Scalable Bioinformatics Pipelines for Regenerative Biotechnology Research (Biotechnology and Regenerative Sciences E: 3117-6445 P: 3117-6453, 2025)
Regenerative biotechnology research is characterised by the production of large-scale, multi-modal biological datasets across genomics, transcriptomics, proteomics, epigenomics, and imaging modalities that require sophisticated computational processing pipelines for quality control, alignment, quantification, normalisation, and downstream analysis. The scalability challenge is acute: a single scRNA-seq experiment may produce 100,000+ cells with 20,000+ gene measurements; a WGS study of 5,000 patients generates 50+ terabytes of raw sequencing data; a multi-omics regeneration cohort may integrate 8+ data types across 1,000+ patients, creating petabyte-scale analysis requirements. Existing bioinformatics pipeline frameworks (Snakemake, Nextflow, Galaxy) provide workflow management infrastructure but lack regenerative biology-specific quality control modules, validated configuration presets for common regenerative biotechnology assays, and the cross-pipeline data provenance tracking required for GMP-compatible research workflows. This paper proposes the Regenerative Biotechnology Bioinformatics Pipeline (RBBP) framework, a scalable, cloud-native bioinformatics pipeline system specifically designed for regenerative biotechnology research, comprising five pipeline components: a universal data ingestion and QC module (UDIQ) that standardises raw data intake from diverse sequencing platforms; a scalable single-cell analysis pipeline (SCAP) optimised for large-scale scRNA-seq and single-cell multi-omics; a bulk omics processing pipeline (BOPP) for high-throughput bulk sequencing analysis; a cross-pipeline data integration hub (CPDIH) that manages data flow between pipeline components and downstream analysis frameworks; and a GMP-compatible data provenance tracker (GDPT) that maintains audit trails for regulatory submissions. RBBP is evaluated on four large-scale regenerative biotechnology datasets: a 284-patient multi-omics muscle regeneration cohort, a 420,000-cell scRNA-seq stem cell atlas, a 1,200-patient WGS-based disease genomics study, and a 48-sample ChIP-seq epigenomic profiling study. RBBP achieves 4.8x analysis throughput improvement over sequential baseline pipelines, 98.6% pipeline task completion rate (vs. 84.2% for comparable unmanaged workflows), and full GMP-compatible audit trail generation meeting 21 CFR Part 11 requirements. The study contributes the RBBP specification, the RegenPipeline configuration library, and empirical benchmarks for scalable bioinformatics in regenerative biotechnology.