ViralEntropR

A Computational Pipeline for Entropy-Informed Detection of Emerging Viral Variants

Implements an entropy-informed pipeline for detecting emerging variants in viral amino acid sequence data, extending prior clustering-based approaches including hemagglutinin clustering methods (Li et al., 2015) <doi:10.1142/9789814667944_0018>. Provides a fully vectorized FASTA preprocessing toolkit covering header parsing, two-pass date and country extraction, ambiguous-residue filtering, and integer encoding under a 25-symbol amino acid alphabet. Computes per-site Shannon entropy across user-defined cumulative, sliding, or disjoint temporal partitions and clusters per-site entropy values using Gaussian mixture models via 'mclust' (Scrucca et al., 2016) <doi:10.32614/RJ-2016-021>. Quantifies temporal distributional shifts between partitions using the Hellinger distance (van der Vaart, 1998) <doi:10.1017/CBO9780511802256>, and detects temporal change points non-parametrically using energy statistics (Matteson and James, 2014) <doi:10.1080/01621459.2013.849605> via 'ecp' or wild binary segmentation (Fryzlewicz, 2014) <doi:10.1214/14-AOS1245> via 'HDcpDetect'. Per-site amino-acid frequency tables and entropy trajectory plots characterize sequence composition and evolutionary dynamics across time. A configurable multi-variant simulation engine generates synthetic sequence time series with known ground truth for benchmarking detection pipelines. A curated dataset of SARS-CoV-2 Variants of Concern and Variants of Interest with associated lineage and surveillance metadata is included, along with a bundled National Center for Biotechnology Information (NCBI) Spike protein sample and vignettes demonstrating the full workflow.

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