awesome_microbiome

This repository serves as a continuously updated collection of algorithms, tools, databases, and tutorials for microbiome research.

Table of content

• Amplicon data analysis
  • Tools for amplicon
  • rRNA Databases
• End-to-end workflows for metagenomes and isolates
  • Workflows for metagenome
  • Workflows for isolate
• Microbial genomes resources
  • Prokaryotic genome
  • Phage genome
• Quality control of sequencing data
  • Reads simulation
  • Basecall
  • QC
  • Correct reads
  • Consensus sequence from long-reads
• Alignment and mapping
  • Short-read to sequence
  • Long-read to sequence
  • Sequence to sequence
  • Multi sequence alignment
  • Cluster sequence
• Metagenome assmeble
  • Short read only assembly
  • Long read only assmebly
  • Hybrid assembly
  • PB HiFi read assembly
  • Polish
  • Genome size estimation
  • Other elements assembly
  • Assembly improvement
• Binning
  • Tools and workflows
  • Strain-level resolve
  • MAG improvement
  • MAG assessment
  • Other types of genome recovery
• Gene and element prediction
  • ORF
  • Non-coding RNA
  • Signal peptides
  • Plasmid databases
• Taxonomy profile
  • Profile read
  • Profile contig
  • MAG taxonomy
• Annotation
  • Tools for annotation
  • Databases for annotation
  • Genome content analysis
  • Protein structure analysis
  • Elements
  • Bacteriophage
• Metabolic construction
  • Tools for metabolic analysis
  • Metabolic database
• Comparative genomics
  • AAI and ANI
  • View comparative map
  • HGT
  • SV
• Visualization
  • View MSA
  • View genome
  • View assemblies
• Phylogenetics
  • Build a tree
  • View tree
• Microbial diversity analysis
  • Abundance
  • Diversity
  • Network
  • Interactions
• Metatranscriptomics
  • RNA quantification
  • RNA assembly
  • RNA SV
  • Stats
• Surveillance
• Modifications
• Pangenome related

Amplicon data analysis

Tools for amplicon

• UPARSE - (v12.0-beta1, 2024.6)

  • Edgar R. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10, 996–998 (2013). https://doi.org/10.1038/nmeth.2604

• Mothur - (C++, v1.48.1, 2024.5)

  • Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl Environ Microbiol 75 (2009). https://doi.org/10.1128/AEM.01541-09

• dada2 - (R, 1.26, 2022.11)

  • Callahan B, McMurdie P, Rosen M, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583 (2016). https://doi.org/10.1038/nmeth.3869

• QIIME2 - (Java, 2024-05, 2024.5)

  • Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37, 852–857 (2019). https://doi.org/10.1038/s41587-019-0209-9

• RDP Classifier - (Java, v2.14, 2023.8)

  • Wang Q, Cole JR. Updated RDP taxonomy and RDP Classifier for more accurate taxonomic classification. Microbiol Resour Announc 13, e01063-23 (2024). https://doi.org/10.1128/mra.01063-23

• Tax4Fun1 - Tax4Fun2 - (R, v1.1.6, 2019.11)

  • Aßhauer KP, Wemheuer B, Daniel R, Meinicke P. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data, Bioinformatics 31(17), 2882–2884 (2015). https://doi.org/10.1093/bioinformatics/btv287
  • Wemheuer F, Taylor JA, Daniel R, et al. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environmental Microbiome 15(11) (2020). https://doi.org/10.1186/s40793-020-00358-7

• PICRUSt - (Python, v1.1.4, 2019.6)

  • Langille M, Zaneveld J, Caporaso J, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31, 814–821 (2013). https://doi.org/10.1038/nbt.2676

• PICRUSt2 - (Python, v2.6.2, 2025.4)

  • Douglas GM, Maffei VJ, Zaneveld JR, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol 38, 685–688 (2020). https://doi.org/10.1038/s41587-020-0548-6

• graftM - (Python, v0.14.0, 2022.5)

  • Boyd JA, Woodcroft BJ, Tyson GW. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res 46(10), e59 (2018). https://doi.org/10.1093/nar/gky174

• RiboSnake - (snakemake, v0.10.0, 2024.8)

  • Dörr AK, Welling J, Dörr A, et al. RiboSnake – a user-friendly, robust, reproducible, multipurpose and documentation-extensive pipeline for 16S rRNA gene microbiome analysis. Gigabyte (2024). https://doi.org/10.46471/gigabyte.132

• ssUMI - (Shell, NoReleaseTag)

  • Lin X, Waring K, Ghezzi H, et al. High accuracy meets high throughput for near full-length 16S ribosomal RNA amplicon sequencing on the Nanopore platform. PNAS Nexus 3(10), pgae411 (2024). https://doi.org/10.1093/pnasnexus/pgae411

rRNA Databases

• EUKARYOME - (18S/ITS/28S, v1.9.2, 2024.8)

  • Tedersoo L, Moghaddam MSH, Mikryukov V, et al. EUKARYOME: the rRNA gene reference database for identification of all eukaryotes, Database 2024, baae043 (2024). https://doi.org/10.1093/database/baae043

• Silva - (rRNA, v138.2, 2024.7) - A continuously updated rRNA database with high update frequency, currently the largest and most comprehensive of its kind.

  • Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41(D1), D590–D596 (2013). https://doi.org/10.1093/nar/gks1219

• Greengenes2 - (16S, v2022.10, 2022.10) - The first version was last updated in 2012. Greengenes2 was released in 2022 and is updated approximately every two years.

  • DeSantis TZ, Hugenholtz P, Larsen N, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72(7), 5069-72 (2006). https://doi.org/10.1128/AEM.03006-05
  • McDonald D, Jiang Y, Balaban M, et al. Greengenes2 unifies microbial data in a single reference tree. Nat Biotechnol 42, 715–718 (2024). https://doi.org/10.1038/s41587-023-01845-1

• MiDAS - (16S, v5, 2024.7)

  • Dueholm MKD, Andersen KS, Korntved AKC, et al. MiDAS 5: Global diversity of bacteria and archaea in anaerobic digesters. Nat Commun 15, 5361 (2024). https://doi.org/10.1038/s41467-024-49641-y

• KSGP - (SSU, v3.1)

  • Grant A, Aleidan A, Davies CS, et al. KSGP 3.1: improved taxonomic annotation of Archaea communities using LotuS2, the Genome Taxonomy Database and RNAseq data. ISME Communications ycaf094 (2025). https://doi.org/10.1093/ismeco/ycaf094

End-to-end workflows for metagenome and isolate

Workflows for metagenome

• Aviary - (Snakemake, v0.9.2, 2024.09)

  • A pipeline for assembly, binning, annotation, and strain diversity analysis.

• MAG - (Nextflow, v3.0.3, 2024.8)

  • Krakau S, Straub D, Gourlé H, et al. nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics 4(1), lqac007 (2022). https://doi.org/10.1093/nargab/lqac007

• MetaWRAP - (Shell/Python, v1.3, 2020.08) - A modular pipeline for metagenomic analysis, covering steps such as quality control, assembly, binning, genome refinement, classification, and annotation. Users can independently run any individual module.

  • Uritskiy GV, DiRuggiero J & Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018). https://doi.org/10.1186/s40168-018-0541-1

• VEBA - (Python, v2.3.0, 2024.9)

  • Espinoza JL, Dupont CL. VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes. BMC Bioinformatics 23, 419 (2022). https://doi.org/10.1186/s12859-022-04973-8

• ATLAS - (Python, v2.19.0, 2024.7)

  • Kieser S, Brown J, Zdobnov EM, et al. ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinformatics 21, 257 (2020). https://doi.org/10.1186/s12859-020-03585-4

• anvi'o - (Win/MacOS/Linux, v8, 2023.09)

  • Eren AM, Kiefl E, Shaiber A, et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat Microbiol 6, 3–6 (2021). https://doi.org/10.1038/s41564-020-00834-3

• nano-rave - (Nextflow, v1.0.0, 2023.01)

  • Girgis ST, Adika E, Nenyewodey FE, et al. Drug resistance and vaccine target surveillance of Plasmodium falciparum using nanopore sequencing in Ghana. Nat Microbiol 8, 2365–2377 (2023). https://doi.org/10.1038/s41564-023-01516-6

• metagWGS - (Nextflow, v2.4.2, 2023.6)

  • Mainguy J, Vienne M, Fourquet J, et al. metagWGS, a comprehensive workflow to analyze metagenomic data using Illumina or PacBio HiFi reads. bioRxiv (2024). https://doi.org/10.1101/2024.09.13.612854

• SqueezeMeta - (C/C++/Python/Perl, v1.6.5, 2024.8)

  • Tamames J, Puente-Sánchez F. SqueezeMeta, A Highly Portable, Fully Automatic Metagenomic Analysis Pipeline. Front Microbiol 9 (2019). https://doi.org/10.3389/fmicb.2018.03349

• slamM - (Snakemake)

• CAT_pack - (Python, v6.0.1, 2024.3)

  • von Meijenfeldt FAB, Arkhipova K, Cambuy DD, et al. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol 20, 217 (2019). https://doi.org/10.1186/s13059-019-1817-x

• Metagenomics-Toolkit - (Nextflow, v0.4.5, 2024.10)

  • Belmann P, Osterholz B, Kleinbölting N, et al. Metagenomics-Toolkit: The Flexible and Efficient Cloud-Based Metagenomics Workflow featuring Machine Learning-Enabled Resource Allocation. bioRxiv (2024). https://doi.org/10.1101/2024.10.22.619569

• BugBuster - Nextflow

  • Fuentes-Santander F, Curiqueo C, Araos R, Ugalde JA. BugBuster: A novel automatic and reproducible workflow for metagenomic data analysis. bioRxiv (2025). https://doi.org/10.1101/2025.02.24.639915

• MARTi

  • Peel N, Martin S, Heavens D, et al. MARTi: a real-time analysis and visualisation tool for nanopore metagenomics. bioRxiv (2025). https://doi.org/10.1101/2025.02.14.638261

Workflows for isolate

• Bactopia - (Nextflow/Perl, v3.1.0, 2024.9)

  • Petit RA and Read TD. Bactopia: a Flexible Pipeline for Complete Analysis of Bacterial Genomes. mSystems 5 (2020). https://doi.org/10.1128/msystems.00190-20

• ASA³P - (Groovy/JS, v1.3.0, 2020.5)

  • Schwengers O, Hoek A, Fritzenwanker M, et al. ASA3P: An automatic and scalable pipeline for the assembly, annotation and higher-level analysis of closely related bacterial isolates. PLoS Comput Biol 16(3), e1007134 (2020). https://doi.org/10.1371/journal.pcbi.1007134

• microPIPE - (html tutorial Nextflow, step by step)

  • Murigneux V, Roberts LW, Forde BM, et al. MicroPIPE: validating an end-to-end workflow for high-quality complete bacterial genome construction. BMC Genomics 22, 474 (2021). https://doi.org/10.1186/s12864-021-07767-z

• AQUAMIS - (Python/Shell, v1.4.2, 2024.6)

  • Deneke C, Brendebach H, Uelze L, et al. Species-Specific Quality Control, Assembly and Contamination Detection in Microbial Isolate Sequences with AQUAMIS. Genes 12(5), 644 (2021). https://doi.org/10.3390/genes12050644

• Nullarbor - (Perl, v1.41, 2018.6) - Pipeline to generate complete public health microbiology reports from sequenced isolates.

