "Docker", "Singularity"], 1024 specialOptions: ["Antimicrobial resistance gene prediction", "Pangenome analysis", "Taxonomic profiling", "Strain delineation"], 1025 update: ["2025"], 1026 license: ["MIT License"], 1027 category: ["Hybrid"], 1028 citations: ["197"] 1029 },
1030 description: "Bactopia is a modular, end-to-end pipeline for bacterial and metagenomic analysis built with Nextflow, offering scalability and reproducibility through Conda, Docker, or Singularity. It includes eight core stepsâfrom data gathering and quality control to annotation, resistance detection, and species-specific analysesâand a suite of Bactopia Tools for advanced comparative genomics.", 1031 url: "https://journals.asm.org/doi/10.1128/msystems.00190-20", 1032 details: "Bactopia is a modular pipeline built with Nextflow, executed with either Conda, Docker, or Singularity. Its workflow is organized into eight main steps: Gather, which collects all input data; QC, performing quality control of the reads; Assembler, which builds contigs from the sequences; Annotator, adding functional and structural annotations; Sketcher, which generates sketches of the assemblies and queries reference databases; Sequence Typing, to identify sequence types; Antibiotic Resistance, to detect resistance genes at the contig and protein levels; and Merlin, an intelligent module that automatically executes species-specific tools based on genomic distance. Beyond these core stages, Bactopia has evolved into a comprehensive suite through its growing collection of Bactopia Tools, enabling advanced analyses such as comparative genomics, pangenome exploration, phylogenetics, ANI estimation, and taxonomic profiling. Its flexible architecture and automated execution make Bactopia a powerful framework for reproducible and large-scale bacterial genomics research, although it is important to keep in mind that Bactopia is thought in principle as a pipeline for single genome analysis.", 1033 category: "Hybrid" 1034 }, 1035 { 1036 id: "pipeline44", 1037 name: "Arcadia-Science/metagenomics", 1038 attributes: { 1039 readTypes: ["Short reads", "Oxford Nanopore (ONT) reads"], 1040 multiSample: ["Yes"], 1041 coAssemblyCoBinning: ["No"], 1042 GUI: ["No"], 1043 Cloud: ["Yes"], 1044 workflowManager: ["Nextflow"], 1045 binRefinement: ["No"], 1046 externalComputationalResources: ["No"], 1047 executionOptions: ["Conda", "Docker", "Singularity"], 1048 specialOptions: ["Taxonomic profiling"], 1049 update: ["2023"], 1050 license: ["MIT License"], 1051 category: ["Dual"], 1052 citations: ["Not found"] 1053 }, 1054 description: "Arcadia-Science/metagenomics performs common QC, processing, and profiling steps of metagenomes obtained through either Illumina or Nanopore technologies. The pipeline consists of two separate workflows for processing the Illumina and Nanopore data and producing assemblies. Therefore Illumina or Nanopore samples are processed separately, as this pipeline does not handle hybrid assembly or polishing with short reads.", 1055 url: "https://github.com/Arcadia-Science/metagenomics", 1056 details: "Arcadia-Science/metagenomics is a robust Nextflow-based workflow designed for profiling metagenomic samples generated with either short-read data or ONT long reads. The pipeline emphasises reproducibility and portability, enabled via containers (Docker or Singularity) or Conda environments. It implements two separate workflows for the assembly, meaning that Illumina or Nanopore samples are processed separately (via metaSPAdes or Flye/medaka), as this pipeline does not handle hybrid assembly or polishing with short reads. Following the assembly, a unified downstream steps for composition profiling and gene/protein prediction (Prodigal, Sourmash and DIAMOND) is triggered. The workflow builds on modules from the nf-core ecosystem wherever possible, and its modular architecture and container separation of processes facilitate maintenance and extendibility.", 1057 category: "Dual" 1058 }, 1059 { 1060 id: "pipeline45", 1061 name: "baargin", 1062 attributes: { 1063 readTypes: ["Short reads", "Hybrid"], 1064 multiSample: ["Yes"], 1065 coAssemblyCoBinning: ["No"], 1066 GUI: ["No"], 1067 Cloud: ["Yes"], 1068 workflowManager: ["Nextflow"], 1069 binRefinement: ["No"], 1070 externalComputationalResources: ["No"], 1071 executionOptions: ["Conda", "Docker", "Singularity"], 1072 specialOptions: ["Taxonomic profiling", "Antimicrobial