Add tool page for CHAMOIS

Author: althonos

assets/images/tools/2025-03-15-chamois-flow.png

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+---
+layout: tools
+title: "CHAMOIS"
+contributors: [mlarralde]
+handle: chamois
+status: complete
+type: software
+projects: [secmet]
+
+# Optional
+website: 
+publications: "https://www.biorxiv.org/content/10.1101/2025.03.13.642868v1"
+doi: "10.1101/2025.03.13.642868"
+image: /assets/images/tools/2025-03-15-chamois-icon.png
+tagline: Machine learning inference of natural product chemistry.
+tags: [bioinformatics, secondary_metabolism]
+
+# Data and code
+github: ["https://github.com/zellerlab/CHAMOIS"]
+---
+{% include JB/setup %}
+
+
+## Abstract
+
+Biosynthetic gene clusters (BGCs) are genomic loci encoding the biosynthetic pathway for producing specialised metabolites
+with a broad spectrum of bioactivities. Many methods have been developed for genome mining of BGCs, such as [GECCO](/tools/gecco).
+However, in many cases the cognate metabolite remains unknown, and experimental characterisation remains difficult.
+
+CHAMOIS is a machine learning-based tool for predicting chemical properties of secondary metabolites from protein domains annotated in the input BGCs. 
+CHAMOIS infers 539 chemical properties from the ChemOnt ontology using logistic regression. It accurately predicts 111 such properties (AUPRC > 0.5) 
+in cross-validation against known instances. Although CHAMOIS is not explicitly trained on biosynthetic knowledge, many of the inferred 
+links between protein domains and metabolite properties are consistent with scientific literature, others suggest new biochemical functions of 
+uncharacterized biosynthetic domains. Finally, CHAMOIS can pinpoint which BGC within a given genome produces a pre-specified metabolite 
+(correct BGC in 72% of cases ranked among the top 5), which holds great potential for prioritising experimental BGC characterisation and discovery 
+of novel biosynthetic enzymes.
+
+The CHAMOIS software is implemented in [Python](https://www.python.org/), 
+supports all versions from [Python 3.7](https://endoflife.date/python) and is provided under 
+the [GNU General Public License v3.0 or later](https://choosealicense.com/licenses/gpl-3.0/).  
+
+![CHAMOIS flowchart](/assets/images/tools/2025-03-15-chamois-flow.png)
+
+Graphical depiction of the chemical hierarchy inference approach implemented in CHAMOIS. Briefly, CHAMOIS
+identifies open reading frames (ORFs) in a given BGC sequence (Step 1). Then, protein domains 
+are annotated in the resulting ORFs using profile hidden Markov models (pHMMs; Step 2). The resulting domain 
+vector for the whole BGC serves as a feature for a logistic regression classifier for each class of the 
+ChemOnt ontology (Step 3). Predicted classes allow filtering for BGCs with particularly relevant chemical classes (Step 4).
+Finally, the fingerprint of class predictions can be used to find BGCs most similar to a particular compound (Step 5).
+