• ProkEvo - jupyter tutorial

  • Pavlovikj N, Gomes-Neto JC, Deogun JS, Benson AK. ProkEvo: an automated, reproducible, and scalable framework for high-throughput bacterial population genomics analyses. PeerJ 9, e11376 (2021). https://doi.org/10.7717/peerj.11376

• Public Health Bioinformatics - (WDL, v3.1.0, 2025.7) - TheiaCoV(viral), TheiaProk(bacterial)

  • Libuit KG, Doughty EL, Otieno JR, et al. Accelerating Bioinformatics Implementation in Public Health. Microbial Genomics 9(7), 2057-5858 (2023). https://doi.org/10.1099/mgen.0.001051

• Public Health Bioinformatics - (WDL, v3.1.0, 2025.7) - TheiaEuk

  • Ambrosio FJ, Scribner MR, Wright SM, et al. TheiaEuk: A Species-Agnostic Bioinformatics Workflow for Fungal Genomic Characterization. Frontiers in Public Health 11 (2023). https://doi.org/10.3389/fpubh.2023.1198213

• rMAP - (Shell/Python, tutorial)

  • Sserwadda I, Mboowa G. rMAP: the Rapid Microbial Analysis Pipeline for ESKAPE bacterial group whole-genome sequence data. Microbial Genomics 7 (2021). https://doi.org/10.1099/mgen.0.000583

• TORMES - (Shell, v1.3.0, 2021.8)

  • Quijada NM, Rodríguez-Lázaro D, Eiros JM, Hernández M. TORMES: an automated pipeline for whole bacterial genome analysis. Bioinformatics 35(21), 4207–4212 (2019). https://doi.org/10.1093/bioinformatics/btz220

• Hybracter - (Python, v0.10.0, 2024.10)

  • Bouras G, Houtak G, Wick R, et al. Hybracter: Enabling Scalable, Automated, Complete and Accurate Bacterial Genome Assemblies. Microbial Genomics 10, 5 (2024). https://doi.org/10.1099/mgen.0.001244

Microbial genomic resources

Prokaryotic genome

• GTDB - (Genomes, R220, 2024.04)

  • Parks DH, Chuvochina M, Rinke C, et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res 50(D1), D785–D794 (2022). https://doi.org/10.1093/nar/gkab776

• GlobDB - (Genomes, 220) - A collection from Daan Septh

  • Speth DR, Pullen N, Aroney STN, et al. GlobDB: A comprehensive species-dereplicated microbial genome resource. arXiv (2025). https://doi.org/10.48550/arXiv.2506.11896

• proGenomes - (Genomes, v3)

• cFMD - (Food Microb Genomes, v1.2.0, 2024.08)

  • Carlino N, Blanco-Míguez A, Punčochář M, et al. Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome. Cell 187(20), 5775-5795 (2024). https://doi.org/10.1016/j.cell.2024.07.039

• GOMC - (Genomes, 2024.09)

  • Chen J, Jia Y, Sun Y, et al. Global marine microbial diversity and its potential in bioprospecting. Nature 633, 371–379 (2024). https://doi.org/10.1038/s41586-024-07891-2

• Ocean Microbiomics Database (OMD) - (Genomes, v1.1)

  • Paoli L, Ruscheweyh HJ, Forneris CC, et al. Biosynthetic potential of the global ocean microbiome. Nature 607, 111–118 (2022). https://doi.org/10.1038/s41586-022-04862-3

• OceanDNA - (Genomes, v2022-05, 2022.05)

  • Yoshizawa S, Nishimura Y. The OceanDNA MAG catalog contains over 50,000 prokaryotic genomes originated from various marine environments. figshare Collection (2022). https://doi.org/10.6084/m9.figshare.c.5564844.v1

• AllTheBacteria - (Genomes, v2024-08, 2024.08)

  • Hunt M, Lima L, Anderson D, et al. AllTheBacteria - all bacterial genomes assembled, available and searchable. bioRxiv (2024). https://doi.org/10.1101/2024.03.08.584059

• MGnify - (Genomes, 2023.09) - ftp: https://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes/

  • Richardson L, Allen B, Baldi G, et al. MGnify: the microbiome sequence data analysis resource in 2023. Nucleic Acids Res 51(D1), D753–D759 (2023). https://doi.org/10.1093/nar/gkac1080

• MGnify genome

  • Gurbich TA, Almeida A, Beracochea M, et al. MGnify Genomes: A Resource for Biome-specific Microbial Genome Catalogues. J Mol Biol 435, 14 (2023). https://doi.org/10.1016/j.jmb.2023.168016

• Logan - (Unitigs/Contigs, AWS)

• BakRep

  • Fenske L, Jelonek L, Goesmann A, Schwengers O. BakRep – a searchable large-scale web repository for bacterial genomes, characterizations and metadata. Microbial Genomics 10, 10 (2024). https://doi.org/10.1099/mgen.0.001305

• BacDive - Web: https://bacdive.dsmz.de

  • Schober I, Koblitz J, Carbasse JS, et al. BacDive in 2025: the core database for prokaryotic strain data. Nucleic Acids Res 53(D1), D748–D756 (2025). https://doi.org/10.1093/nar/gkae959

• GROWdb, NMDC data portal/with KBase/GROWdb Explorer

  • Borton MA, McGivern BB, Willi KR, et al. A functional microbiome catalogue crowdsourced from North American rivers. Nature 637, 103–112 (2025). https://doi.org/10.1038/s41586-024-08240-z

Phage genome

• inphared

  • Cook R, Brown N, Redgwell T, et al. INfrastructure for a PHAge REference Database: Identification of Large-Scale Biases in the Current Collection of Cultured Phage Genomes. Phage 2, 4 (2021). https://doi.org/10.1089/phage.2021.0007

• PhageDive - Web Search

  • Rolland C, Wittmann J, Reimer LC, et al. PhageDive: the comprehensive strain database of prokaryotic viral diversity, Nucleic Acids Res 53(D1), D819–D825 (2025). https://doi.org/10.1093/nar/gkae878

Quality control of sequencing data

Reads simulation

• wgsim - (C) Reads simulator

• badread - (Python, v0.4.1, 2024.2) - a long read simulator that can imitate many types of read problems

Basecall

• Dorado - (C++, v0.9.0, 2024.12) - Oxford Nanopore's Basecaller

QC

• FastQC - (Java, 0.12.1, 2023.3) - A quality control tool for high throughput sequence data

• fastp - (C++/C, v0.23.4, 2023.5)

  • Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17), 884–890 (2018). https://doi.org/10.1093/bioinformatics/bty560
  • Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2(2), e107 (2023). https://doi.org/10.1002/imt2.107

• Trimmomatic - (Java, v0.39, 2021.01)

  • Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15), 2114–2120 (2014). https://doi.org/10.1093/bioinformatics/btu170

• Cutadapt - (Python, v4.9, 2024.6)

  • Martin M. Cutadapt Removes Adapter Sequences From High-Throughput Sequencing Reads. EMBnet J 17, 1 (2010). https://doi.org/10.14806/ej.17.1.200

• FastUniq - (C++, 1.1, 2012.05)

  • Xu H, Luo X, Qian J, et al. FastUniq: A Fast De Novo Duplicates Removal Tool for Paired Short Reads. PLoS ONE 7(12), e52249 (2012). https://doi.org/10.1371/journal.pone.0052249

• PoreChop - (C++, v0.2.4, 2018.10) - adapter trimmer for Oxford Nanopore reads. Unsupported since Oct 2018

• Sickle - (C, v1.33, 2014.7) - Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files

• PEAT - (C++, v1.2.4, 2016.2) - No longer maintained, recommond EARRINGS

  • Li YL, Weng JC, Hsiao CC. et al. PEAT: an intelligent and efficient paired-end sequencing adapter trimming algorithm.BMC Bioinformatics 16(Suppl 1), S2 (2015). https://doi.org/10.1186/1471-2105-16-S1-S2

• EARRINGS - (C++, v1.1.0, 2024.6)

  • Wang TH, Huang CC, Hung JH. EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences. Bioinformatics 37(13), 1846–1852 (2021). https://doi.org/10.1093/bioinformatics/btab025

• NanoPack - (Python) - No release

  • De Coster W, D’Hert S, Schultz DT, et al. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34(15), 2666–2669 (2018). https://doi.org/10.1093/bioinformatics/bty149

• NanoPlot - (Python, v1.42.0, 2023.10)

  • De Coster W, Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics 39(5), btad311 (2023). https://doi.org/10.1093/bioinformatics/btad311

• chopper - (Rust, v0.9.0, 2024.8) - cite NanoPack2

  • De Coster W, Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics 39(5), btad311 (2023). https://doi.org/10.1093/bioinformatics/btad311

• nanoQC - (Python) - no release

  • De Coster W, D’Hert S, Schultz DT, et al. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34(15), 2666–2669 (2018). https://doi.org/10.1093/bioinformatics/bty149

• fastplong - (C++, v0.2.2, 2024.12) - Ultra-fast preprocessing and quality control for long-read sequencing data

• sequali - (C/python, v0.12.0, 2024.10)

  • Vorderman RHP. Sequali: efficient and comprehensive quality control of short- and long-read sequencing data. Bioinformatics Advances 5(1), vbaf010 (2025). https://doi.org/10.1093/bioadv/vbaf010

Correct reads

Consensus sequence from long-reads

• medaka - (Python, v2.0.0, 2024.9) - a tool to create consensus sequences and variant calls from nanopore sequencing data

Alignment and mapping

Short-read to sequence

• Bowtie2 - (C++, v2.5.4, 2024.05)

  • Langmead B, Salzberg S. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357–359 (2012). https://doi.org/10.1038/nmeth.1923

• bwa - (C, v0.7.18, 2024.04)

  • Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25(14), 1754–1760 (2009). https://doi.org/10.1093/bioinformatics/btp324

• bbmap - (Java, v39.10, 2024.09)

  • Bushnell B. BBMap: A Fast, Accurate, Splice-Aware Aligner. LBNL Report (2014). https://escholarship.org/uc/item/1h3515gn

• strobealign - (C++, v0.15.0, 2024.12)

  • Sahlin K. Strobealign: flexible seed size enables ultra-fast and accurate read alignment. Genome Biol 23, 260 (2022). https://doi.org/10.1186/s13059-022-02831-7

Long-read to sequence

• BWA-MEM - (C, v0.7.18, 2024.04)

  • Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv (2013). https://doi.org/10.48550/arXiv.1303.3997

• BWA-MEM2 - (C++, v2.2.1, 2021.3)

  • Vasimuddin M, Misra S, Li H and Aluru S. Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems. IEEE International Parallel and Distributed Processing Symposium (IPDPS) 314-324 (2019). https://doi.org/10.1109/IPDPS.2019.00041

• Minimap2 - (C, v2.28, 2024.03)

  • Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34(18), 3094–3100 (2018). https://doi.org/10.1093/bioinformatics/bty191

Sequence to sequence

• blast+ - (C++, 2.16.0, 2024.06) - download databases: https://ftp.ncbi.nlm.nih.gov/blast/db/

  • Camacho C, Coulouris G, Avagyan V, et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009). https://doi.org/10.1186/1471-2105-10-421

• DIAMOND - (C++, v2.1.9, 2024.01)

  • Buchfink B, Xie C & Huson D. Fast and sensitive protein alignment using DIAMOND. Nat Methods 12, 59–60 (2015). https://doi.org/10.1038/nmeth.3176
  • Buchfink B, Reuter K & Drost HG. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods 18, 366–368 (2021). https://doi.org/10.1038/s41592-021-01101-x

• LexicMap - (Go, v0.4.0, 2024.08)

  • Shen W, Iqbal Z. LexicMap: efficient sequence alignment against millions of prokaryotic genomes. bioRxiv (2024). https://doi.org/10.1101/2024.08.30.610459

• foldmason - (C/C++, v1-763a428, 2024.08)

  • Gilchrist CLM, Mirdita M, Steinegger M. Multiple Protein Structure Alignment at Scale with FoldMason. bioRixv (2024). https://doi.org/10.1101/2024.08.01.606130

• SkyBLAST - a replica of the NIH's interface - providing an alternative to the US gov service that's less congested, faster & more reliable

Multi sequence alignment

• MUSCLE - (C++, v5.2, 2024.08) - Benchmark:https://github.com/rcedgar/muscle_benchmark

  • Edgar RC. Muscle5: High-accuracy alignment ensembles enable unbiased assessments of sequence homology and phylogeny. Nat Commun 13, 6968 (2022). https://doi.org/10.1038/s41467-022-34630-w

