Journal:SODAR: Managing multiomics study data and metadata - Revision history

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	*A LIMS focuses on storing information around [[laboratory]] [[workflow]]s. This includes tracking of consumables, samples, instruments, and tests. A LIMS deals with daily tasks of laboratories such as billing and instrument calibration. It is often specific to certain domain areas such as [[sequencing]] facilities.		*A LIMS focuses on storing information around [[laboratory]] [[workflow]]s. This includes tracking of consumables, samples, instruments, and tests. A LIMS deals with daily tasks of laboratories such as billing and instrument calibration. It is often specific to certain domain areas such as [[sequencing]] facilities.
	*The ELN focuses on allowing humans to record their laboratory work. It replaces paper notebooks and captures experiments and their results, mostly in free-form text, pictures, tables, and so on. The ELN plays a key role in fulfilling regulatory requirements.		*The ELN focuses on allowing humans to record their laboratory work. It replaces paper notebooks and captures experiments and their results, mostly in free-form text, pictures, tables, and so on. The ELN plays a key role in fulfilling regulatory requirements.
	*The CDS/IDS provides data capturing, storage, and analysis functionality in instrument-specific domains. Two examples are the CASAVA pipeline and the BaseSpace [[Cloud computing\|cloud-based service]], both from [[Illumina, Inc.\|Illumina]]. The former is provided without extra cost with the instrument along with its source code, while the latter is purchasable and closed source. Such software often ships with the instruments themselves.		*The CDS/IDS provides data capturing, storage, and analysis functionality in instrument-specific domains. Two examples are the CASAVA pipeline and the BaseSpace [[Cloud computing\|cloud-based service]], both from [[Vendor:Illumina, Inc.\|Illumina]]. The former is provided without extra cost with the instrument along with its source code, while the latter is purchasable and closed source. Such software often ships with the instruments themselves.
	*The SDMS provides scientific content management functionality for scientific data and documentation. It allows for the management of metadata and potentially mass data. The SDMS's core functionality generally doesn't include data analysis, user-centric data collection, or laboratory workflow tracking. Such features may be potentially supported by plugins or extensions. Many such systems offer integration with surrounding systems (e.g., via [[application programming interface]]s [APIs]).		*The SDMS provides scientific content management functionality for scientific data and documentation. It allows for the management of metadata and potentially mass data. The SDMS's core functionality generally doesn't include data analysis, user-centric data collection, or laboratory workflow tracking. Such features may be potentially supported by plugins or extensions. Many such systems offer integration with surrounding systems (e.g., via [[application programming interface]]s [APIs]).

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	==Abbreviations, acronyms, and initialisms==		==Abbreviations, acronyms, and initialisms==
	API: application programmable interface		* '''API''': application programmable interface
	BAM: binary alignment map		* '''BAM''': binary alignment map
	BIDS: Brain Imaging Data Structure		* '''BIDS''': Brain Imaging Data Structure
	CDISC: Clinical Data Interchange Standards Consortium		* '''CDISC''': Clinical Data Interchange Standards Consortium
	DMF: data management framework		* '''DMF''': data management framework
	DRS: data repository system		* '''DRS''': data repository system
	EGA: European Genome-phenome Archive		* '''EGA''': European Genome-phenome Archive
	ELN: electronic laboratory notebook		* '''ELN''': electronic laboratory notebook
	GEO: Gene Expression Omnibus		* '''GEO''': Gene Expression Omnibus
	GHGA: German Human Genome-phenome Archive		* '''GHGA''': German Human Genome-phenome Archive
	GUI: graphical user interface		* '''GUI''': graphical user interface
	HDF5: Hierarchical Data Format v5		* '''HDF5''': Hierarchical Data Format v5
	HPO: human phenotype ontology		* '''HPO''': human phenotype ontology
	IDS: instrument-specific data system		* '''IDS''': instrument-specific data system
	iRODS: Integrated Rules-Oriented Data System		* '''iRODS''': Integrated Rules-Oriented Data System
	ISA: Investigation, Study, Assay		* '''ISA''': Investigation, Study, Assay
	LIMS: laboratory information management system		* '''LIMS''': laboratory information management system
	NFDI: German National Research Data Infrastructure		* '''NFDI''': German National Research Data Infrastructure
	NGS: next-generation sequencing		* '''NGS''': next-generation sequencing
	OMIM: Online Mendelian Inheritance in Man		* '''OMIM''': Online Mendelian Inheritance in Man
	PEP: Portable Encapsulated Projects		* '''PEP''': Portable Encapsulated Projects
	RBAC: role-based access control		* '''RBAC''': role-based access control
	RDM: resource data management		* '''RDM''': resource data management
	SDM: scientific data management		* '''SDM''': scientific data management
	SDMS: scientific data management system		* '''SDMS''': scientific data management system
	SODAR: System for Omics Access and Retrieval		* '''SODAR''': System for Omics Access and Retrieval
	TSV: tabular separated values		* '''TSV''': tabular separated values
	VCF: variant call format		* '''VCF''': variant call format

