Merge tools pages for ANI packages

Author: althonos

assets/images/tools/2025-07-11-ani-icon.png

@@ -12,7 +12,7 @@ is_published: true
 image: /assets/images/papers/nar-gab.png
 projects:
 tags: []
-tools: [pyorthoani,pyfastani,pyskani]
+tools: [pyorthoani-pyfastani-pyskani]
 
 # Text
 fulltext:

papers/_posts/2025-07-11-larralde-pyorthoani-pyfastani-and.md

@@ -23,6 +23,7 @@ github: ["https://github.com/motu-tool/mOTUs"]
 
 ## Abstract
 Taxonomic profiling is a fundamental task in microbiome research that encompasses detection and quantification of microorganisms in biological samples. Many profiling tools for profiling from shotgun metagenomic data rely on reference genomes, introducing estimation biases because many microbial taxa are not represented in reference genome databases – not even for very well-studied environments such as the human gut. The mOTUs (MG-based Operational Taxonomic Units) approach leverages well-established universal, single-copy, protein-coding marker genes to profile microbial abundances in shotgun metagenomic data. This approach allows the mOTUs algorithm to address both cultivation biases (since universal marker genes can be directly extracted from metagenomes even for uncultivated organisms) as well as genome size biases, which together results in very high precision and recall as evident from the latest Critical Assessment of Metagenome Interpretation (CAMI 2) challenge.  
+
 For many years, we have been developing mOTUs jointly with [Shinichi Sunagawa’s group](https://micro.biol.ethz.ch/research/sunagawa.html) at ETH Zurich, with [Daniel Mende’s group](https://www.amc.nl/web/specialismen/medische-microbiologie-infectiepreventie-1/medische-microbiologie-infectiepreventie-1/daniel-mende.htm) and UMC Amsterdam and with [Peer Bork’s group](https://www.embl.org/groups/bork/) at EMBL Heidelberg. mOTUs is published under the [GPL-3](https://www.gnu.org/licenses/gpl-3.0.en.html) licence.  
 
 ![mOTUs](/assets/images/tools/2022-12-09-mOTUs_picture_modified.png)

tools/_posts/2013-10-02-motus.md

@@ -22,7 +22,9 @@ github: ["https://github.com/zellerlab/cayman"]
 
 ## Abstract
 Carbohydrate-active enzymes (CAZymes) allow microbes to  digest both host (e.g. mucins) and diet-derived (e.g. fiber) glycans, but bioinformatics tools for CAZyme profiling and interpretation of substrate preferences in microbial community data are lacking. To address this, we developed the CAZyme profiler Cayman. Cayman (Carbohydrate active enzymes profiling of metagenomes) is an easy-to-use command-line profiling tool for profiling CAZyme abundances in metagenomic data. It takes shotgun reads as input and provides a matrix of CAZyme Reads-Per-Kilobase-Million (RPKM) abundances for your sample as output. 
+
 By additionally providing an integrated manually curated hierarchical substrate annotation scheme, we facilitate the interpretation of the resulting CAZyme profiles by grouping CAZyme families into higher-level, biologically meaningful substrate groups, e.g. dietary fibre. Thus CAZyme and substrate annotations are transferred via sequence homology.  
+
 We showcase the tool on large-scale bacterial gut genome collections and human gut metagenomic datasets demonstrating its utility for pinpointing bacterial species with specific substrate utilisation patterns (e.g. mucin-foraging) and how glycan substrate utilisation inferred from faecal metagenomes can differ across host lifestyles and health states. Cayman is broadly applicable to (meta-)genomic data from a variety of microbial communities.  
 
 ![Cayman overview](/assets/images/tools/2024-01-08-cayman-overview.jpg)

tools/_posts/2024-01-08-cayman.md

@@ -0,0 +1,43 @@
+---
+layout: tools
+title: "PyOrthoANI, PyFastANI, PySkANI"
+contributors: [mlarralde,carroll]
+handle: pyorthoani-pyfastani-pyskani
+status: complete
+type: software
+
+# Optional
+website: 
+publications: "https://academic.oup.com/nargab/article/7/3/lqaf095/8196481"
+doi: "10.1093/nargab/lqaf095"
+image: /assets/images/tools/2025-07-11-ani-icon.png
+tagline: Python interface to OrthoANI, FastANI, and skani; methods for computing average nucleotide identity.
+tags: [bioinformatics, ANI]
+
+# Data and code
+github: ["https://github.com/althonos/pyorthoani", "https://github.com/althonos/pyfastani", "https://github.com/althonos/pyskani"]
+---
+{% include JB/setup %}
+
+
+## Abstract
+
+The average nucleotide identity (ANI) metric has become the gold standard for prokaryotic species delineation in the genomics era. The 
+most popular ANI algorithms are available as command-line tools and/or web applications, making it inconvenient or impossible to incorporate them
+into bioinformatic workflows, which utilize the popular Python programming language. Here, we present PyOrthoANI, PyFastANI, and Pyskani, Python
+libraries for three popular ANI computation methods. ANI values produced by PyOrthoANI, PyFastANI, and Pyskani are virtually identical to those
+produced by OrthoANI, FastANI, and skani, respectively. All three libraries integrate seamlessly with BioPython, making it easy and convenient to
+use, compare, and benchmark popular ANI algorithms within Python-based workflows. Availability and Implementation: Source code is open-source and
+available via GitHub (PyOrthoANI, https://github.com/althonos/orthoani; PyFastANI, https://github.com/althonos/pyfastani; Pyskani,
+https://github.com/althonos/pyskani). Supplementary Information: Supplementary data are available on bioRxiv.
+
+PyFastANI, PyOrthoANI and PySkANI were developed in collaboration with [The CompMicroLab at Umeå University](https://www.microbe.dev/).
+
+### PyOrthoANI has been used in the following publications
+
+- [Accurate de novo identification of biosynthetic gene clusters with GECCO](https://doi.org/10.1101/2021.05.03.442509).
+
+### PyFastANI has been used in the following publications:
+
+- [Machine learning inference of natural product chemistry across biosynthetic gene cluster types](https://www.biorxiv.org/content/10.1101/2025.03.13.642868v1).
+- [No Assembly Required: Using BTyper3 to Assess the Congruency of a Proposed Taxonomic Framework for the Bacillus cereus Group With Historical Typing Methods](https://pmc.ncbi.nlm.nih.gov/articles/PMC7536271/).