resistance gene prediction", "Pangenome analysis"], 1073 update: ["2023"], 1074 license: ["MIT License"], 1075 category: ["Hybrid"], 1076 citations: ["5"] 1077 }, 1078 description: "baargin is a Nextflow-based pipeline for reproducible, high-throughput bacterial genomics with a focus on antimicrobial resistance. It processes raw sequencing data through quality control, assembly, taxonomic decontamination, AMR gene and mutation detection, annotation, and pangenome analysis, using containerized execution for portability and scalability.", 1079 url: "https://joss.theoj.org/papers/10.21105/joss.05397", 1080 details: "baargin (Bacterial Assembly and Antimicrobial Resistance Genes detection In Nextflow) is a Nextflow-based workflow that automates comprehensive bacterial genomics analysis with a strong emphasis on antimicrobial resistance (AMR) detection. It is designed to process multiple bacterial isolates, and it leverages containerization through Docker or Singularity. The workflow accepts raw short reads or hybrid short/long reads and performs quality control and adapter trimming, de novo assembly, and taxonomic assignment to decontaminate assemblies for the target species. Both raw and decontaminated contigs undergo evaluation for assembly quality before being subjected to sequence typing and plasmid identification. baargin detects AMR genes and resistance-conferring mutations using multiple curated databases and tools, enabling robust characterization of resistance determinants. Genome annotation is conducted on the decontaminated contigs, and once all isolates are processed, the workflow performs pangenome analysis, providing a foundation for downstream phylogenetic and comparative studies. While designed in principle for bacterial genomics datasets from multiple isolates, baargin is especially useful when targeting specific species related to AMR within a microbial community.", 1081 category: "Hybrid" 1082 }, 1083 { 1084 id: "pipeline46", 1085 name: "nf-core/metatdenovo", 1086 attributes: { 1087 readTypes: ["Short reads"], 1088 multiSample: ["Yes"], 1089 coAssemblyCoBinning: ["No"], 1090 GUI: ["No"], 1091 Cloud: ["Yes"], 1092 workflowManager: ["Nextflow"], 1093 binRefinement: ["No"], 1094 externalComputationalResources: ["No"], 1095 executionOptions: ["Conda", "Docker", "Singularity"], 1096 specialOptions: ["Metatranscriptome support"], 1097 update: ["2025"], 1098 license: ["MIT License"], 1099 category: ["Short-read centered"], 1100 citations: ["0"] 1101 },
1102 description: "nf-core/metatdenovo is a Nextflow-based pipeline for reproducible de novo analysis of metagenomic and metatranscriptomic data. It integrates quality control, assembly, gene prediction, quantification, and functional and taxonomic annotation using containerized, modular workflows, enabling scalable, reference-free characterization of complex microbial communities.", 1103 url: "https://peerj.com/articles/20328/", 1104 details: "nf-core/metatdenovo is a Nextflow-based pipeline providing a best-practice framework for de novo assembly and analysis of metagenomic and metatranscriptomic data. As most of the nf-core pipelines, metatdenovo uses Docker, Singularity, or Conda environments, and follows the strict nf-core guidelines for continuous testing, documentation, and standardized outputs. The workflow begins with read quality control and preprocessing using tools such as FastQC and fastp, followed by optional filtering and normalization. De novo assembly is performed with assemblers adapted to different data types, including MEGAHIT for both metagenomes and metatranscriptomes and rnaSPAdes for metatranscriptomes. Gene prediction is carried out using tools such as TransDecoder, Prodigal and Prokka. The pipeline integrates quantification of genes identified in assemblies with BBTools, and functional annotation is carried via Prokka, eggNOG-mapper, KofamScan and fast homology searches through HMMER; the taxonomic classification is performed with EUKulele and DIAMOND (user-provided database).", 1105 category: "Short-read centered" 1106 }, 1107 { 1108 id: "pipeline47", 1109 name: "mmlong2", 1110 attributes: { 1111 readTypes: ["Oxford Nanopore (ONT) reads", "Hi-Fi (PacBio) reads"], 1112 multiSample: ["Yes"], 1113 coAssemblyCoBinning: ["Yes"], 1114 GUI: ["No"], 1115 Cloud: ["Yes"], 1116 workflowManager: ["Snakemake"], 1117 binRefinement: ["Yes"], 1118 externalComputationalResources: ["No"], 1119 