• SSU-ALIGN - (Perl, v0.1.1, 2016.02)

  • Nawrocki EP. Structural RNA Homology Search and Alignment Using Covariance Models. Ph.D thesis, Washington U (2009). http://eddylab.org/publications/Nawrocki09b/Nawrocki09b-phdthesis.pdf

• MAFFT - (C, v7.526, 2024.04)

  • Katoh K, Standley DM. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol Biol Evol 30(4), 772–780 (2013). https://doi.org/10.1093/molbev/mst010

• Clustal 2 - (v2.1, 2010.10)

  • Larkin MA, Blackshields G, Brown NP, et al. Clustal W and Clustal X version 2.0. Bioinformatics 23(21), 2947–2948 (2007). https://doi.org/10.1093/bioinformatics/btm404

• T-Coffee - (C/Perl, 13.46.1.b8b01e06, 2024.6)

  • Notredame C, Higgins DG, Heringa J. T-Coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302(1), 205-217 (2000). https://doi.org/10.1006/jmbi.2000.4042

Cluster sequence

• MMseq - (C++, 2016.9)

  • Hauser M, Steinegger M, Söding J. MMseqs software suite for fast and deep clustering and searching of large protein sequence sets. Bioinformatics 32(9), 1323–1330 (2016). https://doi.org/10.1093/bioinformatics/btw006

• MMseq2 - (C/C++, 15-6f452, 2023.10)

  • Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol 35, 1026–1028 (2017). https://doi.org/10.1038/nbt.3988

• MMseq2 CUDA GPU-based - NVIDIA blog: https://developer.nvidia.com/blog/boost-alphafold2-protein-structure-prediction-with-gpu-accelerated-mmseqs2

  • Kallenborn F, Chacon A, Hundt C, et al. GPU-accelerated homology search with MMseqs2. bioRxiv (2025). https://doi.org/10.1101/2024.11.13.623350

• USEARCH - (C++/C, v12.0-beta1, 2024.6)

  • Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19), 2460–2461 (2010). https://doi.org/10.1093/bioinformatics/btq461

• CD-HIT - (Perl/C++, v4.8.1, 2019.3)

  • Fu L, Niu B, Zhu Z. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28(23), 3150–3152 (2012). https://doi.org/10.1093/bioinformatics/bts565

Metagenome assembly

Short read only assembly

• SOAPdenovo2 - (C, r242, 2020.10)

  • Luo R, Liu B, Xie Y, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 1(1), 2047–217X–1–18 (2012). https://doi.org/10.1186/2047-217X-1-18

• idba/IDBA-UD - (C++, v1.1.3, 2016.7)

  • Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28(11), 1420–1428 (2012). https://doi.org/10.1093/bioinformatics/bts174

• SPAdes/metaSPAdes - (C++, v4.0.0, 2024.6)

  • Sergey Nurk, Dmitry Meleshko, Anton Korobeynikov and Pavel A. Pevzner. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824-834 (2017). https://doi.org/10.1101/gr.213959.116

• megahit - (C++, v1.2.9, 2019.10) - A metagenome assembler with high speed and low memory consumption.

  • Li D, Liu CM, Luo R, et al. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph Bioinformatics 31(10), 1674–1676 (2015). https://doi.org/10.1093/bioinformatics/btv033
  • Li D, Luo R, Liu CM, et al. MEGAHIT v1.0: A Fast and Scalable Metagenome Assembler driven by Advanced Methodologies and Community Practices. Methods 102, 3-11 (2016). https://doi.org/10.1016/j.ymeth.2016.02.020

• PenguiN - (C, v5-cf8933, 2024.3)

  • Jochheim A, Jochheim FA, Kolodyazhnaya A, et al. Strain-resolved de-novo metagenomic assembly of viral genomes and microbial 16S rRNAs. bioRxiv (2024). https://doi.org/10.1101/2024.03.29.587318

• BBMerge - (Bash, v39.10, 2024.9)

  • Bushnell B, Rood J, Singer E. BBMerge – Accurate paired shotgun read merging via overlap. PLoS ONE 12(10), e0185056 (2017). https://doi.org/10.1371/journal.pone.0185056

• FLASH - (C, v1.2.11, 2018.3)

  • Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27(21), 2957–2963 (2011). https://doi.org/10.1093/bioinformatics/btr507

• ABySS - (C++/C, v2.3.9, 2024.9)

  • Jackman SD, Vandervalk BP, Mohamadi H, et al. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter. Genome Res 27, 768-777 (2017). http://www.genome.org/cgi/doi/10.1101/gr.214346.116

Long read only assembly

• MaSuRCA - (M4/Perl, v4.1.1, 2024.2)

  • Zimin AV, Marçais G, Puiu D, et al. The MaSuRCA genome assembler. Bioinformatics 29(21), 2669–2677 (2013). https://doi.org/10.1093/bioinformatics/btt476
  • Zimin AV, Salzberg SL. The SAMBA tool uses long reads to improve the contiguity of genome assemblies. PLoS Comput Biol 18(2), e1009860 (2022). https://doi.org/10.1371/journal.pcbi.1009860

• metaFlye - (C/C++, v2.9.5, 2024.8)

  • Kolmogorov M, Bickhart DM, Behsaz B, et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods 17, 1103–1110 (2020). https://doi.org/10.1038/s41592-020-00971-x

• Canu - (C++/C, v2.3, 2024.12)

  • Koren S, Walenz BP, Berlin K, et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res 27, 722-736 (2017). https://genome.cshlp.org/content/27/5/722

• FALCON - (Python, v0.3.0, 2018.8)

• miniasm - (TeX/C, v0.3, 2018.7)

  • Li H. Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences. Bioinformatics 32(14), 2103–2110 (2016). https://doi.org/10.1093/bioinformatics/btw152

• wtdbg2 - (C, v2.5, 2019.09)

  • Ruan J, Li H. Fast and accurate long-read assembly with wtdbg2. Nat Methods 17, 155–158 (2020). https://doi.org/10.1038/s41592-019-0669-3

• Trycycler - (Python, v0.5.5, 2024.3)

  • Wick RR, Judd LM, Cerdeira LT, et al. Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biol 22, 266 (2021). https://doi.org/10.1186/s13059-021-02483-z

• NextDenovo - (C, 2.5.2, 2023.3)

  • Hu J, Wang Z, Sun Z, et al. NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads. Genome Biol 25, 107 (2024). https://doi.org/10.1186/s13059-024-03252-4

• Autocycler - (Rust, v0.4.0, 2025.4) - A tool for generating consensus long-read assemblies for bacterial genomes

  • Wick RR, Howden BP, Stinear TP. Autocycler: long-read consensus assembly for bacterial genomes. bioRxiv (2025). https://doi.org/10.1101/2025.05.12.653612

• nanoMDBG - (C++, v1.1, 2024.12)

  • Benoit G, James R, Raguideau S, et al. High-quality metagenome assembly from nanopore reads with nanoMDBG. bioRxiv (2025). https://doi.org/10.1101/2025.04.22.649928

• myloasm - (Rust, v0.2.0, 2025.8) - A new high-resolution long-read metagenome assembler for even noisy reads, documentaion: https://myloasm-docs.github.io

  • Shaw J, Marin MG, Li H. High-resolution metagenome assembly for modern long reads with myloasm. bioRxiv (2025). https://doi.org/10.1101/2025.09.05.674543

Hybrid assembly

• idba/IDBA-Hybrid - (C++, v1.1.3, 2016.7)

  • Peng Y, Leung HCM, Yiu SM, Chin FYL. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28(11), 1420–1428 (2012). https://doi.org/10.1093/bioinformatics/bts174

• SPAdes/metaSPAdes - (C++, v4.0.0, 2024.6)

  • Nurk S, Meleshko D, Korobeynikov A and Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824-834 (2017). https://doi.org/10.1101/gr.213959.116

• Aviary - (Python, v0.9.2, 2024.9) - A hybrid assembly and MAG recovery pipeline (and more!)

• OPERA-MS - (C++/Python, v0.8.3, 2020.6)

  • Bertrand D, Shaw J, Kalathiyappan M, et al. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes. Nat Biotechnol 37, 937–944 (2019). https://doi.org/10.1038/s41587-019-0191-2

• Unicycler - (C++, v0.5.1, 2024.7)

  • Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol 13(6), e1005595 (2017). https://doi.org/10.1371/journal.pcbi.1005595

• HyLight - (C++/Python, v1.0.0, 2024.9)

  • Kang X, Zhang W, Li Y, et al. HyLight: Strain aware assembly of low coverage metagenomes. Nat Commun 15, 8665 (2024). https://doi.org/10.1038/s41467-024-52907-0

PB HiFi read assembly

• hifiasm-meta - (C++, v0.3.2, 2024.9)

  • Feng X, Li H. Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies. Genome Biol 25, 92 (2024). https://doi.org/10.1186/s13059-024-03234-6

• metaMDBG - (C++, v1.1, 2024.12)

  • Benoit G, Raguideau S, James R, et al. High-quality metagenome assembly from long accurate reads with metaMDBG. Nat Biotechnol 42, 1378–1383 (2024). https://doi.org/10.1038/s41587-023-01983-6

Polish

• FMLRC2 - (Rust, v0.1.8, 2022.7)

  • Mak QXC, Wick RR, Holt JM, Wang JR. Polishing De Novo Nanopore Assemblies of Bacteria and Eukaryotes With FMLRC2. Mol Biol Evol 40(3), msad048 (2023). https://doi.org/10.1093/molbev/msad048

• pilon - (Scala, v1.24, 2021.1)

  • Walker BJ, Abeel T, Shea T, et al. Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement. PLoS ONE 9(11), e112963 (2014). https://doi.org/10.1371/journal.pone.0112963

• NextPolish - (Rust, v1.4.1, 2022.7)

  • Hu J, Fan J, Sun Z, Liu S. NextPolish: a fast and efficient genome polishing tool for long-read assembly. Bioinformatics 36(7), 2253–2255 (2020). https://doi.org/10.1093/bioinformatics/btz891

• Polypolish - (Rust, v0.6.0, 2024.1)

  • Bouras G, Judd L, Edwards R, et al. How low can you go? Short-read polishing of Oxford Nanopore bacterial genome assemblies. Microbial Genomics 10, 6 (2024). https://doi.org/10.1099/mgen.0.001254

• Pypolca - (Python, v0.3.1, 2024.2)

  • Bouras G, Judd L, Edwards R, et al. How low can you go? Short-read polishing of Oxford Nanopore bacterial genome assemblies. Microbial Genomics 10, 6 (2024). https://doi.org/10.1099/mgen.0.001254

• MaSuRCA/POLCA - (M4/Perl, v4.1.1, 2024.2)

  • Zimin AV, Salzberg SL. The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies. PLoS Comput Biol 16(6), e1007981 (2020). https://doi.org/10.1371/journal.pcbi.1007981

• Racon - (C++, v1.5.0, 2021.12)

  • Vaser R, Sović I, Nagarajan N and Šikić M. Ultrafast consensus module for raw de novo genome assembly of long uncorrected reads. Genome Res 27, 737-746 (2017). http://www.genome.org/cgi/doi/10.1101/gr.214270.116

• medaka

• dorado -

• DeepPolisher

  • Mastoras M, Asri M, Brambrink L, et al. Highly accurate assembly polishing with DeepPolisher. Genome Res (2025). https://doi.org/10.1101/gr.280149.124

Genome size estimation

• LRGE - (Rust, v0.1.3, 2024.12) - Pure
  • Hall MB, Coin LJM. Genome size estimation from long read overlaps. bioRxiv (2024). https://doi.org/10.1101/2024.11.27.625777

Other elements assmebly

• Chopper - (Python) - NoRelease

  • Schimke KD, Vollmers C. Sequencing complete plasmids on Oxford Nanopore Technology Sequencers using R2C2 and Chopper. bioRxiv (2025). https://doi.org/10.1101/2025.01.16.633418

