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Updated: Oct 6, 2025
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
LYRUS: a machine learning model for predicting the pathogenicity of missense variants
Jiaying Lai1,2, Jordan Yang3, Ece D Gamsiz Uzun2,4,5
1Center for Biomedical Informatics, Brown University, Providence, RI 02903, USA.
Summary:
Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53.
Availability And Implementation:
LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.
Supplementary Information:
Supplementary data are available at Bioinformatics Advances online.
