Abstract
Synonymous single-nucleotide variants (sSNVs) are increasingly recognized as contributors to disease, yet existing variant annotation databases offer limited functional insights for sSNVs. Here, we present SynMall, a comprehensive resource designed to decipher the functional impact of synonymous variation. SynMall catalogs 25 million potential human sSNVs and integrates evolutionary and population information of sSNVs from 45 non-human species. For each human sSNV, SynMall provides multilevel annotations that combine American College of Medical Genetics and Genomics (ACMG)-aligned variant interpretation information, such as allele frequencies and functional effects, with more than 100 descriptors at the DNA, RNA, and protein levels. These include both handcrafted features and embeddings from large language models to support advanced representation learning. To prioritize pathogenic sSNVs, we have developed SynScore, a machine learning framework that integrates ACMG guidelines and diverse biological characteristics. Benchmark comparisons show that SynScore achieves state-of-the-art performance, validating its effectiveness for genome-wide pathogenicity inference. Furthermore, SynMall enables mechanistic exploration by investigating in silico assessments and curated literature evidence to evaluate sSNV effects on miRNA-mRNA interactions, mRNA splicing, mRNA stability, and codon usage. By consolidating these features into a unified platform, we anticipate that SynMall will serve as a valuable resource for elucidating the functional role of synonymous mutations.