GeneralBeyond the Byte: Accelerating Life Sciences Research and Discovery with Metadata

A metadata-driven approach overcomes the limitations of traditional data management and storage to speed up analysis in the age of exponential data growth.

Glen Shok

The growth of unstructured data in life sciences research continues unabated as the pace of scientific discovery accelerates. This poses a significant challenge as the demand for data-driven research proliferates data types from a range of sources spanning bioinformatics, precision medicine, and advanced pharmaceuticals. Complex genomic sequencing data, for example, is generated by platforms like Illumina NovaSeq and PacBio Revio, and high-resolution medical imaging data is produced by advanced microscopy techniques such as confocal and cryo-electron microscopy (cryo-EM). This massive set of data, the fuel for scientific advancement, often remains inaccessible or prohibitively expensive to manage.

Better ways to find, manage, and share data – and thereby improve productivity that brings new therapies and new medical approaches to the market faster – are needed now more than ever. The future demands more than new tech. It requires that scientists and the IT teams that support their work adapt to emerging data-centric methods. This necessitates cultural shifts and effective change management. Leveraging AI, automation, and precision data systems will drive transformative scientific progress.

Data storage itself is a key starting point in this transformation. However, from the perspective of the technologists who support researchers, traditional solutions like network-attached storage (NAS) simply can’t keep pace.

While reliable for structured data from laboratory information management systems (LIMS) and electronic lab notebooks (ELNs), NAS systems are proving inadequate for the scale and complexity of unstructured data created by life sciences applications, leading to escalating costs and hindering research efficiency. According to IDC, the global datasphere is projected to reach 175 zettabytes by the end of this year. A substantial portion originates from unstructured data within the life sciences sector.

The core issue extends beyond mere storage space. It’s about efficient data accessibility. Researchers need rapid access to the insights embedded within vast datasets, not just the raw files themselves. Think of a bioinformatician looking to quickly identify specific gene variants within terabytes of raw sequencing data, or a pathologist looking to compare high-resolution tissue images to detect subtle morphological changes.

It’s like searching for a needle in a haystack – this is the reality for many scientists trying to extract meaningful information. Metadata, the light-weight descriptive tags and contextual ‘data about data’, is crucial for unlocking these insights. It provides information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource.

Think of it like the label on a cabinet. It tells you what’s inside without having to open it. However, traditional systems typically cannot effectively manage and leverage metadata, leading to prolonged search times that impede research outcomes, and increase costs. Metadata extraction and discovery lets teams give structure to unstructured data, allowing researchers to ensure their data is clean, purpose-structured, and ready for collaborative analysis.

The Transformative Power of Metadata

The demand for real-time data analysis in areas like personalized medicine and AI-driven drug discovery further exacerbates the limitations of traditional storage systems. Modern research demands seamless integration of data across various platforms and tools, including cloud-based analysis platforms like DNAnexus and Seven Bridges Genomics, as well as specialized software like CellProfiler and ImageJ for image analysis. This integration is a challenge that legacy systems struggle to meet.

The stringent regulatory landscape, including HIPAA, FDA 21 CFR Part 11, and GDPR, and CCPA, adds another layer of complexity. Data integrity, security, and compliance are crucial, requiring clear strategies that ensure data is not only accessible but also auditable and secure. For example, ensuring that clinical trial data, including patient-derived omics data and electronic health records (EHRs), adheres to strict data governance policies is foundational for regulatory submissions. Failure to comply can result in hefty fines and reputational damage.

A metadata-driven approach can be transformative. By implementing a sophisticated metadata catalog, it is possible to convert unstructured data into a valuable source of actionable insights. Systems like GRAU DATA’s MetadataHub enable the extraction and analysis of rich metadata, helping researchers to rapidly locate and retrieve relevant datasets. This aligns with the growing emphasis on data fabric architectures – while perhaps not familiar to researchers – which aim to unify data across disparate environments and enable seamless data access and governance.

This goes beyond simple keyword searches. It allows for the creation of custom metadata tags, reflecting the specific needs of research projects and offering a conduit for better collaboration. For instance, researchers can tag genomic data with specific gene ontology (GO) terms, disease associations, or experimental conditions. Granular control over metadata enables nuanced searches, ensuring researchers find precisely the data they need.

Furthermore, advancements in AI-driven metadata enrichment enable automated tagging and classification, using machine learning (ML) models trained on large datasets of annotated biomedical literature and databases like UniProt and GeneCards, reducing manual effort and improving accuracy.