• PlasMAAG - (snakemake) - part of VAMB

  • Líndez PP, Danielsen LS, Kovačić I, et al. Accurate plasmid reconstruction from metagenomics data using assembly-alignment graphs and contrastive learning. bioRxiv (2025). https://doi.org/10.1101/2025.02.26.640269

Assembly improvement

• COBRA - (Python, v1.2.3, 2024.2)
  • Chen L, Banfield JF. COBRA improves the completeness and contiguity of viral genomes assembled from metagenomes. Nat Microbiol 9, 737–750 (2024). https://doi.org/10.1038/s41564-023-01598-2

Binning

Tools and workflows

• COCACOLA - (MATLAB/C++, 2015)

  • Lu YY, Chen T, Fuhrman JA, Sun F. COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge, Bioinformatics 33(6), 791–798 (2017). https://doi.org/10.1093/bioinformatics/btw290

• MetaBAT2 - (C++, v2.17, 2023.09)

  • Kang DD, Li F, Kirton E, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019). https://doi.org/10.7717/peerj.7359

• MaxBin2 - (C++, 2.2.7, 2020.06)

  • Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32(4), 605–607 (2016). https://doi.org/10.1093/bioinformatics/btv638

• CONCOCT - (Python, v1.1.0, 2019.08)

  • Alneberg J, Bjarnason B, de Bruijn I, et al. Binning metagenomic contigs by coverage and composition. Nat Methods 11, 1144–1146 (2014). https://doi.org/10.1038/nmeth.3103

• VAMB - (Python, v4.1.3, 2023.06)

  • Nissen JN, Johansen J, Allesøe RL, et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat Biotechnol 39, 555–560 (2021). https://doi.org/10.1038/s41587-020-00777-4

• TaxVAMB - (Python, v4.1.3, 2023.6)

  • Kutuzova S, Piera P, Nielsen KN, et al. Binning meets taxonomy: TaxVAMB improves metagenome binning using bi-modal variational autoencoder. bioRxiv (2024). https://doi.org/10.1101/2024.10.25.620172

• UniteM - (Python, 1.2.4, 2022.10)

• SemiBin - (Python, v1.5.1, 2023.03)

  • Pan S, Zhu C, Zhao XM, et al. A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments. Nat Commun 13, 2326 (2022). https://doi.org/10.1038/s41467-022-29843-y

• SemiBin2 - (Python, v2.1.0, 2024.03) - Benchmark: https://github.com/BigDataBiology/SemiBin2_benchmark

  • Pan S, Zhao XM, Coelho LP. SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing. Bioinformatics 39(Suppl_1), 21–29 (2023). https://doi.org/10.1093/bioinformatics/btad209

• Smeta - (C++, 2024.09) - NoReleaseTag

  • Zhang Y, Cheng M, Ning K. SMeta. a binning tool using single-cell sequences to aid in reconstructing species from metagenome accurately. bioRxiv (2024). https://doi.org/10.1101/2024.08.25.609542

• Bin Chiken - (Python, v0.12.6, 2024.12)

  • Aroney STN, Newell RJP, Tyson GW, Woodcroft BJ. Bin Chicken: targeted metagenomic coassembly for the efficient recovery of novel genomes. bioRxiv (2024). https://doi.org/10.1101/2024.11.24.625082

• MetaCoAG - (Python, v1.2.2, 2024.9)

  • Mallawaarachchi V, Lin Y. (2022). MetaCoAG: Binning Metagenomic Contigs via Composition, Coverage and Assembly Graphs. Research in Computational Molecular Biology RECOMB 13278 (2022). https://doi.org/10.1007/978-3-031-04749-7_5

• GraphBin - (Python, v1.7.4, 2024.8)

  • Mallawaarachchi V, Wickramarachchi A, Lin Y. GraphBin: refined binning of metagenomic contigs using assembly graphs. Bioinformatics 36(11), 3307–3313 (2020). https://doi.org/10.1093/bioinformatics/btaa180

• GraphBin2 - (Python, v1.3.3, 2024.9)

  • Mallawaarachchi VG, Wickramarachchi AS & Lin Y. Improving metagenomic binning results with overlapped bins using assembly graphs. Algorithms Mol Biol 16, 3 (2021). https://doi.org/10.1186/s13015-021-00185-6

• MetaBinner - (Python/Perl, v1.4.4, 2022.9)

  • Wang Z, Huang P, You R, et al. MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities. Genome Biol 24, 1 (2023). https://doi.org/10.1186/s13059-022-02832-6

Strain-level resolve

• inStrain - (Python, v1.9.0, 2024.5)

  • Olm MR, Crits-Christoph A, Bouma-Gregson K, et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat Biotechnol 39, 727–736 (2021). https://doi.org/10.1038/s41587-020-00797-0

• Floria - (Rust, v0.0.1, 2024.1)

  • Shaw J, Gounot JS, Chen H, et al. Floria: fast and accurate strain haplotyping in metagenomes. Bioinformatics 40(Suppl_1), 30–38 (2024). https://doi.org/10.1093/bioinformatics/btae252

• Lorikeet - (Rust, v0.8.2, 2023.12) - Strain resolver for metagenomics

• STRONG - (Python, v0.0.1-beta, 2021.5)

  • Quince C, Nurk S, Raguideau S, et al. STRONG: metagenomics strain resolution on assembly graphs. Genome Biol 22, 214 (2021). https://doi.org/10.1186/s13059-021-02419-7

• Strainberry - (Python, v1.1, 2021.5)

  • Vicedomini R, Quince C, Darling AE, et al. Strainberry: automated strain separation in low-complexity metagenomes using long reads. Nat Commun 12, 4485 (2021). https://doi.org/10.1038/s41467-021-24515-9

• Strainy - (Python, v1.1, 2024.9)

  • Kazantseva E, Donmez A, Frolova M, et al. Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing. Nat Methods 21, 2034–2043 (2024). https://doi.org/10.1038/s41592-024-02424-1

• VStrains - (Python, v1.1.0, 2023.2)

  • Luo R, Lin Y. VStrains: De Novo Reconstruction of Viral Strains via Iterative Path Extraction From Assembly Graphs. bioRxiv (2023). https://doi.org/10.1101/2022.10.21.513181

• devider - (Rust, v0.0.1, 2024.10)

  • Shaw J, Boucher C, Yu YW, et al. devider: long-read reconstruction of many diverse haplotypes. bioRxiv (2024). https://doi.org/10.1101/2024.11.05.621838

• ChronoStrain - (Python, v0.6.0, 2025.4)

  • Kim Y, Worby CJ, Acharya S, et al. Longitudinal profiling of low-abundance strains in microbiomes with ChronoStrain. Nat Microbiol 10, 1184–1197 (2025). https://doi.org/10.1038/s41564-025-01983-z

MAG improvement

• Circlator - (Python, v1.5.5, 2020.10)

  • Hunt M, Silva ND, Otto TD, et al. Circlator: automated circularization of genome assemblies using long sequencing reads. Genome Biol 16, 294 (2015). https://doi.org/10.1186/s13059-015-0849-0

• gapFinisher - 2019

  • Kammonen JI, Smolander O-P, Paulin L, et al. gapFinisher: A reliable gap filling pipeline for SSPACE-LongRead scaffolder output. PLoS ONE 14(9), e0216885 (2019). https://doi.org/10.1371/journal.pone.0216885

• FGAP - (MATLAB, v1.8.1, 2017.11)

  • Piro VC, Faoro H, Weiss VA, et al. FGAP: an automated gap closing tool. BMC Res Notes 7, 371 (2014). https://doi.org/10.1186/1756-0500-7-371

• MaSuRCA/samba.sh

  • Zimin AV, Salzberg SL. The SAMBA tool uses long reads to improve the contiguity of genome assemblies. PLoS Comput Biol 18(2), e1009860 (2022). https://doi.org/10.1371/journal.pcbi.1009860

MAG assessment

• BUSCO - (Python, v5.8.1, 2024.10)

  • Manni M, Berkeley MR, Seppey M, et al. BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes. Mol Biol Evol 38(10), 4647–4654 (2021). https://doi.org/10.1093/molbev/msab199

• CheckM - (Python, v1.2.3, 2024.06)

  • Parks DH, Imelfort M, Skennerton C, et al. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25, 1043-1055 (2015). https://doi.org/10.1101/gr.186072.114

• CheckM2 - (Python, v1.0.2, 2023.05)

  • Chklovski A, Parks DH, Woodcroft BJ et al. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods 20, 1203–1212 (2023). https://doi.org/10.1038/s41592-023-01940-w

• RefineM - (Python, v0.1.2, 2020.11) - Unsupported

  • Parks DH, Rinke C, Chuvochina M, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol 2, 1533–1542 (2017). https://doi.org/10.1038/s41564-017-0012-7

• MAGpurify - (Python, v2.1.2, 2020.03)

  • Nayfach S, Shi ZJ, Seshadri R, et al. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019). https://doi.org/10.1038/s41586-019-1058-x

• GUNC - (Python, v1.0.6, 2023.11)

  • Orakov A, Fullam A, Coelho LP, et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol 22, 178 (2021). https://doi.org/10.1186/s13059-021-02393-0

• DFAST_QC - (Python, v1.0.5, 2024.09)

  • Elmanzalawi M, Fujisawa T, Mori H, et al. DFAST_QC: quality assessment and taxonomic identification tool for prokaryotic Genomes. BMC Bioinformatics 26, 3 (2025). https://doi.org/10.1186/s12859-024-06030-y

• dRep - (Python, v3.4.2, 2023.02)

  • Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. The ISME Journal 11(12), 2864–2868 (2017). https://doi.org/10.1038/ismej.2017.126

• galah - (Rust, v0.4.2, 2024.09) - More scalable dereplication for metagenome assembled genomes

• 'skDER' - (Python, v1.3.3, 2025.7)

  • Salamzade R, Kottapalli A, Kalan LR. skDER and CiDDER: two scalable approaches for microbial genome dereplication. Microbial Genomics 11(7) (2025). https://doi.org/10.1099/mgen.0.001438

• DAS Tool - (R/Ruby, v1.1.7, 2024.01)

  • Sieber CMK, Probst AJ, Sharrar A, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol 3, 836–843 (2018). https://doi.org/10.1038/s41564-018-0171-1

• Binette - (Python, v1.0.3, 2024.9) - A fast and accurate binning refinement tool to constructs high quality MAGs from the output of multiple binning tools

• MAGqual - (Python, v0.3.0, 2024.8)

  • Cansdale A, Chong JPJ. MAGqual: a stand-alone pipeline to assess the quality of metagenome-assembled genomes. Microbiome 12, 226 (2024). https://doi.org/10.1186/s40168-024-01949-z

Other types of genome recovery

• BEREN - (Python) - No Release Tag
  • Minch B, Moniruzzaman M. BEREN: A bioinformatic tool for recovering Giant viruses, Polinton-like Viruses, and Virophages in metagenomic data. bioRxiv (2024). https://doi.org/10.1101/2024.10.09.617401

Gene and element prediction

ORF

• Prodigal - (C, v2.6.3, 2016.02)

  • Hyatt D, Chen GL, LoCascio PF, et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010). https://doi.org/10.1186/1471-2105-11-119

• Pyrodigal - (Cython/Python, v3.5.2, 2024.9)

  • Larralde M. Pyrodigal: Python bindings and interface to Prodigal, an efficient method for gene prediction in prokaryotes. J Open Source Software 7(72), 4296 (2022). https://doi.org/10.21105/joss.04296

• Prokka - (Perl, v1.14.5, 2019.11)

  • Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30(14), 2068–2069 (2014). https://doi.org/10.1093/bioinformatics/btu153

Non-coding RNA

• Barrnap - (Perl/Shell, 0.9, 2018.4) - BAsic Rapid Ribosomal RNA Predictor. Barrnap predicts the location of ribosomal RNA genes in genomes

