An Integrated Analytical Framework for Human Virome Studies: Validation, Standardization, and Statistical Innovation - Project Summary/Abstract The Human Virome Program (HVP) aims to comprehensively characterize viral communities across human body sites in health and disease. However, virome research lacks benchmarking infrastructure to validate computational tools, evaluate statistical methods, and establish quality control standards. Unlike bacterial microbiome research, which benefits from mock communities and standardized workflows, virome researchers cannot rigorously assess whether tools correctly identify viruses, whether abundance estimates are accurate, or whether statistical methods perform appropriately on extremely sparse data (70-95% zeros). This methodological gap threatens HVP scientific rigor and reproducibility. This R03 will create an integrated benchmarking ecosystem for virome research combining three elements: (1) synthetic ground-truth validation data via ViroForge, (2) virome-specific data infrastructure via ViromeData, and (3) systematic statistical validation for sparse data. Aim 1 will enhance ViroForge, a production-ready synthetic virome platform, to generate HVP-validated datasets covering diverse body sites, sequencing platforms, and study designs including longitudinal sampling and batch effects. We will validate error models against real HVP consortium data ensuring synthetic data accurately reflects actual sequencing characteristics. Aim 2 will develop ViromeData, a virome-specific extension of Bioconductor’s SummarizedExperiment providing standardized infrastructure and tool integration. ViromeData will incorporate Baltimore classification, RdRP classifier integration, and virus-host association tracking, with both R and Python implementations ensuring broad accessibility. Aim 3 will systematically validate statistical methods on sparse virome data, testing whether compositionally-aware methods (ANCOM-BC, ALDEx2, Aitchison distance) outperform borrowed RNA-seq and microbiome approaches (DESeq2, Bray-Curtis). We will evaluate differential abundance methods, diversity metrics, and normalization approaches across varying sparsity levels (50-95% zeros), generating power curves and sample size recommendations. The PI serves as Co-PI on the HVP CODCC (U24HL175772), ensuring consortium integration, dataset access, and dissemination channels. Building on proven technologies (ViroForge v0.10.0, Bioconductor SummarizedExperiment) minimizes risk while maximizing impact. Deliverables include ViroForge v2.0 with HVP-validated parameters, ViromeData packages via Bioconductor and PyPI, statistical validation results with best practices guidelines, and training materials. This establishes the first comprehensive benchmarking platform for virome research, providing validated tools, quality control standards, and standardized infrastructure enabling rigorous, reproducible science.