Consider the power of categorized data in accelerating drug discovery. Tagging data with parameters like experiment type, specimen source, genomic markers, or drug interactions allows scientists to quickly identify patterns and trends. This not only speeds up the research process but also facilitates the integration of ML and AI models.

According to McKinsey, AI-driven drug discovery can massively reduce the time and cost of bringing new drugs to market. This is often achieved through computational chemistry tools like Schrödinger Maestro and OpenEye Scientific, as well as AI platforms for virtual screening and drug repurposing. It’s more than incidental to the trend of leveraging knowledge graphs and semantic technologies to connect disparate data points and speed up drug development.

A metadata-driven approach also enables tiered storage strategies, optimizing costs without sacrificing accessibility. Technologists can better support researchers by moving infrequently accessed data from high-performance computing (HPC) clusters and data lakes to cost-effective tape-based storage solutions significantly reduces storage expenses. These solutions include GRAU DATA’s XtreemStore, a S3-compatible object store that combines modern cloud interfaces with energy-efficient tape storage, and IBM Storage Deep Archive.

Combined with the automated data mobility capabilities of Panzura Symphony, this ensures data remains accessible within existing file and object stores, minimizing disruption to research workflows. It also supports hybrid cloud architectures, allowing technologists to leverage the scalability and cost-effectiveness of cloud storage while maintaining control over sensitive data.

Embracing the Future of Data-Driven Discovery

FAIRification, the process of making scientific and life sciences research data findable, accessible, interoperable, and reusable, is crucial for maximizing the value of research outputs. This involves enriching datasets with metadata, assigning unique and persistent identifiers, and adhering to standardized formats and vocabularies.

GRAU DATA’s MetadataHub enriches metadata by automatically extracting and consolidating content and embedded metadata from diverse file formats, creating comprehensive ‘data proxies’. This enables advanced search, analysis, and customizable metadata extensions, facilitating more efficient research. The integration of MetadataHub with Panzura Symphony enhances overall data management and analytics.

Symphony provides a platform for data management, analytics and governance and MetadataHub provides deeper insights into the data by enriching its metadata. This combination allows Symphony to leverage the enriched metadata for greater control of data orchestration, improved data visibility, and better data governance.

Crucially, this comports with the significance of FAIR data principles which stem from the need to optimize the value of research data, enabling better collaboration, reproducibility, and innovation. By ensuring that data is easily discovered, accessed, integrated, and repurposed, FAIR principles facilitate data-driven discoveries and scientific progress, ultimately fostering a more transparent and efficient research ecosystem.

Automated policy-driven data mobility is another critical component. Integrated with MetadataHub, Symphony enables automated tiering based on extracted metadata attributes. This allows researchers to move large volumes of data to lower-cost storage locations while maintaining visibility and accessibility.

For example, teams can move raw next-generation sequencing (NGS) data to archival storage while keeping processed and analyzed data readily accessible on high-performance storage. The platform also supports data lifecycle management, ensuring that data is retained or deleted according to regulatory requirements and organizational policies.

AI’s impact extends to clinical trials, where it is an accelerant for identifying patient demographics most receptive to precision medicine therapies, leading to fewer unsuccessful trials. It also enables drug developers to optimize trial frameworks through deep analysis of prior data. This is further enhanced using real-world data (RWD) and real-world evidence (RWE), which supplement traditional clinical trial data, as well as through federated learning, which allows AI models to be trained on distributed datasets without compromising data privacy.

Metadata-driven search capabilities allow researchers to quickly locate and retrieve relevant data, reducing the risk of errors and omissions. The integration of natural language processing (NLP) into search tools is also improving the user experience, allowing researchers to query data in a more ‘natural’ and familiar way. Imagine a researcher asking, “Find all genomic data related to breast cancer and HER2 mutations,” and receiving relevant results from a vast unstructured data repository.

In regulated environments, efficient data categorization and archiving ensure compliance. The increasing use of cloud-based technology, for example, for maintaining data provenance and audit trails further strengthens data integrity and compliance. This is especially important for maintaining the chain of custody for biological samples and ensuring the integrity of clinical trial data.

The future of life sciences research hinges on the ability to effectively manage and leverage unstructured data. Embracing a metadata-driven approach – utilizing cost-effective storage solutions and automating data mobility – is a remarkable step toward unlocking data potential. It also puts researchers and the technologists who support them at the leading edge of datacentric architectures, where data is treated as a strategic asset.

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Glen Shok has decades of experience leading complex technology initiatives. His career spans roles at Cisco Systems, Oracle, and Brocade Communications, where he drove pre-sales engineering, OEM development, and product direction initiatives. Shok’s technical background informs his strategic approach to his role as Vice President of Product Marketing at Panzura.