From ArrayExpress to BioStudies

Sarkans, Ugis; Füllgrabe, Anja; Ali, Ahmed; Athar, Awais; Behrangi, Ehsan; Díaz, Néstor Fabián; Fexová, Silvie; George, Nancy; Iqbal, Haider; Kurri, Sandeep; Munoz, Jhoan; Rada, Juan Camillo; Papatheodorou, Irene; Brāzma, Alvis

doi:10.1093/nar/gkaa1062

Cited by 71 publications

(51 citation statements)

References 20 publications

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“…gov/geo/, 4 October 2021) and AE (https://www.ebi.ac.uk/arrayexpress/, 4 October 2021). The AE database is currently being migrated to the BioStudies database [33]. The complete transition is expected in 2021, after which AE will be completely phased out.…”

Section: Discussionmentioning

confidence: 99%

ARGEOS: A New Bioinformatic Tool for Detailed Systematics Search in GEO and ArrayExpress

Gavrish

Chistyakov

Sergeeva

2021

Biology

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Conduct a reanalysis of transcriptome data for studying intracellular signaling or solving other experimental problems is becoming increasingly popular. Gene expression data are archived as microarray or RNA-seq datasets mainly in two public databases: Gene Expression Omnibus (GEO) and ArrayExpress (AE). These databases were not initially intended to systematically search datasets, making it challenging to conduct a secondary study. Therefore, we have created the ARGEOS service, which has the following advantages that facilitate the search: (1) Users can simultaneously send several requests that are supposed to be used for systematic searches, and it is possible to correct the requests; (2) advanced analysis of information about the dataset is available. The service collects detailed protocols, information on the number of datasets, analyzes the availability of raw data, and provides other reference information. All this contributes to both rapid data analysis with the search for the most relevant datasets and to the systematic search with detailed analysis of the information of the datasets. The efficiency of the service is shown in the example of analyzing transcriptome data of activated (polarized) cells. We have performed a systematic search of studies of cell polarization (when cells are exposed to different immune stimuli). The web interface for ARGEOS is user-friendly and straightforward. It can be used by a person who is not familiar with database searching.

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Section: Discussionmentioning

confidence: 99%

ARGEOS: A New Bioinformatic Tool for Detailed Systematics Search in GEO and ArrayExpress

Gavrish

Chistyakov

Sergeeva

2021

Biology

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“…The proposed standard metadata representation will also facilitate the development of common submission systems to streamline the deposition of multiomics datasets to resources for different types of omics data. It may even foster the creation of new multiomics resources, enabling submission, validation, and visualization of multi-omics data in one place 33 , 34 . As an initial step in this direction, the PRIDE group has started to reanalyze proteomics datasets represented in SDRF and integrates them into the multiomics resource Expression Atlas (e.g., https://www.ebi.ac.uk/gxa/experiments/E-PROT-18/Results ).…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

A proteomics sample metadata representation for multiomics integration and big data analysis

et al. 2021

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The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

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“…The proposed standard representation will facilitate the integration and annotation of proteomics studies, thereby enhancing their discoverability, reproducibility, and amenability to systematic reprocessing. In addition, it will enable the development of common submission systems of multi-omics datasets between resources different types of omics data, or even new multi-omics resources, enabling submission, validation, and visualization of multi-omics data 31,32 . Multiple efforts are ongoing to provide tools that help users to annotate datasets on the web.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

A proteomics sample metadata representation for multiomics integration, and big data analysis

Dai

Füllgrabe

Pfeuffer

et al. 2021

Preprint

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The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the users choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data. Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGE-TAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

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From ArrayExpress to BioStudies

Cited by 71 publications

References 20 publications

ARGEOS: A New Bioinformatic Tool for Detailed Systematics Search in GEO and ArrayExpress

ARGEOS: A New Bioinformatic Tool for Detailed Systematics Search in GEO and ArrayExpress

A proteomics sample metadata representation for multiomics integration and big data analysis

A proteomics sample metadata representation for multiomics integration, and big data analysis

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