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NExtSEEK: Extending SEEK for Active Management of Interoperable Metadata

Keywords: metadata, data collection, data management

Published onFeb 02, 2022
NExtSEEK: Extending SEEK for Active Management of Interoperable Metadata
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ABSTRACT

Data management is a critical challenge required to improve the rigor and reproducibility of large projects. Adhering to Findable, Accessible, Interoperable, and Reusable (FAIR) standards provides a baseline for meeting these requirements. Although many existing repositories handle data in a FAIR-compliant manner, there are limited tools in the public domain to handle the metadata burden required to connect data from multi-omic projects that span multiple institutions and are deposited in diverse repositories. One promising approach is the SEEK platform, which allows for diverse metadata and provides an established repository. SEEK is challenged by the assumption of single deposition events where a sample is immutable once entered in the database. This is structured for published data but presents a limitation for ongoing studies where multiple sequential events may occur in a single sample at different sites. To address this issue, we have created a modified wrapper around the SEEK platform that allows for active data management by establishing more discrete sample types that are mutable to permit the expansion of the types of metadata, allowing researchers to track additional information. The use of discrete nodes also converts assays from nodes to edges, creating a network model of the study and more accurately representing the experimental process. With these changes to SEEK, users are able to collect and organize the information that researchers need to improve reusability and reproducibility as well as make data and metadata available to the scientific community through public repositories.

ADDRESS CORRESPONDENCE TO: Stuart Levine, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Building 68-304D, Cambridge, MA 02139, USA (Phone: 617-452-2949; E-mail: slevine@mit.edu).

Conflict of Interest Disclosures: The authors declare no conflicts of interest.

Keywords: metadata, data collection, data management

INTRODUCTION

Improving reproducibility and reusability of data generated from large collaborations is a critical challenge complicated by the variety of data and sample types that must be tracked and shared. Although Findable, Accessible, Interoperable, and Reusable (FAIR) standards were established to facilitate reuse of scientific data[1] and many data repositories are FAIR compliant, they are typically built to handle specific flavors of data.[2] Large multi-omics studies frequently must either split their data across multiple repositories—making connecting datasets challenging—or create specific repositories that serve only a single study (eg, ENCODE[3]), creating new silos of data. Repositories serve as final endpoints for data and prioritize collecting metadata about deposited data and immediately adjacent procedures. Users must complete all of their experiments and finalize their results prior to deposition, separating data acquisition and deposition. This temporal separation can result in the loss of the rich metadata and detailed provenance, which are necessary in multi-omics studies to assess data quality and integrate data,[2],[4] particularly when associated with significant time or staffing changes. There are few resources available to handle this metadata, but SEEK is a promising platform that focuses specifically on metadata collection.[5] SEEK permits complex queries of metadata, interoperability with external repositories, application programming interface access, and controlled access and permissions, all built around FAIR principles. SEEK also supports exportation of data and metadata and has an existing public installation at fairdomhub.org, allowing for broad, non–project-specific public deposition of metadata.[6]

A key limitation of this approach is the focus on the final deposition of data. Because data collection can occur over years, obtaining accurate data provenance is a key challenge.[7] This is especially true for large multisite collaborations. Samples within SEEK are assumed to be immutable (ie, deposited only once), which does not allow for the ongoing collection and updating of information in a simple, coherent, and organized way. We have developed Network Extended SEEK (NExtSEEK), a modified wrapper around SEEK that allows for active data management. We chose to modify SEEK because of its existing functionality, public installation, open-source and modular code, and existence of a public repository. We modified SEEK’s data model to prioritize connections between samples and define discrete sample types that can be freely connected. Sample types in NExtSEEK are mutable to allow researchers to update sample records and add new metadata as required. NExtSEEK also allows for sparsity in data matrices, allowing users to capture only relevant metadata and adhere to the standards of their repository of choice. Finally, we expanded upon SEEK’s ability to extract sample records from Microsoft Excel sheets to allow for greater flexibility in data collection. These additions are designed to facilitate the collection of information alongside the experimental process, help maintain records through staff turnover, share metadata with collaborators, and allow for the integration of datasets. Data and metadata from NExtSEEK are easily exported to the public metadata repository FAIRDOMHub and can be linked to the disparate repositories that hold the respective data files.

RESULTS AND DISCUSSION

In the process of implementing a local SEEK instance for gathering sample and metadata for several large projects, we identified a key challenge: an individual sample handled by multiple users would result in a large number of duplicated sample records on the database, different only slightly by modest changes. For example, a mouse with a record created at birth would get a new record upon an initial treatment protocol, another when challenged with a drug, and another at necropsy. This disconnect between the data records and the physical samples created a very challenging environment, limiting the ability of users to identify key data and metadata points for searching and discouraging reuse by users. Retaining all the protocols separately and uploading a summary at the end could bypass this issue but would be a significant challenge to implement.