executionOptions: ["Conda", "Singularity"], 1120 specialOptions: ["Microdiversity assessment"], 1121 update: ["2025"], 1122 license: ["GNU GPL v3"], 1123 category: ["Long-read focused"], 1124 citations: ["3"] 1125 }, 1126 description: "mmlong2 is a Snakemake-based long-read metagenomics pipeline designed to recover high-quality MAGs from complex terrestrial environments. It integrates long-read assembly, polishing, eukaryotic contig filtering, and iterative ensemble binning to generate highly contiguous genomes, enabling large-scale discovery of novel microbial diversity.", 1127 url: "https://github.com/Serka-M/mmlong2", 1128 details: "mmlong2 is a custom long-read metagenomics workflow developed to maximize the MAG recovery from highly complex terrestrial environmental samples such as soil and sediments. The workflow integrates metagenome assembly (metaFlye), polishing (Medaka), and eukaryotic contig removal (Tiara), followed by binning, including differential coverage, ensemble binning with multiple binning tools (VAMB, SemiBin2, MetaBAT2, COMEBin), and iterative binning to improve genome recovery. mmlong2 also includes extraction of circular MAGs and multi-coverage support by incorporating mapping information from multiple samples, allowing more complete and contiguous genome reconstructions. The pipeline is implemented as a Snakemake workflow, making it modular and reproducible. Moreover, mmlong2 features variant detection in MAGs for microdiversity assessment (Longshot), and leverages Jellyfish and Cramino for read k-mer count and general contig statistics, respectively.", 1129 category: "Long-read focused" 1130 }, 1131 { 1132 id: "pipeline48", 1133 name: "metaMAG", 1134 attributes: { 1135 readTypes: ["Short reads"], 1136 multiSample: ["Yes"], 1137 coAssemblyCoBinning: ["Yes"], 1138 GUI: ["No"], 1139 Cloud: ["No"], 1140 workflowManager: [], 1141 binRefinement: ["Yes"], 1142 externalComputationalResources: ["No"], 1143 executionOptions: ["Conda"], 1144 specialOptions: ["Database augmentation"], 1145 update: ["2025"], 1146 license: ["MIT License"], 1147 category: ["Short-read centered"], 1148 citations: ["0"] 1149 }, 1150 description: "MetaMAG Explorer is a modular metagenomics pipeline offering end-to-e
1150nd preprocessing, assembly, binning, annotation, and publication-ready visualization. It automates the discovery of novel MAGs, dereplicates them against curated repositories, and dynamically augments Kraken2 classification databases. This unique framework streamlines complex workflows and enhances data interpretation for scientific papers.", 1151 url: "https://doi.org/10.64898/2026.04.27.721001", 1152 details: "MetaMAG orchestrates a modular, seven-phase metagenomic workflow optimized for high-performance computing clusters via SLURM. It initiates data preprocessing using FastQC and fastp for quality control and trimming, followed by host read removal through BWA-mem and SAMtools. For sequence assembly, this pipeline uses IDBA-UD for single-sample runs, MEGAHIT for co-assembly, and MetaQUAST for validation. Metagenome-assembled genomes (MAGs) are reconstructed via MetaWRAPâintegrating MetaBAT2, MaxBin2, and CONCOCTâbefore refinement with DAS Tool and quality assessment via CheckM2. To achieve database augmentation, this pipeline uses GTDB-Tk for taxonomic classification, identifying novel candidate genomes and dereplicating them against external repositories using dRep and fastANI to dynamically update a customized Kraken2 reference database. Downstream functional annotation is driven by Prodigal, eggNOG-mapper, and dbCAN3. Finally, this pipeline uses Kraken2 and Bracken for taxonomic abundance estimation, utilizing GTDB-Tk, ggtree, matplotlib, seaborn, plotly, and NetworkX to perform phylogenetic profiling, statistical analyses, and generate publication-ready figures.", 1153 category: "Short-read centered" 1154 }, 1155 { 1156 id: "pipeline49", 1157 name: "MAGFlow", 1158 attributes: { 1159 readTypes: ["Short reads"], 1160 multiSample: ["Yes"], 1161 coAssemblyCoBinning: ["No"], 1162 GUI: ["No"], 1163 Cloud: ["No"], 1164 workflowManager: ["Snakemake"], 1165 binRefinement: ["No"], 1166 externalComputationalResources: ["No"], 1167 executionOptions: ["Conda"], 1168 specialOptions: [], 1169 update: ["2022"], 1170 license: [], 1171 category: ["Short-read centered"], 1172 citations: ["0"] 1173 }, 1174 description: "MAGFlow is a streamlined program designed to facilitate