	==Acknowledgements==		==Acknowledgements==

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	===Sample sheet editor, import, export===		===Sample sheet editor, import, export===
			Sample sheets can be included into SODAR projects by either importing existing ISA-Tab files or template-based creation. When importing, the user can upload a Zip archive or a set of individual ISA-Tab files. For creating sample sheets from templates, the user needs to fill in certain details in the SODAR GUI. SODAR contains multiple built-in templates for generic RNA sequencing, germline DNA sequencing, and MS–based metabolomics, for example. After import or creation, the sample sheets are stored in an object-based format in the SODAR database for easy search and modification. In the GUI, they are presented to the user as spreadsheet-style study and assay tables.

			The user can edit sample sheets in the SODAR GUI (see Additional File 2). Cells in the study and assay tables can be edited like in a spreadsheet application. For each column, the project owner or delegate can define the accepted format, value choices, value ranges, regular expressions for accepted values, and other settings depending on the column type. This ensures the validity of data and their compatibility with the study's requirements and conventions.

			SODAR supports ontology term lookup for cell editing. Commonly used ontologies such as Human Phenotype Ontology (HPO) ([https://scicrunch.org/resolver/RRID:SCR_006016 RRID:SCR_006016]) [36], Online Mendelian Inheritance in Man (OMIM) ([https://scicrunch.org/resolver/RRID:SCR_006437 RRID:SCR_006437]) [37], and NCBI Taxonomy Database Ontology (NCBITaxon) ([https://scicrunch.org/resolver/RRID:SCR_000479 RRID:SCR_000479]) [38] can be uploaded into SODAR for local querying as OBO or OWL files, without the need to rely on third-party APIs. Manual entering of ontology terms is also allowed. It is possible to include multiple ontology terms in a single cell, and one or several ontologies can be used in a single column.

			When editing sample sheets, old sheet versions are stored as backup. These versions can be compared and restored in case of mistakes, as well as exported from the system. SODAR allows for sample sheet export in the full ISA-Tab TSV format or simplified Excel tables. Replacing existing sheets with versions modified outside of SODAR is also supported.

			===Integrating SODAR Core–based sites===
			Several subcomponents of the SODAR server such as project and user management have proven to be useful in other contexts. We have extracted them into the SODAR Core software package [32], which forms the foundation of other projects such as VarFish ([https://scicrunch.org/resolver/RRID:SCR_023710 RRID:SCR_023710]) [39] and Kiosc ([https://scicrunch.org/resolver/RRID:SCR_023711 RRID:SCR_023711]). [40] Using a common library for projects and access management has several advantages and enables the integration of VarFish and Kiosc with SODAR.

			SODAR can be configured to work as a “source” site. Applications based on SODAR Core can then be configured as “target” sites of the source site. Projects and access to users will then be synchronized to target sites. This allows us to manage sample and experiment definitions in SODAR and upload corresponding variant data to VarFish. VarFish can then use the REST APIs defined by SODAR for synchronizing sample metadata, such as phenotype terms, directly from SODAR. Similarly, users can upload mass data files into the iRODS data repository and create access tokens to them in SODAR. These tokens can be used to provide data visualization applications in Kiosc with data access via HTTP and iRODS protocols or external applications such as UCSC Genome Browser.

			===SODAR administration===
			We provide a straightforward way to install SODAR and related components (SODAR, iRODS, Davrods, and supporting database servers) and maintain such an installation based on Docker containers and Docker compose. Detailed installation instructions can be found in the “sodar-server” source code repository. [41]

			The entire system can be set up using an external LDAP or ActiveDirectory server for users and credentials or as an alternative in a standalone fashion where SODAR hosts this information. Existing iRODS installations can also be used with SODAR. For administrators, SODAR features dashboards that provide statistics regarding projects and usage of storage resources.