To address these challenges, we developed a wrapper around the SEEK architecture, NExtSEEK, which modifies the tool to accommodate sample mutability and improve data capture while not disrupting the derivative archival nature of the platform. First, the concept of assays was modified, moving them from nodes that are parents of samples to the edges between samples, describing either the update of a sample or the connections between samples (Figure 1A).[8] In this way, it becomes clear how a physical sample was processed to give rise to additional samples or datasets. We define assays as experiment procedures described by protocols. Although SEEK defines assays to be discrete measurement events (equivalent to experiments), we have redefined assays to be broad classes, of which experiments are individual instances, that can connect samples across multiple experiments and be reused throughout the project (Figure 1B).

Figure 1

NExtSEEK modifications to the SEEK architecture. (A) NExtSEEK converts the hierarchical Investigation/Study/Assay (ISA) model to a network model by implementing Assays as edges instead of nodes. The top image represents SEEK architecture and the bottom represents NExtSEEK architecture. (B) In the NExtSEEK network model, experiments are considered specific instances of Assays. The top image represents SEEK architecture and the bottom represents NExtSEEK architecture. Yellow boxes show Assays with example protocols, and green boxes show individual samples. (C) NExtSEEK categorizes sample types as broad groups identifiable by being possible inputs to shared Assays. An example is shown of a GAS sample type (including atmospheric air sample) and a CEX sample type—both lysates and cell culture supernatants. All samples of both sample types could be subject to mass spectrometry, resulting in the creation of D.MSP sample types. (D) The NExtSEEK network is a nonhierarchical directed graph. Examples shown are between MUS and DNA samples with different assays highlighted. CEX, cell extract; D.MSP, mass spectrometry data; ISA, Investigation/Study/Assay infrastructure; MUS, murine; TIS, tissue.

Next, samples were allowed to be mutable to capture updates as alterations to their sample records. To facilitate this mutability, we added the concept of a Sample Type, of which an individual sample record is an instance. A Sample Type is a list of all possible metadata attributes that describe the samples, whether they are populated or not. Different individual samples are expected to have different metadata as a result of having been produced by different assays (Figure 1C). Different sample types are delineated by which assays can generate them and which assays can be performed on them. As researchers do novel experiments and need to capture new metadata, additional attributes to sample types can be added. Users can update sample records with altered or additional metadata as they are affected by experiments, changes in storage condition, location, and other factors.

The addition of “hard sample typing” contrasts with the general SEEK platform, which allows every sample to be a unique type specific to their experiments. SEEK uses the Just Enough Results Model based on Minimal Information Models, which define the minimal amount of metadata that needs to be published with a sample for interoperability and reuse of metadata.[9] By instead defining hard sample types and allowing them to be modified as additional information needs to be collected, NExtSEEK allows for similar samples to be grouped together for easier browsing, standardized attribute names for understanding between collaborators, and space to collect the metadata that repositories or community standards will require up-front.[10],[11],[12] Sample types are defined to be broad and allowed to be sparse so that users have the flexibility to comply with the standards and requirements of the repository of their choosing. However, because the sample types are not universal, different projects or installations can have their own sets of sample types, defined as needed by the project.

To capture detailed provenance, NExtSEEK prioritizes connections between samples equally to the organization of assays. Typically, SEEK requires that the definition for a sample type designate its parent types. This requires anticipating the connections between types and prohibits types from pointing to themselves, which is often not possible in large collaborations. NExtSEEK adds the ability for any sample to point to samples of any other sample type or of the same type as its parent, so long as an assay can connect them, as seen in Figure 1D. Users can capture relationships between samples and data as necessary, with the ability to make novel connections. As researchers do experiments and generate new samples, their records expand the network of samples, forming a directed multigraph (Figure 2A). The NExtSEEK data model assumes inheritance of metadata[13] so users do not have to upload redundant metadata. For example, the genotype of the tissues in Figure 2A is implicitly captured, as the tissues are connected to their parent mice, whose genotypes are explicitly recorded.

Figure 2

Practical utilization of NExtSEEK for data management and deposition. (A) A network model of sample types in two projects, which are highlighted as light red and light blue. Sample Types are highlighted in colors based on inclusion in the projects, and their shape outlines are based on sample type. Representative sample types are shown. Protocols used in Assays are indicated in colors based on the group performing the experiments. (B) A deposition of project data from NExtSEEK. The mass spectrometry dataset deposited in Proteomics IDEntification Database (PRIDE) collects metadata from multiple sample types in NExtSEEK, which is highlighted in green. Full metadata are also deposited in FAIRDOMHub, highlighted in blue, with pointers to the dataset in PRIDE, indicated by dashed blue lines.  

