Quickly search, compare, and analyze genomic and metagenomic data sets.

Fast taxonomic classification of metagenomic sequencing reads using a protein reference database

ultrafast taxonomic profiling and genome querying for metagenomic samples by abundance-corrected minhash.

Amplicon sequencing analysis workflow using DADA2 and QIIME2

Accurate metagenomic profiling && Fast large-scale sequence/genome searching

Highly parallelised multi-taxonomic profiling of shotgun short- and long-read metagenomic data

Metabuli: specific and sensitive metagenomic classification via joint analysis of DNA and amino acid.

Massively parallel phylogenetic placement of genetic sequences

FlexTaxD (Flexible Taxonomy Databases) - Create, add, merge different taxonomy sources (QIIME, GTDB, NCBI and more) and create metagenomic databases (kraken2, ganon and more )

Fast and accurate tool for calculating Average Nucleotide Identity (ANI) and clustering virus genomes and metagenomes

Create BIOM-format tables (http://biom-format.org) from Kraken output (http://ccb.jhu.edu/software/kraken/, https://github.com/DerrickWood/kraken).

Fast and space-efficient taxonomic classification of long reads

Tag and recommend Python exercises based on algorithmic features

PyTorch implementation of Metric-Guided Prototype Learning for hierarchical classification.

This repository contains all the source files required to run DeLUCS, a deep learning clustering algorithm for DNA sequences.

Lemur is a tool for rapid and accurate taxonomic profiling on long-read metagenomic datasets

CyanoSeq: A curated cyanobacterial 16S rRNA database for next-generation sequencing

A pipeline to identify (and remove) certain sequences from raw genomic data. Default taxon to identify (and remove) is Homo sapiens. Removal is optional.

Phylogenetic Placement Evaluation Workflows : Benchmark placement software and different reference trees

📓 Companion code for Grenié et al. 2022 MEE "Harmonizing taxon names in biodiversity data: a review of tools, databases, and best practices" preprint: 10.32942/osf.io/e3qnz