Genomic Data Standards
Bridge2AI Recommendations for AI-Ready Genomic Data
Rapid advancements in technology have led to an increased use of artificial intelligence (AI) technologies in medicine and bioinformatics research. In anticipation of this, the National Institutes of Health (NIH) assembled the Bridge to Artificial Intelligence (Bridge2AI) consortium to coordinate development of “AI-ready” datasets that can be leveraged by AI models to address grand challenges in human health and disease. The widespread availability of genome sequencing technologies for biomedical research presents a key data type for informing AI models, necessitating that genomics data sets are “AI-ready”. To this end, the Genomic Information Standards Team (GIST) of the Bridge2AI Standards Working Group has documented a set of recommendations for maintaining AI-ready genomics datasets. In this report, we describe recommendations for the collection, storage, identification, and proper use of genomics datasets to enable them to be considered "AI-ready” and thus drive new insights in medicine through AI and machine learning applications.
Driving Precision in AI-Driven Genomics
Ensuring data is explainable, reusable, and computationally accessible is paramount for accelerating biomedical discovery with AI.
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Deep Analysis & Enterprise Applications
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FAIR Principles
Foundation for AI-Ready Datasets
Genomic Data AI-Readiness Workflow
Sample Origin & Prep Metadata
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Sequencing Prep & Process Metadata
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Full Sequencing Procedure Metadata
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Quality Control Data
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Data Storage Formats
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Variant Call Data
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Autism Genotype-Phenotype Associations: The Power of Metadata
Summary: This case study illustrates the critical role of metadata in refining genotype-phenotype associations within large-scale autism sequencing projects. Initial AI models, trained on whole-genome sequencing (WGS) data from two centers (B1 and B2) with varying sample sources (blood vs. saliva), PCR chemistries, and bioinformatics pipelines, yielded findings that were often confounded by technical artifacts rather than true biological signals.
Challenge: Discrepancies in sample origins (saliva vs. blood ratios), sequencing chemistry (2-channel vs. 4-channel), and varied bioinformatics tools (BWA-MEM/GATK vs. DRAGMAP/DRAGEN) led to preliminary associations being linked to technical variables (e.g., DELIN sequences, homopolymers, center-specific SNVs) rather than genuine genotype-phenotype correlations. The omission of detailed metadata obscured true biological insights.
Solution: A refined AI model was conceptualized with comprehensive metadata integration. This included pre-training with sample-level and variant-level metadata, such as sequencing chemistry and center-specific procedures. Fine-tuning incorporated detailed WGS metadata including sequencing depth, alignment, variant calling nuances, and the 27 neuro-cognitive development measures.
Outcome: The refined model significantly outperformed its predecessor, successfully filtering out artifactual associations. It identified several new promising genotype-phenotype associations with potential impact on patient care, demonstrating that meticulous metadata capture and standardization are essential for enabling precision medicine through AI/ML applications and distinguishing real biological effects from experimental noise.
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Your AI-Ready Data Implementation Roadmap
A strategic approach to transforming your genomic data into a powerful asset for AI and machine learning initiatives.
Phase 1: Data Assessment & Metadata Strategy
Evaluate existing genomic datasets, identify metadata gaps, and define a comprehensive strategy for capturing and standardizing all necessary information, adhering to FAIR principles.
Phase 2: Pipeline Harmonization & Quality Control
Implement standardized data processing and analysis pipelines, ensuring consistent application of bioinformatics tools, reference genomes, and robust quality control metrics for AI-readiness.
Phase 3: Data Curation & Storage Optimization
Curate and annotate datasets with rich, semantically precise metadata. Optimize storage formats (e.g., CRAM, gVCF with VRS) for efficient access, reusability, and integration into AI models.
Phase 4: AI Integration & Model Validation
Integrate AI-ready genomic data into machine learning applications, rigorously validate model performance, and establish continuous feedback loops for data quality improvement and new discovery.
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