MuSA: a Nextflow pipeline for deep, reproducible annotation and clinical ranking of genomic variants

BACKGROUND: Accurate clinical interpretation of genetic variants requires integration of functional predictions, evolutionary constraint, population allele frequencies, and clinical evidence from heterogeneous resources. Conventional workflows based on standalone tools such as ensembl variant effect predictor (VEP) and ANNOVAR require complex manual configuration of plugins and databases, generate verbose transcript-level outputs unsuitable for clinical review, and rely on ad hoc scripts for format conversion and prioritization. These limitations hinder reproducibility and scalability, making data interpretation a major bottleneck in genomic medicine. RESULTS: We present MuSA (Multi-Source variant Annotation), an nf-core-compliant Nextflow pipeline that automates germline variant annotation from resource setup to clinical interpretation. MuSA supports both a streamlined basic mode for diagnostic workflows and an extended deep-annotation mode for comprehensive analyses. The pipeline integrates Ensembl VEP with 22 curated plugins (including AlphaMissense, CADD, SpliceAI, and Enformer), ANNOVAR, a standalone pre-configured dbNSFP distribution, the RENOVO pathogenicity predictor, and automated ACMG/AMP classification via GeneBe and InterVar. MuSA standardizes input VCFs, executes parallel annotation branches in a fully containerized workflow, and consolidates results into richly annotated mutation annotation format (MAF) files (up to 920 columns per variant), alongside interactive HTML reports tailored for clinical review with HPO-matched gene panels. Benchmarked on a WES-like dataset of 22,705 variants derived from the public GIAB NA12878/HG001 GRCh38 benchmark VCF, MuSA completes full extended-mode annotation in approximately 20 min on a 64-core server. Systematic comparison with nf-core/sarek and nf-core/variantprioritization demonstrates that MuSA uniquely combines automated resource management with YAML-based version tracking and SHA-256 integrity verification, native dbNSFP integration, RENOVO-based VUS prioritization, HPO-driven gene panel filtering, and a clinically oriented interactive HTML report; those features are mostly absent in existing nf-core annotation pipelines. Containerization through Docker/Singularity and predefined execution profiles support reproducible deployment across workstations, HPC clusters, and cloud environments. CONCLUSIONS: MuSA provides an end-to-end framework for clinically oriented germline variant annotation and prioritization, addressing key limitations of manual and general-purpose workflows. Its dual-output design bridges research (machine-readable MAF files compatible with downstream tools such as maftools) and clinical diagnostics (interpretation-ready HTML reports), supporting reproducible and standardized variant interpretation across teams. Current limitations include restriction to germline small variants on hg38, a substantial storage footprint (up to 223.5 GB for extended mode), and dependence on external APIs for ACMG/AMP classification and phenotype-driven filtering. The RENOVO-based VUS prioritization module is experimental and requires expert review before clinical interpretation.