As samples in large collaborations are handled by multiple researchers, the information required by data repositories and journals is often generated by different people at different times. In order to minimize the delay between data generation and deposition, users require flexibility in how and when they can upload metadata. SEEK collected metadata via Sample Sheets, which are templates uniquely defined for each sample type that users could fill in with their metadata for each specific experiment. In contrast, by using the concept of defined and discrete sample types, NExtSEEK instead defines Assay Sheets, which collect information for specific assays rather than individual samples. Whereas a SEEK Sample Sheet contained the full list of metadata attributes for a single experiment, Assay Sheets can span multiple Sample Types while collecting only specific fields for those Sample Types as needed (ie, creating a sparse matrix) and while capturing parent–child relationships. For example, in creating a DNA sample from an ear punch, an Assay Sheet would include both the mouse and DNA sample types (Figure 1D). Within the DNA sample metadata, it would include DNA type, concentration, and volume, whereas metadata fields for Illumina barcode or plasmid properties (such as selectable marker) would not be included on the Assay Sheet, as they are irrelevant for the protocol. Should the resultant DNA be used to create an Illumina library, the library preparation Assay Sheet would include different metadata fields, such as Illumina barcode and library preparation kit. As such, Assay Sheets allow users to upload and update samples in a manner that fits into their workflow, and individual users only need to be responsible for the information that they generate.

NExtSEEK is designed around the expectation that data from large multi-omic projects often will require multiple depositions in several public repositories. To accomplish this, researchers should identify the ideal endpoints for their data and samples before the start of data and metadata collection as well as note the mandatory metadata they need (a checklist for preparation to use NExtSEEK is available at https://github.com/BMCBCC/NExtSEEK). Assay Sheets can be tailored to include fields for all information required at deposition. This allows for a strong maintenance of both data provenance (as information is collected close to the time of generation) as well as maintaining field-specific ontologies, which are both requirements of FAIR principles. Upon completion, a project may span multiple laboratories and sample types (Figure 2A). The NExtSEEK database can be queried, and the metadata required for deposition will be aggregated and made available immediately (Figure 2B). Additionally, records can be converted to an Investigation/Study/Assay (ISA) infrastructure for deposition to FAIRDOMHub, where they can point to the data deposited in public data repositories.[14] As research builds on existing sample networks in NExtSEEK, samples may also belong to multiple projects (Figure 2A). Additional work that builds upon existing samples can be connected to the old records.

By defining hard sample types that are also mutable and sparse, NExtSEEK builds a module for active data management around the SEEK platform. This helps researchers close the gap between data generation and deposition while navigating the variety of data standards set by journals and repositories. Collecting information at the point of generation also helps to preserve rich metadata for better quality control of samples and data. Because records on NExtSEEK are collected prior to deposition, users can also store information that is private and internally useful alongside the key metadata to be published, supporting limited data sharing for collaborations and maintaining records through staff turnover. NExtSEEK also preserves relationships among datasets, key information that is often lost when subtypes of data are put into different public repositories. The network-based data model allows users to easily build on existing research and connect new samples and data to existing records. This maintains provenance of samples and data, helping researchers aggregate metadata across projects and collaborations for depositions to public data repositories. Provenance can be publicly maintained by deposition to FAIRDOMHub, an open installation of SEEK. Taken together, NExtSEEK greatly facilitates rendering data FAIR compliant by making data easier to find and more easily accessible and by providing key information so that data can be used in a wide range of applications. Together with enhanced capacity for preserving detailed provenance and metadata information by enabling collection in real time, NExtSEEK allows researchers to reuse data for different purposes than originally envisioned, including multi-omics studies that give rise to novel insights.

ACKNOWLEDGMENTS

The authors thank the members of the IMPAcTB consortium and MIT Superfund Research Project, particularly Douglas Lauffenburger and Sarah Fortune, for their contributions to the development of NExtSEEK and members of the MIT BioMicro Center for their review of the manuscript. This work was supported by the Koch Institute Support Grant P30-CA14051 from the National Cancer Institute, the MIT Center for Environmental Health Sciences Support Grant P30-ES002109 from the National Institute of Environmental Health Sciences, the National Institute of Environmental Health Sciences Superfund Basic Research Program, National Institute of Health, P42 ES027707, and National Institutes of Health contract 75N93019C00071.

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