Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.03.16.712174

VICAST: An Integrated Toolkit for Viral Genome Annotation Curation and Low-Frequency Variant Analysis in Passage Studies

Abstract

Cultured virus passage studies are fundamental to understanding viral evolution, attenuation, and host adaptation, yet analyzing genomic changes across passages requires both accurate functional annotation of viral genomes and sensitive detection of low-frequency variants. Existing tools address these needs separately: automated annotation pipelines such as VADR and VIGOR4 perform well for well-characterized virus families but struggle with poorly-annotated or novel genomes, while variant calling pipelines designed for clinical diagnostics focus on consensus sequences rather than the low-frequency variants (3-50% frequency) that are biologically meaningful in passage studies. Here we present VICAST (Viral Cultured-virus Annotation and SnpEff Toolkit), an integrated software suite that combines semi-automated genome annotation with manual curation checkpoints and low-frequency variant calling optimized for viral populations. VICAST provides four annotation pathways to accommodate diverse genome annotation quality, including polyproteins, unannotated and multi-segmented genomes. It integrates with SnpEff for functional variant annotation and includes a BAM-level read co-occurrence module for haplotype validation. We validated VICAST using publicly available datasets from three virus families representing distinct analytical challenges: SARS-CoV-2 for polyprotein cleavage-aware annotation, Dengue virus 2 for standard flavivirus annotation and low-frequency variant detection, and Influenza A H1N1 for multi-segmented genome handling. Additionally, VICASTs annotation curation workflow has produced validated annotations not available from NCBI, including protein-level annotations for Chikungunya virus (NC_004162.2). These curated annotations have been incorporated into a custom SnpEff database distributed with VICAST, enabling immediate functional variant annotation for Chikungunya without requiring users to build the database from scratch. Benchmark comparisons with VADR demonstrate VICASTs advantages for passage study workflows, including 5.6-8.1 times faster processing and integrated contamination screening. VICAST is freely available at https://github.com/mihinduk/VICAST and distributed as both Docker containers and conda-based installations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Handley, S. A., Chica Cardenas, L. A., Mihindukulasuriya, K. A.. 2026-03-18. VICAST: An Integrated Toolkit for Viral Genome Annotation Curation and Low-Frequency Variant Analysis in Passage Studies. https://doi.org/10.64898/2026.03.16.712174

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Multi-model biological and sequence information fusion for gene regulatory network inference from single-cell transcriptomics

Identification of transcription factor target gene interactions and construction of the gene regulatory networks (GRNs) are essential for understanding the molecular mechanisms underlying transcriptional gene regulation. Large scale single cell transcriptomics across different tissues offers unprecedented resolution of cellular diversity and regulatory dynamics by capturing gene expression heterogeneity. However, existing methods often lack effective multimodal integration and fail to fully exploit the hierarchical structure in Gene Ontology (GO) and gene sequence level representations, which limits their ability for predictive performance and biological interpretability. We present scMGFGRN, a multi-model deep learning framework that integrates single-cell transcriptomic profiles with GO hierarchical relationships, gene sequences by leveraging denoising auto encoders, graph attention feature extraction and pertained DNA language model to capture multi-source dependencies within multi-model biological knowledge, while its gated multi head attention module effectively identifies informative regulatory signatures and integrate complementary features from different sources to predict accurate gene regulatory networks. Benchmarking on the seven datasets of human and mouse demonstrates that scMGFGRN outperforms state of the art methods in identifying GRNs. Further analyses reveal that scMGFGRN effectively identifies novel TF gene interactions (TGIs) and reconstructs cell type specific GRNs. Interpretability analysis reveals the contribution patterns of heterogeneous biological sources, demonstrating the ability of scMGFGRN to integrate transcriptomic profiles with multi model structure information.

bioinformatics↗

Scaling Functional Annotation Across Proteomes, Pangenomes and Metagenomes with Sma3s v3

High-throughput sequencing has generated protein datasets whose scale increasingly exceeds the practical limits of conventional functional annotation workflows. We present Sma3s v3, a scalable reimplementation of the Sma3s three-step annotation strategy, which combines transfer from highly similar homologs, orthology-based inference, and functional enrichment among homologous proteins. Sma3s v3 replaces BLAST-based searches with MMseqs2 and introduces parallel processing, reusable SQLite caches, taxonomic filtering, and traceable outputs that retain the evidence underlying each assignment. We evaluated the method on a Vibrio cholerae pangenome comprising 50,415 gene clusters from 11,295 quality-filtered genomes and on a metagenomic catalogue containing 843,935 proteins. After excluding non-informative assignments, Sma3s v3 annotated 30,662 pangenome clusters (60.8%), comparable to InterProScan (60.2%) and exceeding eggNOG-mapper (41.4%), while providing 5,747 annotations not recovered by either comparator. Gene Ontology comparisons showed broad semantic agreement between methods, with Sma3s v3 frequently contributing more non-redundant information in Molecular Function and Biological Process. Within the pangenome, annotation coverage reached 97.1% for core clusters and approximately 59% for accessory and unique clusters. Exact protein matches to non-Vibrio genera identified 1,838 candidate horizontally transferred clusters enriched in genetic mobility, antimicrobial resistance, and metal tolerance functions. In the metagenomic catalogue, Sma3s v3 annotated 728,014 proteins (86.3%), compared with 616,895 (73.1%) using InterProScan 2026, and recovered approximately 20,000 unique functional terms. These results establish Sma3s v3 as a scalable and interpretable tool for functional annotation and re-annotation of proteomes, pangenomes, and metagenomic protein catalogues.

bioinformatics↗

Somatic haplotype reconstruction and variant recalibration from tumor-only long-read sequencing

Separating somatic from germline variants and reconstructing somatic haplotypes are the two central problems of tumor-only cancer genome analysis. Long reads carry the linkage needed to solve both, but chromosome-scale loss of heterozygosity (LOH) and an unknown degree of normal-cell admixture blur the distinction between somatic and germline haplotypes. Here we present LongPhase-TO, the first method to reconstruct somatic haplotypes from a tumor sample alone. Rather than mapping somatic variants onto germline haplotypes, LongPhase-TO co-phases germline and somatic alleles in a unified graph, in which LOH and tumor DNA fraction are resolved internally from heterozygosity depletion and haplotype imbalance rather than a copy-number and ploidy model. Across eight datasets from six cancer cell lines, LongPhase-TO increased haplotype block N50 by a median of 2.9-fold relative to germline phasers. It also consistently improved somatic single-nucleotide variant (SNV) and indel calls from ClairS-TO and DeepSomatic-TO, raising mean F1 from 0.55 to 0.62 and 0.65 for SNVs and from 0.19 to 0.23 for indels, with the largest gains at low tumor DNA fraction. Across breast, melanoma and lung cancer cell lines, LongPhase-TO improves the accuracy of existing somatic callers and reconstructs megabase-scale somatic haplotypes.

bioinformatics↗