the binning and assembly of metagenomes. It trims metagenomic reads, performs assembly with metaSPADES, bins contigs with metabat2, concoct, and maxbin2, generates consensus bins with DASTools, and evaluates bins with CheckM while enabling users to specify custom resource allocations for each task.", 1175 url: "https://github.com/thrash-lab/MAGFlow", 1176 details: "MAGFlow reconstructs microbial genomes from complex metagenomic samples through a series of sequential and parallel processing steps. Raw interleaved FASTQ files first undergo quality trimming with Sickle to remove low-quality bases and reads. The cleaned reads are then assembled into contigs using metaSPAdes, a de Bruijn graph-based assembler optimized for metagenomic data. To enable coverage-based binning, trimmed reads are mapped back to the assembled contigs using Bowtie2, with SAMtools handling BAM file conversion, sorting, and indexing. Coverage depth profiles are then calculated per contig using MetaBAT2's jgi_summarize_bam_contig_depths utility. Three complementary binning algorithms â MetaBAT2, CONCOCT, and MaxBin2 â run in parallel, each leveraging different combinations of tetranucleotide frequency and coverage information to cluster contigs into draft genomes (bins). Because each tool has distinct strengths and failure modes, their outputs are reconciled by DASTool, which identifies the highest-scoring non-redundant set of bins through a consensus approach. Finally, CheckM evaluates the quality of the consensus bins using lineage-specific marker genes, producing a summary table of completeness and contamination estimates for each recovered MAG.", 1177 category: "Short-read centered" 1178 }, 1179 { 1180 id: "pipeline50", 1181 name: "metaFUN", 1182 attributes: { 1183 readTypes: ["Short reads"], 1184 multiSample: ["Yes"], 1185 coAssemblyCoBinning: ["No"], 1186 GUI: ["No"], 1187 Cloud: ["Yes"], 1188 workflowManager: ["Nextflow"], 1189 binRefinement: ["Yes"], 1190 externalComputationalResources: ["No"], 1191 executionOptions: ["Singularity", "Conda"], 1192 specialOptions: ["Functional profiling", "Visualization module", "Virulence factor annotation", "Microdiversity assessment", "Pangenome analysis", "Comparative genomic analysis", "Antimicrobial resistance gene prediction"], 1193 update: ["2025"], 1194 license: ["MIT License"], 1195 category: ["Short-read centered"], 1196 citations: ["0"] 1197 },
1198 description: "metaFun is an open-source, end-to-end pipeline that integrates quality control, taxonomic profiling, functional profiling, de novo assembly, binning, genome assessment, comparative genomic analysis, pangenome annotation, network analysis, and strain-level microdiversity analysis into a unified framework. Interactive modules support standardized data interpretation and exploratory visualization. The pipeline is implemented with Nextflow and containerized with Apptainer, ensuring environment reproducibility and scalability. metaFUN incorporates optimized parameters derived from controlled benchmarks.", 1199 url: "https://doi.org/10.1080/19490976.2025.2611544", 1200 details: "metaFun integrates analyses of metagenomic reads and MAGs using the GTDB classification system. This workflow consists of seven analytical modules and four interactive analysis modules, processing short metagenomic sequences and assembled genomes with metadata. This pipeline can process quality control of raw metagenomic data, de novo assembly, binning, quality assessment and taxonomic assignment of genomes, genomic comparison, taxonomic and functional profiling of metagenomic reads, population microdiversity analysis, and network analysis. Interactive modules were designed for microbiome structure analysis, microbiome network analysis, strain-level diversity analysis, and comparative genomic analysis. All programs were containerized into Apptainer container images. A Conda environment was constructed and deposited into the Bioconda channel for easy installation and execution of the pipeline. Module components were selected based on performance evaluations in previous benchmark studies, update status, and computational efficiency. We evaluated various programs and parameters using simulated data, with focus on de novo assembly, taxonomic composition profiling of metagenomes, and comparative genomic analysis.", 1201 category: "Short-read centered" 1202 }, 1203];
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