The BioImage Archive – Building a Home for Life-Sciences Microscopy Data

Hartley, Matthew; Kleywegt, Gerard J.; Patwardhan, Ardan; Sarkans, Ugis; Swedlow, Jason R.; Brāzma, Alvis

doi:10.1016/j.jmb.2022.167505

Cited by 70 publications

(42 citation statements)

References 31 publications

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“…The second is the BioImage Archive 28 , an image data repository hosted by the European Bioinformatics Institute. It accepts image data submissions associated with publications in the life-sciences.…”

Section: Methodsmentioning

confidence: 99%

“…Overall, MoBIE enables life scientists to seamlessly access, explore and share their often massive microscopy data through all stages of the lifetime of a project in a convenient Fiji plugin. We are working with online archives to deploy MoBIE to support on-demand access to existing scientific image data and have already implemented two prototypes for this kind of access, with the BioImage Archive 24 and with Open Organelle 25 . See Methods for details.…”

Section: Mainmentioning

confidence: 99%

“…Overall, MoBIE enables scientists to seamlessly access, explore and share their massive microscopy data through all stages of a project in a convenient Fiji plugin. We are working with online archives to deploy MoBIE to support on-demand access to existing scientific image data and have already implemented a prototype with the BioImage Archive 11 . Furthermore, we are convinced that many of MoBIE’s features are of general use and aim to contribute its functionality into “upstream” core components such as BDV.…”

Section: Mainmentioning

confidence: 99%

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MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

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Meechan

Moreva

et al. 2022

Preprint

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Facing the challenge of exploring and sharing multi-terabyte, multi-modal and multi-scale image data of heterogeneous dimensionality, we developed MoBIE, a Fiji plugin that provides rich visualization features to enable browsing data from numerous biomedical applications on a standard laptop computer. MoBIE also supports segmentations, associated measurements and annotations. Users can configure complex views of datasets, share them with collaborators, and use them for interactive figure panels. The MoBIE plugin also offers a convenient interface for converting data into compatible data formats; an additional Python library facilitates managing diverse MoBIE projects.

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Section: Methodsmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

See 1 more Smart Citation

MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

Pape

Meechan

Moreva

et al. 2022

Preprint

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“…Sharing of 3D imaging data to a similar extend as currently achieved for sequencing data will be important. The BioImage Archive, a central archival system at EMBL-EBI (Hartley et al, 2022), now provides infrastructure for archiving and accessing existing imaging data. Although already a highly commendable resource, supporting patient-identifiable data in the future could further advance its application in cancer research.…”

Section: Increasing the Dimensionality Of 3d Datasetsmentioning

confidence: 99%

3D imaging for driving cancer discovery

et al. 2022

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Our understanding of the cellular composition and architecture of cancer has primarily advanced using 2D models and thin slice samples. This has granted spatial information on fundamental cancer biology and treatment response. However, tissues contain a variety of interconnected cells with different functional states and shapes, and this complex organization is impossible to capture in a single plane. Furthermore, tumours have been shown to be highly heterogenous, requiring large-scale spatial analysis to reliably profile their cellular and structural composition. Volumetric imaging permits the visualization of intact biological samples, thereby revealing the spatio-phenotypic and dynamic traits of cancer. This review focuses on new insights into cancer biology uniquely brought to light by 3D imaging and concomitant progress in cancer modelling and quantitative analysis. 3D imaging has the potential to generate broad knowledge advance from major mechanisms of tumour progression to new strategies for cancer treatment and patient diagnosis. We discuss the expected future contributions of the newest imaging trends towards these goals and the challenges faced for reaching their full application in cancer research.

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“…Previous initiatives have established multiple high-quality repositories for biological imaging data, including the BioImage Archive [ 56 ] and Open Microscopy Environment [ 57 ]. We anticipate that our resource will integrate seamlessly with these repositories, allowing users to directly import data from outside sources into our pipeline using dedicated plugins.…”

Section: Addressing Outstanding Questions In Cell Biologymentioning

confidence: 99%

Towards ‘end-to-end’ analysis and understanding of biological timecourse data

Jena

Goglia

Engelhardt

2022

Biochemical Journal

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Petabytes of increasingly complex and multidimensional live cell and tissue imaging data are generated every year. These videos hold large promise for understanding biology at a deep and fundamental level, as they capture single-cell and multicellular events occurring over time and space. However, the current modalities for analysis and mining of these data are scattered and user-specific, preventing more unified analyses from being performed over different datasets and obscuring possible scientific insights. Here, we propose a unified pipeline for storage, segmentation, analysis, and statistical parametrization of live cell imaging datasets.

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The BioImage Archive – Building a Home for Life-Sciences Microscopy Data

Cited by 70 publications

References 31 publications

MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

3D imaging for driving cancer discovery

Towards ‘end-to-end’ analysis and understanding of biological timecourse data

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