• RNAmmer - v1.2

• tRNAscan-SE 2.0 - (C/Perl, v2.0.12, 2022.11)

  • Chan PP, Lin BY, Mak AJ, et al. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res 49(16), 9077–9096 (2021). https://doi.org/10.1093/nar/gkab688

• tRNAscan-SE - Web: https://trna.ucsc.edu/tRNAscan-SE

  • Chan PP, Lowe TM. tRNAscan-SE: Searching for tRNA Genes in Genomic Sequences. In: Kollmar, M. (eds) Gene Prediction. Methods in Molecular Biology 1962. Humana, New York, NY (2019). https://doi.org/10.1007/978-1-4939-9173-0_1

• PILER-CR - v1.06

  • Edgar RC. PILER-CR: Fast and accurate identification of CRISPR repeats. BMC Bioinformatics 8, 18 (2007). https://doi.org/10.1186/1471-2105-8-18

• pybarrnap - (Python, v0.5.0, 2024.3) - pybarrnap: Python implementation of barrnap [Computer software]

Signal peptides

• SignalP - (Python, v6, 2022.1) - online prediction: https://services.healthtech.dtu.dk/services/SignalP-6.0/

  • Teufel F, Almagro Armenteros JJ, Johansen AR, et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol 40, 1023–1025 (2022). https://doi.org/10.1038/s41587-021-01156-3

• DeepLocPro - web api: https://ku.biolib.com/deeplocpro

  • Moreno J, Nielsen H, Winther O, et al. Predicting the subcellular location of prokaryotic proteins with DeepLocPro. Bioinformatics 40(12), btae677 (2024). https://doi.org/10.1093/bioinformatics/btae677

Plasmid databases

• PIPdb - plasmids in pathogens, web: https://nmdc.cn/pipdb, 2024.9

  • Zhu Q, Chen Q, Gao S, et al. PIPdb: a comprehensive plasmid sequence resource for tracking the horizontal transfer of pathogenic factors and antimicrobial resistance genes. Nucleic Acids Res 53(D1), D169–D178 (2025). https://doi.org/10.1093/nar/gkae952

• PlasmidScope - Website: https://plasmid.deepomics.org - download database

  • Li Y, Feng X, Chen X, et al. PlasmidScope: a comprehensive plasmid database with rich annotations and online analytical tools. Nucleic Acids Res 53(D1), D179–D188 (2025). https://doi.org/10.1093/nar/gkae930

Taxonomy profile

Profile read

• MetaPhlAn - (Python, v4.1.1, 2024.5)

  • Blanco-Míguez A, Beghini F, Cumbo F, et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol 41, 1633–1644 (2023). https://doi.org/10.1038/s41587-023-01688-w

• StrainPhlAn - (Python, v4.1.1, 2024.5)

  • Truong DT, Tett A, Pasolli E, et al. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res 27, 626-638 (2017). https://dx.doi.org/10.1101/gr.216242.116

• kraken2 - (C++, v2.1.3, 2023.6)

  • Wood DE, Lu J & Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019). https://doi.org/10.1186/s13059-019-1891-0

• Metabuli - (C++, v1.0.7, 2024.9)

  • Kim J, Steinegger M. Metabuli: sensitive and specific metagenomic classification via joint analysis of amino acid and DNA. Nat Methods 21, 971–973 (2024). https://doi.org/10.1038/s41592-024-02273-y

• Metabuli-App - (js, v1.0.1, 2025.1) - compatible with macOS/Windows/Linux

  • Lee SJ, Kim J, Mirdita M, et al. Easy and interactive taxonomic profiling with Metabuli App. bioRxiv (2025). https://doi.org/10.1101/2025.03.10.642298

• sylph - (Rust, v0.6.1, 2024.4)

  • Shaw J, Yu YW. Rapid species-level metagenome profiling and containment estimation with sylph. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02412-y

• Kaiju - (C, v1.10.1, 2024.3)

  • Menzel P, Ng K & Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun 7, 11257 (2016). https://doi.org/10.1038/ncomms11257

• mOTUs-db - web

  • Dmitrijeva M, Ruscheweyh HJ, Feer L, et al. The mOTUs online database provides web-accessible genomic context to taxonomic profiling of microbial communities. Nucleic Acids Res 53(D1), D797–D805 (2025). https://doi.org/10.1093/nar/gkae1004

• mOTUs - (Python, v3.1.0, 2023.10)

  • Ruscheweyh HJ, Milanese A, Paoli L, et al. Cultivation-independent genomes greatly expand taxonomic-profiling capabilities of mOTUs across various environments. Microbiome 10, 212 (2022). https://doi.org/10.1186/s40168-022-01410-z

• melon - (Python, v0.2.4, 2024.12)

  • Chen X, Yin X, Shi X, et al. Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes. Genome Biol 25, 226 (2024). https://doi.org/10.1186/s13059-024-03363-y

• kun-peng - (Rust, v0.7.4, 2024.9)

  • Chen Q, Zhang B, Peng C, et al. Kun-peng: an ultra-memory-efficient, fast, and accurate pan-domain taxonomic classifier for all. bioRxiv (2024). https://doi.org/10.1101/2024.12.19.629356

• MNBC - (Java, v1.2, 2025.1)

  • Lu R, Dumonceaux T, Anzar M, et al. MNBC: a multithreaded Minimizer-based Naïve Bayes Classifier for improved metagenomic sequence classification. Bioinformatics 40(10), btae601 (2024). https://doi.org/10.1093/bioinformatics/btae601

Profile contig

• MEGAN6

  • Huson DH, Albrecht B, Bağcı C, et al. MEGAN-LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs. Biol Direct 13, 6 (2018). https://doi.org/10.1186/s13062-018-0208-7

• MMseq2/easy-taxonomy - (C/C++, v15-6f452, 2023.10)

  • Mirdita M, Steinegger M, Breitwieser F, et al. Fast and sensitive taxonomic assignment to metagenomic contigs. Bioinformatics 37(18), 3029–3031 (2021). https://doi.org/10.1093/bioinformatics/btab184

• CDKAM - (C++/Perl, v1.1, 2020.11)

  • Bui VK, Wei C. CDKAM: a taxonomic classification tool using discriminative k-mers and approximate matching strategies. BMC Bioinformatics 21, 468 (2020). https://doi.org/10.1186/s12859-020-03777-y

• MetaMaps - (Perl/C++)

  • Dilthey AT, Jain C, Koren S, et al. Strain-level metagenomic assignment and compositional estimation for long reads with MetaMaps. Nat Commun 10, 3066 (2019). https://doi.org/10.1038/s41467-019-10934-2

• BugSeq - Web Online, 可试用

  • Fan J, Huang S & Chorlton SD. BugSeq: a highly accurate cloud platform for long-read metagenomic analyses. BMC Bioinformatics 22, 160 (2021). https://doi.org/10.1186/s12859-021-04089-5

• Vamb/Taxometer - (Python, v4.1.3, 2023.6)

  • Kutuzova S, Nielsen M, Piera P, et al. Taxometer: Improving taxonomic classification of metagenomics contigs. Nat Commun 15, 8357 (2024). https://doi.org/10.1038/s41467-024-52771-y

• geNomad - (Python, v1.8.0, 2024.4)

  • Camargo AP, Roux S, Schulz F, et al. Identification of mobile genetic elements with geNomad. Nat Biotechnol 42, 1303–1312 (2024). https://doi.org/10.1038/s41587-023-01953-y

• VITAP - (Python, v1.7.1, 2025.2)

  • Zheng K, Sun J, Liang Y, et al. VITAP: a high precision tool for DNA and RNA viral classification based on meta-omic data. Nat Commun 16, 2226 (2025). https://doi.org/10.1038/s41467-025-57500-7

MAG taxonomy

• GTDB-Tk - (Python, v1.7.0, 2021.10) - A tools for genome classification based on GTDB. Allocating multiple CPUs can cause a dramatic increase in memory usage. Therefore, the program is typically run with a single CPU. It usually requires large memory, and small nodes (eg, with 120Gb RAM) are generally insufficient.

  • Chaumeil PA, Mussig AJ, Hugenholtz P, et al. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36(6), 1925–1927 (2020). https://doi.org/10.1093/bioinformatics/btz848

• GTDB-Tk 2 - (Python, v2.4.1, 2025.04)

  • Chaumeil PA, Mussig AJ, Hugenholtz P, et al. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics 38(23), 5315–5316 (2022). https://doi.org/10.1093/bioinformatics/btac672

• tronko - C

  • Pipes L, Nielsen R. A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets. eLife 13, e85794 (2024). https://doi.org/10.7554/eLife.85794

• kMetaShot - (Python,2024.9)

  • Defazio G, Tangaro MA, Pesole G, et al. kMetaShot: a fast and reliable taxonomy classifier for metagenome-assembled genomes. Briefings in Bioinformatics 26(1), bbae680 (2025). https://doi.org/10.1093/bib/bbae680

Annotation

Tools for annotation

• eggNOG-mapper v2 - (Python, v2.1.12, 2023.8)

  • Cantalapiedra CP, Hernández-Plaza A, Letunic I, et al. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol Biol Evol 38(12), 5825–5829 (2021). https://doi.org/10.1093/molbev/msab293

• KofamKOALA - (Ruby, v1.3.0, 2020.05) - A web version

  • Aramaki T, Blanc-Mathieu R, Endo H, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36(7), 2251–2252 (2020). https://doi.org/10.1093/bioinformatics/btz859

• BlastKOALA, GhostKOALA - Web

  • Kanehisa M, Sato Y, and Morishima K。 BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol 428, 726-731 (2016). https://doi.org/10.1016/j.jmb.2015.11.006

• Macrel - (Python, v1.5.0, 2024.9)

  • Santos-Júnior CD, Pan S, Zhao X, Coelho LP. Macrel: antimicrobial peptide screening in genomes and metagenomes. PeerJ 8, e10555 (2020). https://doi.org/10.7717/peerj.10555

• RGI - (Python, 6.0.3, 2023.9) - This tool is designed for resistome prediction from protein or nucleotide data, with a complementary database required.

  • Alcock BP, Huynh W, Chalil R, et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res 51(D1), D690–D699 (2023). https://doi.org/10.1093/nar/gkac920

• MetaCerberus - (Python, v1.4.0, 2024.8)

  • Figueroa JL III, Dhungel E, Bellanger M, et al. MetaCerberus: distributed highly parallelized HMM-based processing for robust functional annotation across the tree of life. Bioinformatics 40(3), btae119 (2024). https://doi.org/10.1093/bioinformatics/btae119

• GMSC-mapper - (Python, v0.1.0, 2024.04)

  • Duan Y, Santos-Júnior CD, Schmidt TS, et al. A catalog of small proteins from the global microbiome. Nat Commun 15, 7563 (2024). https://doi.org/10.1038/s41467-024-51894-6

• Bakta - (Python, v1.10.1, 2024.11) - web: https://bakta.computational.bio

  • Schwengers O, Jelonek L, Dieckmann MA, et al. Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification. Microbial Genomics 7, 11 (2021). https://doi.org/10.1099/mgen.0.000685

• DRAM - (Python, v1.5.0, 2024.1)

  • Shaffer M, Borton MA, McGivern BB, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res 48(16), 8883–8900 (2020). https://doi.org/10.1093/nar/gkaa621

• graftM - (Python, v0.14.0, 2022.5)

  • Boyd JA, Woodcroft BJ, Tyson GW. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res 46(10), e59 (2018). https://doi.org/10.1093/nar/gky174

• CD-Search - Web

  • Marchler-Bauer A, Bryant SH. CD-Search: protein domain annotations on the fly. Nucleic Acids Res 32(suppl_2), W327–W331 (2004). https://doi.org/10.1093/nar/gkh454

• HMMER3 - (C, v3.4, 2023.8)

  • Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol 7(10), e1002195 (2011). https://doi.org/10.1371/journal.pcbi.1002195

• DeepARG - (Python2.7, 2023.11)