			==Availability of supporting source code and requirements==
			Project name: sodar-server

			Project homepage: https://github.com/bihealth/sodar-server

			Operating system: Linux/Unix

			Programming language: Python

			License: MIT

			RRID: SCR_022175

			Biotools: biotools:sodar

			==Additional Files==
			The following additional files can be found in [https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/gigascience/12/10.1093_gigascience_giad052/1/giad052_supplemental_files.zip?Expires=1707241538&Signature=pK4xi8U5GksNSKAwQvgvM1ndo2IIddWJlI2r9kFDAT8J4EQ0beBQ4Rdnbk4oapgeYIxBnCysJwLBCxBwGET3yOP4fsQyvTG5-zq-A-88H5~dWtB-2HiticvIukDF3Urs~WLS5coa~jh7ZRC0PrR-p5Rr22YkWV6utA4LGHACFyobug8sTpfuLC3YBkMsaoTAjFrv3wKgCpA9JDuJA0TiL~36hwW9r15BNW46e~6~4EyUO~JLwUKMBZrY9b9VNrO7oafY3LEHh5CmWda0Z6C~O2BKG3xMplQv9UjJdIHq9C2REHioCmKmNA2tFNL1QJ0cqnHLw382rRPgEhKfot~OGQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA giad052_supplemental_files.zip]:

			* Additional File 1. Data management software comparison table. Comparison of features between SODAR and related data management software
			* Additional File 2. SODAR sample sheet editor. Figure consisting of screenshots of the SODAR sample sheet editor with its major features annotated

			==Abbreviations, acronyms, and initialisms==
			API: application programmable interface
			BAM: binary alignment map
			BIDS: Brain Imaging Data Structure
			CDISC: Clinical Data Interchange Standards Consortium
			DMF: data management framework
			DRS: data repository system
			EGA: European Genome-phenome Archive
			ELN: electronic laboratory notebook
			GEO: Gene Expression Omnibus
			GHGA: German Human Genome-phenome Archive
			GUI: graphical user interface
			HDF5: Hierarchical Data Format v5
			HPO: human phenotype ontology
			IDS: instrument-specific data system
			iRODS: Integrated Rules-Oriented Data System
			ISA: Investigation, Study, Assay
			LIMS: laboratory information management system
			NFDI: German National Research Data Infrastructure
			NGS: next-generation sequencing
			OMIM: Online Mendelian Inheritance in Man
			PEP: Portable Encapsulated Projects
			RBAC: role-based access control
			RDM: resource data management
			SDM: scientific data management
			SDMS: scientific data management system
			SODAR: System for Omics Access and Retrieval
			TSV: tabular separated values
			VCF: variant call format

			==Acknowledgements==
			The authors thank all internal users, particularly CUBI members, for their feedback. Some icons from OpenMoji.org were used in the figures.

			===Author contributions===
			Conceptualization: M.N., M.H., D.B. Funding acquisition: D.B. Methodology: M.H., M.N. Project administration and supervision: M.H., D.B. Resources: D.B. Software: M.N., M.H., O.S., P.P. Writing and editing: all authors.

			===Funding===
			This work has been supported by the Ministry of Education and Research (BMBF), as part of the National Research Initiative “Mass Spectrometry in Systems Medicine” (MSCoreSys), grant-ID 031L0220A (MSTARS) and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—project-ID 427826188–SFB 1444.

			===Data availability===
			All supporting data and materials are available in the GigaScience GigaDB database. [41]

			===Competing interests===
			The authors declare that they have no competing interests.

	==References==		==References==

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	===Our work===		===Our work===
	In our work, we focus on managing many omics projects of varying data size and various use cases, including cancer and functional genomics studies. We also need to support multiple technologies such as whole-genome sequencing, single-cell sequencing, proteomics, and ~~mass spectrometry~~. Deposition to public repositories was not a necessity in our context. However, SODAR is an ISA-compliant system. Should the data owner require it, it is easily feasible to create appropriate exports to public data repositories using the APIs provided by SODAR. Open-source software is a requirement to avoid vendor lock-in and allow for flexibility in different use cases. A suitable end-to-end solution was not available when we started our work in 2016. Therefore, we set out to implement an integrated system for managing omics-specific data and metadata.		In our work, we focus on managing many omics projects of varying data size and various use cases, including [[cancer]] and functional genomics studies. We also need to support multiple technologies such as [[whole genome sequencing]], single-cell sequencing, proteomics, and MS. Deposition to public repositories was not a necessity in our context. However, SODAR is an ISA-compliant system. Should the data owner require it, it is easily feasible to create appropriate exports to public data repositories using the APIs provided by SODAR. [[Open-source software]] is a requirement to avoid vendor lock-in and allow for flexibility in different use cases. A suitable end-to-end solution was not available when we started our work in 2016. Therefore, we set out to implement an integrated system for managing omics-specific data and metadata.

