Andika Pratama, Dewi Nur Lestari, Bambang Hartono, Sri Wahyuni · 5 authors
Modern bioinformatics has entered a multi-omics era in which genomic, transcriptomic, proteomic, and metabolomic datasets accumulate at unprecedented velocity, volume, and variety. Conventional centralized governance — institutional databases protected by role-based access control — struggles with single points of failure, opaque consent enforcement, weak provenance, and brittle interoperability across jurisdictions. Blockchain technology has been proposed as an alternative substrate for trustworthy multi-omics data sharing, but the literature remains fragmented across isolated mechanisms (immutability, smart contracts, on-chain storage) without a coherent system view. This article systematically reviews 82 peer-reviewed studies published between 2017 and 2025, indexed in Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library, using a five-stage screening protocol and a five-question quality assessment rubric. Building on the synthesis, we propose a six-layer architectural framework that combines a permissioned blockchain ledger, smart-contract-based consent and access control, privacy-preserving cryptography (zero-knowledge proofs, homomorphic encryption, differential privacy), decentralized identity, off-chain storage on the InterPlanetary File System, and native interoperability with HL7 FHIR-compliant electronic health records. A multi-criterion comparison shows that Practical Byzantine Fault Tolerance is best suited to the latency, throughput, and energy constraints of multi-omics workflows, outperforming Proof-of-Work and Proof-of-Stake on five of six evaluation dimensions. Compared with traditional security baselines, blockchain delivers measurable advantages in tamper-resistance, provenance, and patient-centric consent, but does not universally dominate on confidentiality and scalability. The framework offers a practical roadmap for big-data governance in life-science research while highlighting open problems in standardization, regulatory alignment, and energy efficiency.
We introduce a reversible 2×2 matrix encoding for DNA sequences, the M¨obius-Compatible Transform (MCT), which maps each base to a pair of signed linear update operators whose product yields a final matrix from which the original sequence can be exactly recovered. To capture global structural properties, we further define an 8-dimensional structural signature vector. Combining matrix-level and signature-level deviations, we construct a hybrid distance metric that is biologically meaningful, mutation-stable, and fully linearizable. All matrix and signature components are projected into a finite field and committed using Pedersen commitments. We design an R1CS formulation that expresses the hybrid distance D using absolute-value decomposition and non-negativity constraints, enabling a zero-knowledge proof that D ≤ T without revealing any DNA information. We implement a complete end-to-end Python prototype integrating reversible encoding, commitment generation, R1CS verification, and large-scale distance evaluation. In a blind analysis of 10,000 randomly generated sequences, the system classifies individuals into high-, medium-, and low-similarity groups relative to a reference sequence without accessing any raw genomic data. Our results demonstrate a lightweight, algebraically structured alternative to MPC- and HE-based approaches for privacy-preserving genomics, providing a practical foundation for zero- knowledge genomic similarity proofs.
William Sánchez, Larue Linder, Robert C. Miller, Amelia Hood · 5 authors
Introduction: Scientists use donated biospecimens to create organoids, which are miniature copies of patient tumors that are revolutionizing precision medicine and drug discovery. However, biobanking platforms remove donor identifiers to protect privacy, precluding patients from benefiting from their contributions or sharing information that may be relevant to research outcomes. Decentralized biobanking (de-bi) leverages blockchain technology to empower patient engagement in biospecimen research. We describe the creation of the first de-bi prototype for an organoid biobanking use case. Methods: We designed and developed a proof-of-concept non-fungible tokens (NFTs) framework for an organoid research network of patients, physicians, and scientists within a synthetic dataset modeled on a real-world breast cancer organoid ecosystem. Our implementation deployed multiple smart contracts on Ethereum test networks, minting NFTs representing each stakeholder, biospecimen, and organoid. The system architecture was designed to be composable with established biobanking programs. Results: Our de-bi prototype demonstrated how NFTs representing patients, physicians, scientists, and organoids may be united in a privacy-preserving platform that builds upon relationships and transactions of existing biobank research networks. The mobile application simulated key features, enabling patients to track their biospecimens, view organoid images and research updates from scientists, and allow physicians to participate in peer-to-peer communications with basic scientists and patients alike, all while ensuring compliance with de-identification requirements. Discussion: We demonstrate proof-of-concept for a web3 platform engaging patients, physicians, and scientists in a dynamic research community, unlocking value for a model organoid ecosystem. This initial prototype is a critical first step for advancing paradigm-shifting de-bi technology that provides unprecedented transparency and suggests new standards for equity and inclusion in biobanking. Further research must address feasibility and acceptability considering the ethical, legal, economic, and technical complexities of organoid research and clinical translation.
Patrick Silva, Deborah Vollmer Dahlke, Matthew Lee Smith, Wendy Charles · 7 authors
Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.
Eric Venner, Mullai Murugan, Walker Hale, Jordan M Jones · 7 authors
MOTIVATION: Clinical genome sequencing laboratories return reports containing clinical testing results, signed by a board-certified clinical geneticist, to the ordering physician. This report is often a PDF, but can also be a paper copy or a structured data file. The reports are frequently modified and reissued due to changes in variant interpretation or clinical attributes. MATERIALS AND METHODS: To precisely track report authenticity, we developed ARBoR (Authenticated Resources in a Hashed Block Registry), an application for tracking the authenticity and lineage of versioned clinical reports even when they are distributed as PDF or paper copies. ARBoR tracks clinical reports as cryptographically signed hash blocks in an electronic ledger file, which is then exactly replicated to many clients. RESULTS: ARBoR was implemented for clinical reporting in the Human Genome Sequencing Center Clinical Laboratory, initially as part of the National Institute of Health's Electronic Medical Record and Genomics (eMERGE) project. CONCLUSIONS: To date, we have issued 15 205 versioned clinical reports tracked by ARBoR. This system has provided us with a simple and tamper-proof mechanism for tracking clinical reports with a complicated update history.
Atalay Mert İleri, Halil Ibrahim Ozercan, Alper Gundogdu, Ahmet K. Senol · 6 authors
Rate of growth of the amount of data generated using the high throughput sequencing (HTS) platforms now exceeds the growth stipulated by Moore's Law. The HTS data is expected to surpass those of other "big data" domains such as astronomy, before the year 2025. In addition to sequencing genomes for research purposes, genome and exome sequencing in clinical settings will be a routine part of health care. The analysis of such large amounts of data, however, is not without computational challenges. This burden is even more increased due to the periodic updates to reference genomes, which typically require re-analysis of existing data. Here we propose Coin-Application Mediator Interface (Coinami) to distribute the workload for mapping reads to reference genomes using a volunteer grid computer approach similar to Berkeley Open Infrastructure for Network Computing (BOINC). However, since HTS read mapping requires substantial computational resources and fast analysis turnout is desired, Coinami uses the HTS read mapping as proof-of-work to generate valid blocks to main its own cryptocurrency system, which may help motivate volunteers to dedicate more resources. The Coinami protocol includes mechanisms to ensure that jobs performed by volunteers are correct, and provides genomic data privacy. The prototype implementation of Coinami is available at http://coinami.github.io/.