  • Arango-Argoty G, Garner E, Pruden A, et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6, 23 (2018). https://doi.org/10.1186/s40168-018-0401-z

• ARGs-OAP - (Python, v3.2.4, 2023.10)

  • Yang Y, Jiang X, Chai B, et al. ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database. Bioinformatics 32(15), 2346–2351 (2016). https://doi.org/10.1093/bioinformatics/btw136

• ARGs-OAP3.0 - (Python, v3.2.4, 2023.10)

  • Yin X, Zheng X, Li L, et al. ARGs-OAP v3.0: Antibiotic-Resistance Gene Database Curation and Analysis Pipeline Optimization. Engineering 27, 234-241 (2023). https://doi.org/10.1016/j.eng.2022.10.011

• argNorm - (Python, v0.7.0, 2025.3)

  • Perovic SU, Ramji V, Chong H, et al. argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO). Bioinformatics 41(5), btaf173 (2025). https://doi.org/10.1093/bioinformatics/btaf173

• OrthoLoger - (bash, v3.5.0, 2024.10)

  • Kuznetsov D, Tegenfeldt F, Manni M, et al. OrthoDB v11: annotation of orthologs in the widest sampling of organismal diversity. Nucleic Acids Res 51(D1), D445–D451 (2023). https://doi.org/10.1093/nar/gkac998

• nail - (Rust, v0.2.0, 2024.7)

  • Roddy JW, Rich DH, Wheeler TJ. nail: software for high-speed, high-sensitivity protein sequence annotation. bioRxiv (2024). https://doi.org/10.1101/2024.01.27.577580

• FastOMA - (Python, v0.3.4, 2024.9)

  • Majidian S, Nevers Y, Yazdizadeh Kharrazi A, et al. Orthology inference at scale with FastOMA. Nat Methods 22, 269–272 (2025). https://doi.org/10.1038/s41592-024-02552-8

• BenchAMRking - web workflow AAMR detection

  • Strepis N, Dollee D, Vrins D, et al. BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows. BMC Genomics 26, 27 (2025). https://doi.org/10.1186/s12864-024-11158-5

• OMArk - (Python, v0.3.0, 2023.10)

  • Nevers Y, Warwick Vesztrocy A, Rossier V, et al. Quality assessment of gene repertoire annotations with OMArk. Nat Biotechnol 43, 124–133 (2025). https://doi.org/10.1038/s41587-024-02147-w

• mettannotator - (Nextflow, 2024.12, v1.4.0)

  • Gurbich TA, Beracochea M, De Silva NH, et al. mettannotator: a comprehensive and scalable Nextflow annotation pipeline for prokaryotic assemblies. Bioinformatics 41(2), btaf037 (2025). https://doi.org/10.1093/bioinformatics/btaf037

• pseudofinder - (Python, v1.1.0, 2022.3)

  • Syberg-Olsen MJ, Garber AI, Keeling PJ, et al. Pseudofinder: Detection of Pseudogenes in Prokaryotic Genomes. Mol Biol Evol 39(7), msac153 (2022). https://doi.org/10.1093/molbev/msac153

• AntiDeffenseFinder - Web Service

  • Tesson F, Huiting E, Wei L, et al. Exploring the diversity of anti-defense systems across prokaryotes, phages and mobile genetic elements. Nucleic Acids Res 53(1), gkae1171 (2025). https://doi.org/10.1093/nar/gkae1171

• Genomic conttext - Python - a step by step tutorial

  • Toibazar D, Kulmanov M, Hoehndorf R. Context-based protein function prediction in bacterial genomes. bioRxiv (2024). Context-based protein function prediction in bacterial genomes

• OMAnnotator, Python

  • Bates S, Dessimoz C, Nevers Y. OMAnnotator: a novel approach to building an annotated consensus genome sequence. bioRxiv (2024). https://doi.org/10.1101/2024.12.04.626846

• MHCScan - Python

  • Garber AI, Nealson KH and Merino N. Large-scale prediction of outer-membrane multiheme cytochromes uncovers hidden diversity of electroactive bacteria and underlying pathways. Front Microbiol 15, 1448685 (2024). https://doi.org/10.3389/fmicb.2024.1448685

• DRAMMA - Python

  • Rannon E, Shaashua S & Burstein D. DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data. Microbiome 13, 67 (2025). https://doi.org/10.1186/s40168-025-02055-4

• ChroQueTas - (Shell, v0.4.2, 2024.10) - Fungi, work with FungAMR

  • Bédard C, Pageau A, Fijarczyk A, et al. FungAMR: A comprehensive portrait of antimicrobial resistance mutations in fungi. bioRxiv (2025). https://doi.org/10.1101/2024.10.07.617009

• MICROPHERRET - Python

  • Bizzotto E, Fraulini S, Zampieri G, et al. MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques. Environmental Microbiome 19, 58 (2024). https://doi.org/10.1186/s40793-024-00600-6

Databases for annotation

• eggNOG 6.0 - (AA Genes, v6.0, 2022.09)

  • Hernández-Plaza A, Szklarczyk D, Botas J, et al. eggNOG 6.0: enabling comparative genomics across 12 535 organisms. Nucleic Acids Res 51(D1), D389–D394 (2023). https://doi.org/10.1093/nar/gkac1022

• KEGG - (AA Genes, 111.0, 2024.08)

  • Kanehisa M, Sato Y, Kawashima M, et al. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44(D1), D457–D462 (2016). https://doi.org/10.1093/nar/gkv1070

• CAZy - AA, web

  • Drula E, Garron ML, Dogan S, et al. The carbohydrate-active enzyme database: functions and literature. Nucleic Acids Res 50(D1), D571–D577 (2022). https://doi.org/10.1093/nar/gkab1045

• CARD -（ARG, v3.3.0, 2024.08）

  • Alcock BP, Huynh W, Chalil R, et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res 51(D1), D690–D699 (2023). https://doi.org/10.1093/nar/gkac920

• UniProt - (AA genes, v2024_5)

  • The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1), D523–D531 (2023) , https://doi.org/10.1093/nar/gkac1052
  • The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Res 53(D1), D609–D617 (2025). https://doi.org/10.1093/nar/gkae1010

• ISOSDB - (IS Sequences, v3, 2023.12)

  • Kirsch JM, Hryckowian A, Duerkop B. A metagenomics pipeline reveals insertion sequence-driven evolution of the microbiota. Cell Host Microbe 32(5), 739-754.e4 (2024). https://doi.org/10.1016/j.chom.2024.03.005

• AMPSphere - (AMP, v2022-03, 2022.03)

  • Santos-Júnior CD, Torres MDT, Duan Y, et al. Discovery of antimicrobial peptides in the global microbiome with machine learning. Cell 187(14), 3761-3778.e16 (2024). https://doi.org/10.1016/j.cell.2024.05.013

• DRAMP - (AMP, v4.0, 2024.09)

  • Shi G, Kang X, Dong F, et al. DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides. Nucleic Acids Res 50(D1), D488–D496 (2022). https://doi.org/10.1093/nar/gkab651

• GMSC - (smORF, v1.0, 2024.08)

  • Duan Y, Santos-Júnior CD, Schmidt TS et al. A catalog of small proteins from the global microbiome. Nat Commun 15, 7563 (2024). https://doi.org/10.1038/s41467-024-51894-6

• TCDB - (transporters, 2024-09, 2024.09)

  • Saier MH, Reddy VS, Moreno-Hagelsieb G, et al. The Transporter Classification Database (TCDB): 2021 update. Nucleic Acids Res 49(D1), D461–D467 (2021). https://doi.org/10.1093/nar/gkaa1004

• VFDB - (virulence factors, 2024.9)

  • Liu B, Zheng D, Zhou S, et al. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res 50(D1), D912–D917 (2022). https://doi.org/10.1093/nar/gkab1107

• DoriC - (oriC, v12.1)

  • Dong MJ, Luo H, Gao F. DoriC 12.0: an updated database of replication origins in both complete and draft prokaryotic genomes. Nucleic Acids Res 51(D1), D117–D120 (2023). https://doi.org/10.1093/nar/gkac964

• TIGRAFMs - (protein sequence, v15.0, 2014.9)

  • Haft DH, Loftus BJ, Richardson DL, et al. TIGRFAMs: a protein family resource for the functional identification of proteins, Nucleic Acids Res 29(1), 41–43 (2001). https://doi.org/10.1093/nar/29.1.41

• Pfam - (protein sequences, v37.0, 2024.5)

  • Mistry J, Chuguransky S, Williams L, et al. Pfam: The protein families database in 2021. Nucleic Acids Res 49(D1), D412–D419 (2021). https://doi.org/10.1093/nar/gkaa913

• Rfam - (RNA families, v15, 2024.9)

  • Kalvari I, Nawrocki EP, Ontiveros-Palacios N, et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res 49(D1), D192–D200 (2021). https://doi.org/10.1093/nar/gkaa1047

• PHI - (Pathogen Host Interactions, v5.0, 2024.3)

  • Urban M, Cuzick A, Seager J, et al. PHI-base in 2022: a multi-species phenotype database for Pathogen–Host Interactions. Nucleic Acids Res 50(D1), D837–D847 (2022). https://doi.org/10.1093/nar/gkab1037

• oriTDB

  • Liu G, Li X, Guan J, et al. oriTDB: a database of the origin-of-transfer regions of bacterial mobile genetic elements. Nucleic Acids Res 53(D1), D163–D168 (2025). https://doi.org/10.1093/nar/gkae869

• COG - FTP: https://ftp.ncbi.nlm.nih.gov/pub/COG/

  • Galperin MY, Alvarez RV, Karamycheva S, et al. COG database update 2024. Nucleic Acids Res 53(D1), D356–D363 (2025). https://doi.org/10.1093/nar/gkae983

• OrthoDB - v11.0

  • Kuznetsov D, Tegenfeldt F, Manni M, et al. OrthoDB v11: annotation of orthologs in the widest sampling of organismal diversity. Nucleic Acids Res 51(D1), D445–D451 (2023). https://doi.org/10.1093/nar/gkac998

• CAZyme3D - web

  • Shanmugam NRS, Yin Y. CAZyme3D: a database of 3D structures for carbohydrate-active enzymes. J Mol Biol 437, 15 (2025). https://doi.org/10.1016/j.jmb.2025.169001

• S9BactDB - S9 proteases, web

  • Nayak S, Sowdhamini R. S9BactDB: A database for S9 family of proteases in bacterial genomes. bioRxiv (2025). https://doi.org/10.1101/2025.01.01.631042

• FungAMR - (Fungi AMR) - work with ChroQueTas

  • Bédard C, Pageau A, Fijarczyk A, et al. FungAMR: A comprehensive portrait of antimicrobial resistance mutations in fungi. bioRxiv (2024). https://doi.org/10.1101/2024.10.07.617009

Gennome content analysis

• doubletrouble - (R, v1.6.0, 2025.2)

  • Almeida-Silva F, Van de Peer Y. doubletrouble: an R/Bioconductor package for the identification, classification, and analysis of gene and genome duplications. Bioinformatics 41(2), btaf043 (2025). https://doi.org/10.1093/bioinformatics/btaf043

• gyoza - (snakemake, v11.0.0, 2025.2)

  • Durand R, Pageau A, Landry CR. gyōza: a Snakemake workflow for modular analysis of deep-mutational scanning data. bioRxiv (2025). https://doi.org/10.1101/2025.02.19.639168

Protein structure analysis

• Chai-1 - (Python, v0.0.1, 2024.09) - blog: Introducing Chai-1: Decoding the molecular interactions of life, URL: https://www.chaidiscovery.com/blog/introducing-chai-1

• AlphaFold2 - (Python, v2.3.2, 2023.0)

  • Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

• AlphaFold3 - Web Service

  • Abramson J, Adler J, Dunger J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024). https://doi.org/10.1038/s41586-024-07487-w