	In this article, we introduce SODAR (System for Omics Data Access and Retrieval). SODAR combines the modeling of studies and assays using the ISA-Tab format with handling of mass data storage using iRODS. More example projects are available in the SODAR online demo server [41].		In this article, we introduce SODAR (System for Omics Data Access and Retrieval). SODAR combines the modeling of studies and assays using the ISA-Tab format with handling of mass data storage using iRODS. More example projects are available in the SODAR online demo server. [41]

			==Results==
			We present the results by first giving an overview of the developed SODAR system. Next, we compare it to a selection of existing tools and their relevant features. We then describe processes we have established around SODAR. Finally, internal usage statistics are detailed along with discussion on the limitations of SODAR.

			===Resulting system overview===
			Figure 1 presents the components of the SODAR system. The SODAR server is built on the Django web framework. It contains the main system logic and provides both a graphical user interface (GUI) and APIs for managing projects, studies, and data.


			[[File:Fig1 Nieminen GigaScience2023 12.jpeg\|800px]]
			{{clear}}
			{\|
			\| style="vertical-align:top;" \|
			{\| border="0" cellpadding="5" cellspacing="0" width="800px"
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;" \|<blockquote>'''Figure 1.''' SODAR system with its components and actors. The figure illustrates how actors interact with SODAR and iRODS through different APIs.</blockquote>
			\|-
			\|}
			\|}

	==References==		==References==

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{{Infobox journal article
|name =
|image =
|alt = 
|caption =
|title_full = SODAR: Managing multiomics study data and metadata
|journal = ''GigaScience''
|authors = Nieminen, Mikko; Stolpe, Oliver; Kuhring, Mathias; Weiner III, January; Pett, Patrick; Beule, Dieter; Holtgrewe, Manual
|affiliations = Berlin Institute of Health at Charité–Universitätsmedizin Berlin
|contact = Email: mikko dot nieminen at bih dash charite dot de
|editors =
|pub_year = 2023
|vol_iss = '''12'''
|at = giad052
|doi = [https://doi.org/10.1093/gigascience/giad052 10.1093/gigascience/giad052]
|issn = 2047-217X
|license = [http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International]
|website = [https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giad052/7232111 https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giad052/7232111]
|download = [https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giad052/50974561/giad052.pdf https://academic.oup.com/gigascience/article-pdf/doi/10.1093/gigascience/giad052/50974561/giad052.pdf] (PDF)
}}
{{ombox
| type = notice
| image = [[Image:Emblem-important-yellow.svg|40px]]
| style = width: 500px;
| text = This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.
}}
==Abstract==
Scientists employing omics in [[Life sciences industry|life science]] studies face challenges such as the modeling of multiassay studies, recording of all relevant parameters, and managing many [[Sample (material)|samples]] with their [[metadata]]. They must manage many large files that are the results of the assays or subsequent computation. Users with diverse backgrounds, ranging from computational scientists to wet-lab scientists, have dissimilar needs when it comes to data access, with programmatic interfaces being favored by the former and graphical ones by the latter.

We introduce SODAR, the system for [[omics]] data access and retrieval. SODAR is a software package that addresses these challenges by providing a web-based graphical user interface (GUI) for managing multiassay studies and describing them using the ISA (Investigation, Study, Assay) data model and the ISA-Tab file format. Data storage is handled using the iRODS [[Information management|data management]] system, which handles large quantities of files and substantial amounts of data. SODAR also offers programmable [[application programming interface]]s (APIs) and command-line access for metadata and file storage.

SODAR supports complex omics integration studies and can be easily installed. The software is written in [[Python (programming language)|Python 3]] and freely available at https://github.com/bihealth/sodar-server under the MIT license.

