The Challenge
The increased power of computing, cloud networks, AI, and other technological advances enable scientists to make discoveries at unprecedented pace—while also producing massive amounts of data from a multitude of sources. Automated DNA sequencing provides just one example of this exponential data growth.Research and development teams handling more than 10 terabytes of scientific data using standard cloud resources often face a range of challenges: data chaos, where disorganized, hard-to-find datasets slow research; siloed workflows that prevent collaboration between wet-lab scientists and data engineers; high storage costs from inefficient use of cloud services; and limited accessibility due to complex tooling that requires coding skills for data retrieval and analysis. These obstacles can significantly impede the very scientific progress that modern technology promises to accelerate.Inari faced many of these challenges. The company harnesses advances in genomics, AI, and multiplex gene editing through its SEEDesign™ platform to develop new seed iterations that drive transformative improvements in yield and resource use efficiency. Key to its growth is continued development of its technology to refine current offerings and design solutions for novel and age-old agricultural challenges. The ability to ingest, share, and gain insights from massive amounts of data created across its operations—from dry lab to wet lab to greenhouses to the field—is mission critical. However, the company struggled to break down data silos, achieve broad visualization (such as interactive genomic views to explore and compare DNA sequencing data), and maintain consistent data governance to keep the organization in sync with data standards across its in-house and cloud infrastructure.
The Solution
Inari Agriculture quickly recognized it needed more than a collection of incremental point solutions. The team required a full-spectrum life sciences data management platform that was cloud-native, scalable, and usable by both technical and non-technical teams.Enter Quilt. Quilt is built natively on Amazon Web Services (AWS) and runs entirely within Inari's own AWS environment, ensuring data remains under Inari's control rather than moving into a third-party platform. Quilt makes Amazon S3 intuitive and accessible through seamless integration, while breaking down data silos by enabling both code-first and no-code users to work with the same datasets. By packaging data with rich metadata and governance controls, Quilt supports FAIR data principles and enables compliant, interoperable workflows across ELN and LIMS systems, allowing teams to collaborate more efficiently and confidently at scale.After a period of due diligence, Inari selected Quilt as the obvious choice and adopted the Quilt life sciences data management solution as a key piece of its research and development platform rather than a single-point solution. As such, Inari quickly adapted Quilt to several workflows while continuing to find and explore new use cases as the company grows.One key use case is adopting Quilt as a self-servicing catalog for data and analysis by its computational scientists. Meanwhile, lab scientists found that they could use the catalog similarly and field analysts were not far behind. In every case, Quilt allows users across the organization to self-assemble data packages with full-versioning and flexible metadata so they can rapidly and iteratively store, enrich, and share their data and analysis.Quilt's built-in and extensible visualization capabilities make it easy to compose and share a wide array of analysis and visualization types. Quilt stores and renders common image and document formats out of the box and also has a built-in Interactive Genomic Viewer that allows Inari to visually explore and compare its DNA sequencing data as soon as it lands on the platform. Extending Quilt's visualization capabilities with embedded HTML lets Inari take the platform even further in terms of interactive visualization tools. In fact, Inari leverages Quilt visualization so extensively that the company has built its own Python library to catalog and manage its visualizations within Quilt packages as code. This greatly eases the burden of managing visualizations on users, especially when packages are large or heavy in visualizations.Inari also leverages Quilt for data governance. Quilt's configurable workflows enable Inari to capture agreed-on requirements for file naming conventions, types of data included, and attach metadata fields depending on the workflow. Inari manages internal workflows in another internal Python package that is deployed as a configuration in its Quilt application. This keeps the entire company in sync on data standards and provides an avenue to capture and enforce requirements for data handoffs between teams. For example, when its FieldOps team provides drone images to the field analysis platform, a Quilt workflow enforces that all the imagery is tagged with a location identifier native to the analysis system. This ensures that images are only shown in the correct spatial context. Inari has similarly developed Quilt workflows for package data to be pipelined into its managed data lake where it can be incorporated in near real-time and analysis at scale.
The Results
Quilt accelerates scientific discovery by streamlining data management for science data management systems and R&D teams. The platform consolidates scattered files from Amazon S3 buckets, spreadsheets, and lab tools into structured, queryable datasets with integrated metadata. This enhances collaboration between wet-lab scientists, computational biologists, and IT teams while improving data-driven decision making.Companies like Inari use Quilt's APIs and integrations to automatically ingest and structure data from lab instruments, replacing fragmented data sharing with unified packages. Key results include:• 50% reduction in data retrieval time for scientists• Up to 30% lower storage costs through intelligent data management• Accelerated research pipelines through eliminated bottlenecks in data access, compliance, and securitySupporting all of this utility is AWS, which offers the scalability, security, and ecosystem compatibility needed for managing petabyte-scale scientific data. Amazon S3 serves as the backbone of the Quilt solution, ensuring reliability and cost efficiency for customers. With AWS, Quilt can offer high-throughput bioinformatics and imaging workflows, secure compliant storage with built-in access controls, and seamless integration with compute and AI/ML services for advanced data processing.This grants considerable competitive advantage as Quilt supports datasets exceeding petabyte scale without performance degradation, leveraging Amazon S3 Intelligent-Tiering to optimize storage costs, and AI/ML integration via Amazon Bedrock and AWS HealthOmics for automated metadata extraction.
