SOLUTIONS for LIFE SCIENCE, GENOMICS, AND HEALTHCARE

BAM Plugin

Grant Plugin

From data silos to discovery-driven insight.

Fragmented research data. Genomic sequences, imaging results, and clinical records often reside across disconnected systems, making it difficult to locate, correlate, and reuse valuable datasets.
Data sprawl and versioning chaos. Repeated experiments and file duplication inflate storage costs and complicate traceability.
Limited visibility and control. Distributed data environments obscure lineage, metadata, and lifecycle status – slowing collaboration and discovery.
Compliance risk. Inconsistent file organization and ad-hoc retention practices hinder reproducibility and audit readiness.
High operational costs. Manual data management, inefficient tiering, and duplicated files inflate costs and hinder agility.
Unified metadata catalog. Diskover indexes genomic, imaging, and clinical datasets, making billions of files searchable and traceable from a single interface.
Rich contextual insight. Metadata enrichment connects samples, models, and experimental results for reproducibility and cross-study analysis.
Built-in data intelligence that analyzes patterns, predicts bottlenecks, and powers smarter storage and workflow decisions.
Automated curation workflows. Tag, tier, and retain research data based on lifecycle policies and project context—no manual intervention required.
Policy-based compliance. Streamline retention, verification, and deletion of sensitive data while maintaining full audit trails.
AI-ready data orchestration. Prepare clean, structured datasets for machine learning, predictive diagnostics, and large-scale biomedical modeling.
Accelerated discovery. Curated, metadata-rich datasets speed research cycles and reduce data-related delays.
Up to 50% lower storage costs. Automated lifecycle management eliminates redundant, obsolete, or temporary data.
Improved reproducibility. Standardized metadata and retention workflows ensure data integrity across experiments.
Simplified compliance. Policy-driven audit trails and automated data governance help maintain HIPAA and FDA-ready standards.
Smarter science. AI-ready datasets enhance model accuracy and insight quality for precision medicine and bioinformatics breakthroughs.

Diskover’s Life Science solutions manage research data through every stage—from acquisition to archive and reuse—so your teams can focus on discovery, not data wrangling.

Sample Collection

Data Sequencing

Analysis & Modeling

Clinical Correlation

AI-Driven Insight

Publication & Sharing

Archive & Retention

No matter the data type, format, or platform — from sequencing and imaging systems to lab management and analytics tools — we connect every stage of your data lifecycle. From capture to archive, Diskover helps research and healthcare organizations locate, enrich, and automate data movement and curation — ensuring data integrity, collaboration, and readiness for AI-driven discovery and clinical insight.

No single owner of pathology data pipelines.
Applications in play create unwieldy structures on storage systems.
Fractured procurement throws more capacity issues at the problem.
Rapid index of 4.7PB, 27 million file dataset.
Identified 1PB of files older than 5 years old for immediate tiering/archiving.
Identified hotspots of unmanaged application temporary and cache data.
Estached policy-driven rules for critical data vs application temporary data.
Tiering policy manages data lifecycle between filesystem and cloud.
Operationalized complex data flows by understanding pathology apps and genomics pipelines.
Reclaimed 10% of data estate—$750K—within days and identification of pipelines that do not clean up data.
Protects sensitive data with role-based access control and comprehensive audit trails.
Enabled strategic data estate planning and reduces complexity of lifecycle management.

BAM plugin | Optimize every data pipeline in your genomics ecosystem.

The BAM Metadata Enrichment Plugin extends Diskover’s indexing capabilities into bioinformatics pipelines by harvesting alignment and command-line metadata directly from BAM and SAM files—without requiring any read/write access, ensuring full data integrity.

Harvests key genomic metadata. Using Python’s pysam, the plugin extracts attributes such as sample ID, sequencing platform, alignment method, and genome build—plus the MD5 checksum of the original command line to verify lineage and detect redundant or derived files.
Enriches Diskover indexes with scientific context. Extracted BAM and SAM metadata are indexed and correlated across datasets, making genomic files searchable, comparable, and reportable without exposing raw data.
Automates lifecycle and analysis workflows. Researchers can leverage agentic workflows to drive policy-based lifecycle management, streamline validation, and surface context-rich data for downstream AI and modeling pipelines.
Transforms static files into actionable insight. Turns unstructured genome sequence data into structured, metadata-rich datasets—ready for analytics, machine learning, and reproducible research.
Accelerates research and reproducibility. Enables teams to quickly trace lineage, validate file integrity, and eliminate redundant data, ensuring confidence in every analysis.
Bridges data management and AI-readiness.
By unifying scientific metadata with operational context, Diskover connects the data that fuels genomic discovery, automation, and AI-driven precision analysis.

Rich BAM attributes.

Screenshot showing Diskover’s BAM/SAM Metadata Enrichment Plugin in action. The interface displays indexed BAM file attributes and harvested metadata fields, including sequencing platform, alignment method, genome build, and MD5 checksum. These enriched fields make genomic data searchable, reportable, and AI-ready for downstream analysis and automation.

BAM plugin overview.

Centralizes grant metadata. Collects and organizes grant IDs, group numbers, and funding references into searchable datasets without requiring access to raw research files.
Associates cost and storage data. Links infrastructure usage and storage costs directly to grants for real-time visibility and fiscal accountability.
Supports NIH-aligned data management. Maps metadata and lifecycle practices to NIH DMS Policy requirements—from proposal through publication.
Enables policy-driven automation. Uses agentic workflows to automate data curation, retention, and reporting tasks based on grant and project policies.
Maintains integrity and traceability. Keeps research data read-only while associating grants, projects, and datasets through secure metadata mapping.
Simplifies compliance. Helps research teams meet and exceed NIH data-management and sharing requirements effortlessly.
Improves visibility. Provides grant-level insight into data usage, cost, and project outputs without exposing sensitive research content.
Reduces operational waste. Minimizes redundant storage and manual reporting, freeing funds for active research instead of infrastructure.
Accelerates collaboration. Links investigators, datasets, and grant information in one unified index for faster discovery and validation.
Drives accountability through automation. Ensures reproducibility, transparency, and efficient use of grant resources with automated lifecycle tracking.

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