• DNAproDB - Online database

  • Mitra R, Cohen AS, Sagendorf JM, et al. DNAproDB: an updated database for the automated and interactive analysis of protein–DNA complexes. Nucleic Acids Res 53(D1), D396–D402 (2025). https://doi.org/10.1093/nar/gkae970

Elements

• DeepInverton - Python - NoReleaseTag
  • Wen J, Zhang H, Chu D, et al. Deep learning revealed the distribution and evolution patterns for invertible promoters across bacterial lineages. Nucleic Acids Res 52(21), 12817–12830 (2024). https://doi.org/10.1093/nar/gkae966

Bacteriophage

• SatelliteFinder - Docker, Galaxy Service: https://galaxy.pasteur.fr/root?tool_id=toolshed.pasteur.fr/repos/fmareuil/satellitefinder/SatelliteFinder/0.9

  • Moura de Sousa JA, Fillol-Salom A, Penadés JR, et al. Identification and characterization of thousands of bacteriophage satellites across bacteria. Nucleic Acids Res 51(6), 2759–2777 (2023). https://doi.org/10.1093/nar/gkad123

• Jaeger - (Python, v1.3.30-alpha, 2024.8)

  • Wijesekara Y, Wu LY, Beeloo R, et al. Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences. bioRxiv (2024). https://doi.org/10.1101/2024.09.24.612722

• PhageGE - Web Platform

  • Zhao J, Han J, Lin YW, et al. PhageGE: an interactive web platform for exploratory analysis and visualization of bacteriophage genomes. GigaScience 13, giae074 (2024). https://doi.org/10.1093/gigascience/giae074

• Phynteny - (Jupyter, v0.1.2, 2025.06)

  • Grigson SR, Bouras G, Papudeshi B, et al. Synteny-aware functional annotation of bacteriophage genomes with Phynteny. bioRxiv (2025). https://doi.org/10.1101/2025.07.28.667340

Metabolic construction

Tools for metabolic analysis

• antiSMASH - (Python, v7.1.0.1, 2023.11)

  • Blin K, Shaw S, Augustijn HE, et al. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res 51(W1), W46–W50 (2023). https://doi.org/10.1093/nar/gkad344

• MelonnPan - R, dev

  • Mallick H, Franzosa EA, Mclver LJ, et al. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat Commun 10, 3136 (2019). https://doi.org/10.1038/s41467-019-10927-1

• HUMAnN 2.0 - (Python, 最新2版v2.8.2, 2020.4)

  • Franzosa EA, McIver LJ, Rahnavard G, et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods 15, 962–968 (2018). https://doi.org/10.1038/s41592-018-0176-y

• HUMAnN 3.0 - (Python, 最新3版v3.9, 2024.2)

  • Beghini F, McIver LJ, Blanco-Míguez A, et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10, e65088 (2021). https://doi.org/10.7554/eLife.65088

• HUMAnN 4.0 - (Python, 4.0.0.alpha.1-final, 22024.7) - active dev

• gapseq - (R, v1.3.1, 2024.8)

  • Zimmermann J, Kaleta C & Waschina S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models.Genome Biol 22, 81 (2021). https://doi.org/10.1186/s13059-021-02295-1

• ./gapseq pan - (R, v1.3.1, 2024.8) - The source code has been integrated into gapseq and is accessible via ./gapseq pan

  • De Bernardini N, Zampieri G, Campanaro S, et al. pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes. Genome Biol 25, 280 (2024). https://doi.org/10.1186/s13059-024-03425-1

• Bactabolize - (Python, v1.0.4, 2024.12)

  • Vezina B, Watts SC, Hawkey J, et al. Bactabolize is a tool for high-throughput generation of bacterial strain-specific metabolic models. eLife 12, RP87406 (2023). https://doi.org/10.7554/eLife.87406.3

Metabolic databases

• mVOC 4.0

  • Kemmler E, Lemfack MC, Goede A, et al. mVOC 4.0: a database of microbial volatiles. Nucleic Acids Res 53(D1), D1692–D1696 (2025). https://doi.org/10.1093/nar/gkae961

• SMC - Web

  • Udwary DW, Doering DT, Foster B, et al. The secondary metabolism collaboratory: a database and web discussion portal for secondary metabolite biosynthetic gene clusters. Nucleic Acids Res 53(D1), D717–D723 (2025). https://doi.org/10.1093/nar/gkae1060

Comparative genomics

AAI and ANI

• ANI calculator - web

• AAI calculator - web

• fastANI - (C++, v1.34, 2023.07)

  • Jain C, Rodriguez-R LM, Phillippy AM, et al. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 9, 5114 (2018). https://doi.org/10.1038/s41467-018-07641-9

• Pyskani - (Python, v0.1.3, 2024.12) - PyOrthoANI - (Python, v0.7.0, 2025.2) - PyFastANI - (Python, v0.6.1, 2025.1)

  • Larralde M, Zeller G, Carroll LM. PyOrthoANI, PyFastANI, and Pyskani: a suite of Python libraries for computation of average nucleotide identity. bioRxiv. (2025). https://doi.org/10.1101/2025.02.13.638148

• EzAAI - (Java, v1.2.3, 2024.02)

  • Kim D, Park S & Chun J. Introducing EzAAI: a pipeline for high throughput calculations of prokaryotic average amino acid identity. J Microbiol 59, 476–480 (2021). https://doi.org/10.1007/s12275-021-1154-0

• SynTracker - (Python, v1.3.1, 2024.09)

  • Enav H, Paz I & Ley RE. Strain tracking in complex microbiomes using synteny analysis reveals per-species modes of evolution. Nat Biotechnol 43, 773–783 (2025). https://doi.org/10.1038/s41587-024-02276-2

• skani - (Rust, v0.2.2, 2024.07)

  • Shaw J, Yu, YW. Fast and robust metagenomic sequence comparison through sparse chaining with skani. Nat Methods 20, 1661–1665 (2023). https://doi.org/10.1038/s41592-023-02018-3

• ANIclustermap - (Python, v1.4.0, 2024.7)

  • Shimoyama Y. ANIclustermap: A tool for drawing ANI clustermap between all-vs-all microbial genomes. [Computer software] (2022).

• EvANI - (Shell/Python, Norelease)

  • Sina Majidian, Stephen Hwang, Mohsen Zakeri, Ben Langmead. EvANI benchmarking workflow for evolutionary distance estimation. bioRxiv (2025). https://doi.org/10.1101/2025.02.23.639716

• MANIAC - (Snakemake, v1.0.0, 2024.10) - MMseqs2-based ANI calculator.

  • Ndovie W, Havr´anek J, Leconte J, et al. Exploration of the genetic landscape of bacterial dsDNA viruses reveals an ANI gap amidst extensive mosaicism. bioRxiv (2024). https://doi.org/10.1101/2024.04.23.590796

view comparative map

• gggenes - Draw gene arrow maps in ggplot2

• LoVis4u - (Python, v0.0.11, 2024.10)

  • Egorov AA, Atkinson GC. LoVis4u: Locus Visualisation tool for comparative genomics. bioRxiv (2024). https://doi.org/10.1101/2024.09.11.612399

• gggenomes - gggenomes: A Grammar of Graphics for Comparative Genomics

• plasmapR - Creating plasmid maps inside ggplot

• geneviewer - (R, CRAN release, 2025.1) - An R package designed for drawing gene arrow maps

HGT

• WAFFLE - (Python, v1.1.0, 2024.10)

  • Hsu TY, Nzabarushimana E, Wong D, et al. Profiling lateral gene transfer events in the human microbiome using WAAFLE. Nat Microbiol 10, 94–111 (2025). https://doi.org/10.1038/s41564-024-01881-w

• SCARAP - (Python, v1.0.0, 2024.11)

  • Wittouck S, Eilers T, van Noort V, et al. SCARAP: scalable cross-species comparative genomics of prokaryotes. Bioinformatics 41(1), btae735 (2025). https://doi.org/10.1093/bioinformatics/btae735

SV

• SVbyEye - R, active dev

  • Porubsky D, Guitart X, Yoo DA, et al. SVbyEye: A visual tool to characterize structural variation among whole genome assemblies. bioRxiv (2024). https://doi.org/10.1101/2024.09.11.612418

• CompareM - (Python, v0.1.2, 2020.12) - Unsupported. Last version relsease on Dec 31 2020

• gcSV - C/C++, human

  • Li G, Liu Y, Liu B, et al. gcSV: a unified framework for comprehensive structural variant detection. bioRxiv (2025). https://doi.org/10.1101/2025.02.10.637589

• minipileup - (C, v1.1, 2025.2) - Minipileup is a simple pileup-based variant caller. It takes a reference FASTA and one or multiple alignment BAM as input, and outputs a multi-sample VCF along with allele counts

Visualization

View MSA

• MSAplot - Python, no release tag. Plot multiple sequence alignment (MSA),with Jupyter notes

• pyMSAviz - (Python, v0.5.0, 2024.9)

  • Shimoyama Y. pyMSAviz: MSA visualization python package for sequence analysis [Computer software] (2022)

• ggmsa - (R, v1.0.2, 2021.8) - Visualizing publication-quality multiple sequence alignment using ggplot2,, CRAN移除了

• IGV

  • Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings in Bioinformatics 14(2), 178–192 (2013). https://doi.org/10.1093/bib/bbs017

• pymsaploter - plot Multiple Sequence Alignment (MSA) of Clustal by python package, nuc only

View genome

• pyGenomeViz - (Python, v1.4.1, 2024.9) - A genome visualization python package for comparative genomics

• pyCircos - (Python, v0.3.0, 2022.4)

• pyCirclize - (Python, v1.7.1, 2024.9) - Shimoyama Y. (2022). pyCirclize: Circular visualization in Python [Computer software]

• circlize - (R, v0.4.16, 2024.2)

  • Gu Z, Gu L, Eils R, et al. circlize implements and enhances circular visualization in R. Bioinformatics 30(19), 2811–2812 (2014). https://doi.org/10.1093/bioinformatics/btu393

View assemblies

• Bandage - (C++, v0.9.0, 2022.1)
  • Wick RR, Schultz MB, Zobel J, et al. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31(20), 3350–3352, (2015). https://doi.org/10.1093/bioinformatics/btv383

Phylogenetics

Build a tree

• IQ-TREE - (Java, v2.3.6, 2024.8)

  • Minh BQ, Schmidt HA, Chernomor O, et al. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol Biol Evol 37(5), 1530–1534 (2020). https://doi.org/10.1093/molbev/msaa015

• iqtree -m - (Java, v2.3.6, 2024.8)

  • Kalyaanamoorthy S, Minh B, Wong T, et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14, 587–589 (2017). https://doi.org/10.1038/nmeth.4285

• fasttree2 - (C++, v2.1.11)

  • Price MN, Dehal PS, Arkin AP. FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE 5(3), e9490 (2010). https://doi.org/10.1371/journal.pone.0009490

• PhyloPhlAn - (Python, v3.1.1, 2024.03)

  • Asnicar F, Thomas AM, Beghini F, et al. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat Commun 11, 2500 (2020). https://doi.org/10.1038/s41467-020-16366-7

• MrBayes - C

  • Huelsenbeck JP, Ronquist F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17(8), 754–755 (2001). https://doi.org/10.1093/bioinformatics/17.8.754

• MrBayes - (C, v3)

  • Ronquist F, Huelsenbeck JP. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19(12), 1572–1574 (2003). https://doi.org/10.1093/bioinformatics/btg180

• MrBayes - (C, v3.2.7, 2020.1)

  • Ronquist F, Teslenko M, van der Mark P, et al. MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space. Systematic Biology 61(3), 539–542, (2012). https://doi.org/10.1093/sysbio/sys029

• trimAI - (C++, v1.5.0, 2024.7)

  • Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25(15), 1972–1973 (2009). https://doi.org/10.1093/bioinformatics/btp348

• RAxML-NG - (C++, v1.2.2, 2024.5)

  • Kozlov AM, Darriba D, Flouri T, et al. RAxML-NG: a fast, scalable and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35(21), 4453–4455 (2019). https://doi.org/10.1093/bioinformatics/btz305