'''Keywords''': omics, research, research data, data management

==Introduction==
Modern studies in [[Life sciences industry|life sciences]] rely on “[[omics]]” assays, which encompass branches of science such as [[genomics]], [[proteomics]], and [[metabolomics]]. One or multiple assays can be run within a single study, potentially including assays for multiple omics studies of several types.

The following key steps are required for executing these complex omics studies: (i) planning, which results in study [[metadata]]; (ii) collection of mass data; and (iii) [[data analysis]], including the integration of multiple assays. The aim of SODAR is to ensure support for scientists within all the steps.

===Challenges===
Each step presents its own set of challenges. During planning, it is important to enable recording crucial factors and covariates. The flow of materials and [[Sample (material)|samples]] through processes must also be specified in sufficient detail. Further challenges arise from, for example, assays using complex multiplexing, such as the need for reference samples; requirements for using controlled vocabularies or [[Ontology (information science)|ontologies]]; and possible change of assays over time.

In the data collection step, scientists must record the used machines, kits, and versions of both hardware and software used. Omics studies also create large volumes of data, ranging from a few gigabytes for [[mass spectrometry]] (MS) to terabytes for [[imaging]] such as [[microscopy]]. These data may be spread among many files, further complicating the needs for managing mass data storage. Instead of a rigid process, data collection should also be adjustable to changes and developments in data generation over time.

Data analysis is often split into multiple phases, with primary analysis of each assay followed by steps for integration of results. Specific results need to be fed back to metadata management, annotation, [[quality control]], or storing resulting markers. Access to metadata with recorded factors and confounders is necessary in each step, while access to primary raw data becomes less important after the primary analysis. Certain analysis results are written back into the mass data storage. This includes binary alignment map (BAM) files and variant call format (VCF) files.

There are also overarching challenges for the steps in study execution. All data should be recorded in structured format. [[Laboratory automation|Automation]] should be applied where possible, and on-premises installation might be preferable or even required when [[Information privacy|data privacy]]–relevant data are generated such as with [[DNA sequencing]].

===Data management approaches===
In this and the following section, we will discuss the topic of [[Information management||data management]] and software. The terms “data” and “document” will be used interchangeably in this section. The steps described in the prior section can be interpreted as processes taking documents and materials as input, as well as generating more documents and materials as the result. For example, data collection takes the plan document and samples and generates assay result files (documents). Scientists thus need computational tools for supporting them in managing their scientific and research data.

Historically, such documents are maintained on paper in [[laboratory notebook]]s, or documentation created by [[Quality management system|quality management systems]] (QMSs). For the most direct and unstructured approaches in maintaining digital data, this corresponds to word processing, spreadsheet, and image files on local or network drives. More structured approaches are desirable for taking advantage of digital documents, preventing research data loss [1] or fostering reuse. [2]

While data management in science is a broad topic, the library and [[information science]] community is frequently approaching it using a top-down approach. Frequently, in this context, the term “research data management” (RDM) is used. Here, the needs of whole organizations and their parts for managing their research data, as well as the necessary steps to establish whole RDM systems, are considered first (''cf.'' Donner [3]). This correlates with the role of libraries in certain academic organizations for organizing data that were collected in research.

A second approach, which can be described as “bottom-up,” originates from different “working scientist” communities. The communities commonly refer to the topic as “scientific data management” (SDM) and solve their problems at hand, often starting with specific small-scale solutions, which are then upscaled if the need arises. While considering their organizational embedding, they focus on solving specific data management challenges for themselves and their peers. We found ourselves in this situation and will thus focus on this perspective.

===Data management software packages===
SDM needs come in different forms and shapes. We could find no general treatment of the subject of data management in the literature. Machina and Wild [4] provide a collection of four tool categories: [[laboratory information management system]]s (LIMSs), [[electronic laboratory notebook]]s (ELNs), [[scientific data management system]]s (SDMSs), and [[chromatography data system]]s (CDSs) that we generalize as instrument-specific data systems (IDS). In this section, we provide our take on explaining what these systems comprise. We also note—as Machina and Wild [4] did—that categorization of such software solutions is not clear-cut, and features may be overlapping. We expand this list by two more system types: data repository systems (DRSs) and [[database]]/[[data warehouse]] management frameworks (DMFs).

