ETH Zurich Unveils MetaGraph DNA Search Engine Compressing Global Genomics Data 300-Fold
ETH Zurich researchers introduced ‘MetaGraph’, a graph-based search engine for genomic data that compresses vast datasets into searchable structures, enabling queries across trillions of DNA and RNA sequences in seconds. The system indexes human and microbial genomes using de Bruijn graphs, where k-mers—short nucleotide subsequences—serve as nodes connected by overlaps, achieving 300 times smaller file sizes than raw FASTQ formats. This allows biomedical scientists to perform exact pattern matching, variant detection, and phylogenetic analyses without decompressing entire archives, addressing storage bottlenecks in precision medicine. Developed over three years, MetaGraph integrates with tools like BLAST for hybrid workflows, supporting terabyte-scale operations on standard servers.
The core innovation lies in its lossless compression algorithm, which preserves sequence integrity while embedding metadata for rapid traversal. Traditional genomic databases, such as NCBI’s GenBank holding over 300 petabytes, demand high-bandwidth access that strains cloud resources; MetaGraph reduces this to gigabytes, with query times under 100 milliseconds for 100-base patterns. Built on open-source C++ libraries, it handles multi-omics integration, linking DNA graphs to protein structures via embeddings from AlphaFold models. Early adopters in cancer genomics report 50-fold speedups in identifying somatic mutations across 1,000 patient cohorts.
Performance benchmarks on the 1000 Genomes Project dataset—comprising 2,500 individuals’ exomes—demonstrate 99.9 percent recall for single-nucleotide variants, outperforming Burrows-Wheeler transforms by 20 percent in space efficiency. The tool’s graph representation facilitates subgraph isomorphism for motif discovery, crucial in non-coding RNA studies where regulatory elements span distant loci. ETH’s team, led by Prof. Boas Pucker, validated it on microbial resistomes, querying antibiotic resistance genes across 10,000 bacterial genomes in under 10 minutes. Availability on GitHub under MIT license includes Docker containers for reproducibility.
This release coincides with surging genomic data volumes, projected to reach zettabytes by 2030 per the Global Alliance for Genomics and Health. MetaGraph’s scalability supports federated learning in international consortia, where privacy-preserving queries avoid data transfers under GDPR constraints. Integration with cloud platforms like AWS S3 enables on-demand indexing, with costs dropping to $0.01 per gigabase searched. Bioinformatics pipelines can chain MetaGraph outputs to downstream tools like GATK for imputation, enhancing accuracy in polygenic risk scoring.
The engine’s extensibility to metagenomics positions it for environmental sequencing, such as biodiversity monitoring via eDNA samples. Developers note its compatibility with emerging long-read technologies from PacBio, accommodating error-corrected haplotypes up to 100 kilobases. As AI-driven sequence prediction advances, MetaGraph provides a robust backend for validating models like those from DeepMind’s Enformer. Initial deployments in European biobanks process 500 terabases weekly, accelerating drug target identification in rare diseases.
Broader adoption could democratize access for under-resourced labs, where hardware limits previously confined analyses to supercomputing centers. Future updates plan GPU acceleration for real-time querying during clinical sequencing, targeting turnaround times under one hour for whole-genome diagnostics. ETH emphasizes ethical indexing, excluding identifiable variants without consent. This tool reinforces Europe’s leadership in computational biology, fostering innovations in synthetic biology and personalized therapeutics.