• getphylo - Python

  • Booth TJ, Shaw S, Cruz-Morales P, et al. getphylo: rapid and automatic generation of multi-locus phylogenetic trees. BMC Bioinformatics 26, 21 (2025). https://doi.org/10.1186/s12859-025-06035-1

• Poplar - Python

  • Elizabeth Koning, Raga Krishnakumar. Poplar: A Phylogenetics Pipeline. bioRxiv (2024). https://doi.org/10.1101/2024.11.11.623070

• WitChi - Python

  • Köstlbacher S, Panagiotou K, Tamarit D, Ettema TJG. WitChi: Efficient Detection and Pruning of Compositional Bias in Phylogenomic Alignments Using Empirical Chi-Squared Testing. bioRxiv (2025). https://doi.org/10.1101/2025.07.14.663642

View tree

• iTol - Web

  • Letunic I, Bork P. Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res 52(W1), W78–W82 (2024). https://doi.org/10.1093/nar/gkae268

• ARB - (需要转发, arb-7.0, 2021.09)

  • Ludwig W, Strunk O, Westram R, et al. ARB: a software environment for sequence data. Nucleic Acids Res 32(4), 1363–1371 (2004). https://doi.org/10.1093/nar/gkh293

• FigTree - (Java, v1.4.5-pre, 2024.02)

• GraPhlAn - (Python, 1.1.3, 2020.06)

  • Asnicar F, Weingart G, Tickle TL, et al. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029 (2015). https://doi.org/10.7717/peerj.1029

• phyTreeViz - (Python, v0.2.0, 2024.1) - Shimoyama Y. (2023). phyTreeViz: Simple phylogenetic tree visualization python package [Computer software]

Microbial diversity analysis

Abundance

• coverM - (Rust, v0.7.0, 2024.1)

  • Aroney STN, Newell R, Nissen J, et al. CoverM: read alignment statistics for metagenomics. Bioinformatics 41(4), btaf147 (2025). https://doi.org/10.1093/bioinformatics/btaf147

• Fairy - (Rust, v0.5.5, 2024.5)

  • Shaw J, Yu Y. Fairy: fast approximate coverage for multi-sample metagenomic binning. Microbiome 12, 151 (2024). https://doi.org/10.1186/s40168-024-01861-6

• Tronko - C, NoRelese

  • Pipes L, Nielsen R. A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets. eLife 13, e85794 (2024). https://doi.org/10.7554/eLife.85794

• MUSET - (C++, v0.5.1, 2024.12)

  • Vicedomini R, Andreace F, Dufresne Y, et al. MUSET: set of utilities for constructing abundance unitig matrices from sequencing data. Bioinformatics 41(3), btaf054 (2025). https://doi.org/10.1093/bioinformatics/btaf054

• SingleM Microbial Fraction(SMF) - Python

  • Eisenhofer R, Alberdi A, Woodcroft BJ. Quantifying microbial DNA in metagenomes improves microbial trait estimation. ISME Communications 4(1), ycae111 (2024). https://doi.org/10.1093/ismeco/ycae111

• Lyrebird - Python - A tool developed by Samuel Aroney

• SingleM - (Python, v0.19.0, 2025.5)

  • Woodcroft BJ, Aroney STN, Zhao R, et al. Comprehensive taxonomic identification of microbial species in metagenomic data using SingleM and Sandpiper. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02738-1

• AEMB implemented in strobealign - Benchmark: https://github.com/BigDataBiology/AEMB_benchmark

  • Pan S, Tolstoganov I, Coelho L, et al. AEMB: efficient abundance estimation for metagenomic binning. bioRxiv (2025). https://doi.org/10.1101/2025.07.30.667338

Diversity

• q2-kmerizer
  • Bokulich NA. Integrating sequence composition information into microbial diversity analyses with k-mer frequency counting. mSystems 10 (2025). https://doi.org/10.1128/msystems.01550-24

Network

• SpeSpeNet - v1.0 - Web: https://tbb.bio.uu.nl/SpeSpeNet

  • van Eijnatten AL, van Zon L, Manousou E, et al. SpeSpeNet: an interactive and user-friendly tool to create and explore microbial correlation networks. ISME Communications ycaf036 (2025). https://doi.org/10.1093/ismeco/ycaf036

• LUPINE - (R, NoReleaseTag) - tutorial: https://mixomics.org/2024/05/lupine/

  • Kodikara S, Lê Cao KA. Microbial network inference for longitudinal microbiome studies with LUPINE. Microbiome 13, 64 (2025). https://doi.org/10.1186/s40168-025-02041-w

Interactions

• PHLAME - (Python, NoRelease Tag)

  • Qu EB, Baker JS, Markey L, et al. Intraspecies associations from strain-rich metagenome samples. bioRxiv. (2025) https://doi.org/10.1101/2025.02.07.636498

• iPHoP - (Python, v1.3.3, 2023.11)

  • Roux S, Camargo AP, Coutinho FH, et al. iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria. PLoS Biol 21(4), e3002083 (2023). https://doi.org/10.1371/journal.pbio.3002083

• Anpan - R

  • Ghazi AR, Thompson KN, Bhosle A, et al. Quantifying Metagenomic Strain Associations from Microbiomes with Anpan. bioRxiv. (2025). https://doi.org/10.1101/2025.01.06.631550

Metatranscriptomics

RNA quantification

• sailfish - (C++, v0.10.0, 2016.4)

  • Patro R, Mount S & Kingsford C Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nat Biotechnol 32, 462–464 (2014). https://doi.org/10.1038/nbt.2862

• SortMeRNA - (C++/C, v4.3.7, 2024.04)

  • Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28(24), 3211–3217 (2012). https://doi.org/10.1093/bioinformatics/bts611

• salmon - (C++, v1.10.1, 2023.03)

  • Patro R, Duggal G, Love M, et al. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14, 417–419 (2017). https://doi.org/10.1038/nmeth.4197

• RiboDetector - (Python, v0.3.1, 2024.1)

  • Deng ZL, Münch PC, Mreches R, et al. Rapid and accurate identification of ribosomal RNA sequences via deep learning. Nucleic Acids Res 50(10), e60 (2022). https://doi.org/10.1093/nar/gkac112

• kallisto - (C/C++, v0.51.1, 2024.9)

  • Bray N, Pimentel H, Melsted P, et al. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34, 525–527 (2016). https://doi.org/10.1038/nbt.3519

• NanoCount - (Python, v1.1.0, 2022.12)

  • Gleeson J, Leger A, Prawer YDJ, et al. Accurate expression quantification from nanopore direct RNA sequencing with NanoCount. Nucleic Acids Res 50(4), e19 (2022). https://doi.org/10.1093/nar/gkab1129

• step by step shell and python code

  • Mameda R, Bono H. Data-driven workflow for comprehensive gene expression analysis in complex. bioRxiv. (2025). https://doi.org/10.1101/2025.01.17.632662

RNA assembly

• SPAdes/rnaSPAdes - (C++, v4.0.0, 2024.6)

  • Bushmanova E, Antipov D, Lapidus A, et al. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. GigaScience 8(9), giz100 (2019). https://doi.org/10.1093/gigascience/giz100

• idba/IDBA-tran - (C++, v1.1.3, 2016.7)

  • Peng Y, Leung HCM, Yiu SM, et al. IDBA-tran: a more robust de novo de Bruijn graph assembler for transcriptomes with uneven expression levels, Bioinformatics 29(13), 326–334 (2013). https://doi.org/10.1093/bioinformatics/btt219

RNA SV

• Clair3-RNA - (Python, 2025.1, v0.2.1)
  • Zheng Z, Yu X, Chen L, et al. Clair3-RNA: A deep learning-based small variant caller for long-read RNA sequencing data. bioRxiv (2024). https://doi.org/10.1101/2024.11.17.624050

Stats

• GraphPCA - (Python, GraphPCA-746ef16, 2024.8)
  • Yang J, Wang L, Liu L, et al. GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data. Genome Biol 25, 287 (2024). https://doi.org/10.1186/s13059-024-03429-x

Surveillance

• VirDetector - Nextflow, NoRelease

  • Kaiser NL, Groschup MH, Sadeghi B. VirDetector: a bioinformatic pipeline for virus surveillance using nanopore sequencing. Bioinformatics 41(2), btaf029 (2025). https://doi.org/10.1093/bioinformatics/btaf029

• Castaanet - (Python, v8.1, 2025.1)

  • Mayne R, Secret S, Geoghegan C, et al. Castanet: a pipeline for rapid analysis of targeted multi-pathogen genomic data. Bioinformatics 40(10), btae591 (2024). https://doi.org/10.1093/bioinformatics/btae591

Modifications

• Robin - (Python, Robin-d2f9b3, 2025.1) - A package to run real time analysis of nanopore methylation data

  • Deacon S, Cahyani I, Holmes N, et al. ROBIN: A unified nanopore-based assay integrating intraoperative methylome classification and next-day comprehensive profiling for ultra-rapid tumor diagnosis. Neuro-Oncology noaf103 (2025). https://doi.org/10.1093/neuonc/noaf103

• uncalled4 - (Python, v4.1.0, 2024.8)

  • Kovaka S, Hook PW, Jenike KM, et al. Uncalled4 improves nanopore DNA and RNA modification detection via fast and accurate signal alignment. Nat Methods 22, 681–691 (2025). https://doi.org/10.1038/s41592-025-02631-4

Pangenome related

• PanGraph - (Julia/Python, v0.7.3, 2023.11)

  • Noll N​, Molari M​, Shaw LP and Neher RA. PanGraph: scalable bacterial pan-genome graph construction. Microbial Genomics 9, 6 (2023). https://doi.org/10.1099/mgen.0.001034

• PanGenie - (C++, v3.1.0, 2024.3)

  • Ebler J, Ebert P, Clarke WE et al. Pangenome-based genome inference allows efficient and accurate genotyping across a wide spectrum of variant classes. Nat Genet 54, 518–525 (2022). https://doi.org/10.1038/s41588-022-01043-w

• Panaroo - (Python, v1.5.0, 2024.4)

  • Tonkin-Hill G, MacAlasdair N, Ruis C, et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol 21, 180 (2020). https://doi.org/10.1186/s13059-020-02090-4

• CELEBRIMBOR - (Python, v0.1.2, 2024.7)

  • Hellewell J, Horsfield ST, von Wachsmann J, et al. CELEBRIMBOR: core and accessory genes from metagenomes. Bioinformatics 40(9), btae542 (2024). https://doi.org/10.1093/bioinformatics/btae542

• PangeBlocks - snakemake

  • Avila Cartes J, Bonizzoni P, Ciccolella S, et al. PangeBlocks: customized construction of pangenome graphs via maximal blocks. BMC Bioinformatics 25, 344 (2024). https://doi.org/10.1186/s12859-024-05958-5

• PGGB - (shell, v0.7.2, 2024.10)

  • Garrison E, Guarracino A, Heumos S, et al. Building pangenome graphs. Nat Methods 21, 2008–2012 (2024). https://doi.org/10.1038/s41592-024-02430-3

• GrAnnoT - (Python, v1.0.3, 2024.8)

  • Marthe N, Zytnicki M, Sabot F. GrAnnoT, a tool for effecient and reliable annotation transfer through pangenome graph. bioRxiv (2025). https://doi.org/10.1101/2025.02.26.640337

• Pangene - (C/JS, v1.1(r2311), 2024.2)

  • Li H, Marin M, Farhat MR. Exploring gene content with pangene graphs. Bioinformatics 40(7), btae456 (2024). https://doi.org/10.1093/bioinformatics/btae456

• Mumemto - (C++/Python, v1.3.1, 2025.6)

  • Shivakumar VS, Langmead B. Mumemto: efficient maximal matching across pangenomes. Genome Biol 26, 169 (2025). https://doi.org/10.1186/s13059-025-03644-0

Contact

This repository was created and maintained by Jie Li.