The four tools described by Machina and Wild [4] are as follows:

* A LIMS focuses on storing information around [[laboratory]] [[workflow]]s. This includes tracking of consumables, samples, instruments, and tests. A LIMS deals with daily tasks of laboratories such as billing and instrument calibration. It is often specific to certain domain areas such as [[sequencing]] facilities.
* The ELN focuses on allowing humans to record their laboratory work. It replaces paper notebooks and captures experiments and their results, mostly in free-form text, pictures, tables, and so on. The ELN plays a key role in fulfilling regulatory requirements.
* The CDS/IDS provides data capturing, storage, and analysis functionality in instrument-specific domains. Two examples are the CASAVA pipeline and the BaseSpace [[Cloud computing|cloud-based service]], both from [[Illumina, Inc.|Illumina]]. The former is provided without extra cost with the instrument along with its source code, while the latter is purchasable and closed source. Such software often ships with the instruments themselves.
* The SDMS provides scientific content management functionality for scientific data and documentation. It allows for the management of metadata and potentially mass data. The SDMS's core functionality generally doesn't include data analysis, user-centric data collection, or laboratory workflow tracking. Such features may be potentially supported by plugins or extensions. Many such systems offer integration with surrounding systems (e.g., via [[application programming interface]]s [APIs]).

We augment this list by two system types:

* The DRS provides shared access to data with appropriate documentation and metadata. Examples are FAIRdom Seek [5], Dataverse [6], and Yoda. [7] There are also specialized DRSs focusing on particular use cases, such as dbGAP [8], MetaboLights [9], and Gene Expression Omnibus [10], that allow for managing public or controlled public access to large research data collections.
* The DMF allows for the rapid development of database and data warehouse applications. It often provides preexisting components to build on ready-made functionality and extension by implementing custom components. This enables the creation of domain-specific databases and structured data capturing. Examples include Molgenis [11] and Zendro. [12]

Other types of systems also exist, and not every system falls into just one category. A complete review of such systems is beyond the scope of this article. This section identifies focus areas of systems involved in some form of scientific data management. SODAR falls into the category of SDMS.

===Data management technologies===
For planning and documenting experiments and their structure, experiment-oriented metadata storage formats with predefined syntax and semantics exist. A popular standard is the ISA (Investigation, Study, Assay) model [13], which allows describing studies with multiple samples and assays. The ISA model defines the ISA-Tab tabular file format, which allows users to model each processing step with each intermediate result and annotate each of these with arbitrary metadata. An example of an alternative to ISA-Tab is Portable Encapsulated Projects (PEPs). [14] There are also more specialized standards such as Brain Imaging Data Structure (BIDS) for brain imaging data [15], as well as other approaches such as [[Clinical Data Interchange Standards Consortium]] (CDISC) standards [16] and the Hierarchical Data Format (HDF5). [17] Use of generic file formats such as HDF5, TSV, XML, and JSON is also common.

For storing large volumes of omics data, it is possible to simply use file systems or object storage systems. More advanced solutions such as Shock [18] or dCache [19] allow for storing metadata and distributing data over multiple servers. iRODS (Integrated Rule-Oriented Data System) [20, 21] adds further features, such as running rules and programs within the data system and enabling integration with arbitrary authentication methods.

For publication, raw and processed data and metadata are deposited in scientific catalogs, study databases, and registries. Examples include the BioSamples database for metadata [22] and Sequence Read Archive (SRA) for raw sequencing data. [23]

===Our work===
In our work, we focus on managing many omics projects of varying data size and various use cases, including cancer and functional genomics studies. We also need to support multiple technologies such as whole-genome sequencing, single-cell sequencing, proteomics, and mass spectrometry. Deposition to public repositories was not a necessity in our context. However, SODAR is an ISA-compliant system. Should the data owner require it, it is easily feasible to create appropriate exports to public data repositories using the APIs provided by SODAR. Open-source software is a requirement to avoid vendor lock-in and allow for flexibility in different use cases. A suitable end-to-end solution was not available when we started our work in 2016. Therefore, we set out to implement an integrated system for managing omics-specific data and metadata.

In this article, we introduce SODAR (System for Omics Data Access and Retrieval). SODAR combines the modeling of studies and assays using the ISA-Tab format with handling of mass data storage using iRODS. More example projects are available in the SODAR online demo server [41].

==References==
{{Reflist|colwidth=30em}}

==Notes==
This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added.


[[Category:LIMSwiki journal articles (added in 2023)]]
[[Category:LIMSwiki journal articles (all)]]
[[Category:LIMSwiki journal articles on clinical research]]
[[Category:LIMSwiki journal articles on forensic science]]