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A call for public archives for biological image data.

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TLDR
The rationale for the construction of bioimage archives and their associated databases to underpin the next revolution in bioinformatics discovery is presented.
Abstract
Public data archives are the backbone of modern biological research. Biomolecular archives are well established, but bioimaging resources lag behind them. The technology required for imaging archives is now available, thus enabling the creation of the first public bioimage datasets. We present the rationale for the construction of bioimage archives and their associated databases to underpin the next revolution in bioinformatics discovery.

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Spatial proteomics: a powerful discovery tool for cell biology.

TL;DR: Spatial proteomics improves the understanding of protein function by revealing the subcellular localizations of proteins and their movement between compartments, and ways to improve integration of spatial proteomics data are discussed.
Journal ArticleDOI

Tutorial: guidance for quantitative confocal microscopy.

TL;DR: This tutorial and the accompanying poster provide a guide for performing quantitative fluorescence imaging using confocal microscopy, including advice and troubleshooting information from sample preparation and microscope setup to data analysis and statistics.
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Image-based profiling for drug discovery: due for a machine-learning upgrade?

TL;DR: How the application of machine learning is renewing interest in image-based profiling for all aspects of the drug discovery process, from understanding disease mechanisms to predicting a drug’s activity or mechanism of action is discussed.
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OpenCell: Endogenous tagging for the cartography of human cellular organization

TL;DR: This work combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins, and shows that proteins that bind RNA form a separate subgroup defined by specific localization and interaction signatures.
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Reproducibility standards for machine learning in the life sciences.

TL;DR: To make machine-learning analyses in the life sciences more computationally reproducible, standards based on data, model and code publication, programming best practices and workflow automation are proposed.
References
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Journal ArticleDOI

DNA sequencing with chain-terminating inhibitors

TL;DR: A new method for determining nucleotide sequences in DNA is described, which makes use of the 2',3'-dideoxy and arabinon nucleoside analogues of the normal deoxynucleoside triphosphates, which act as specific chain-terminating inhibitors of DNA polymerase.
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The FAIR Guiding Principles for scientific data management and stewardship

TL;DR: The FAIR Data Principles as mentioned in this paper are a set of data reuse principles that focus on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
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A new method for sequencing DNA

TL;DR: Reactions that cleave DNA preferentially at guanines, at adenines,At cytosines and thymines equally, and at cytosine alone are described.
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From core referencing to data re-use: two French national initiatives to reinforce paleodata stewardship (National Cyber Core Repository and LTER France Retro-Observatory)

TL;DR: ROZA was developed under the umbrella of LTER-France (Long Term Ecological Research) in order to facilitate the re-use of data and samples and will favor to use of paleodata by non-paleodata scientists, in particular ecologists.
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The FAIR Guiding Principles for scientific data management and stewardship