4.8 Article

Datanator: an integrated database of molecular data for quantitatively modeling cellular behavior

Journal

NUCLEIC ACIDS RESEARCH
Volume 49, Issue D1, Pages D516-D522

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkaa1008

Keywords

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Funding

  1. National Institutes of Health [P41EB023912, R35GM119771]
  2. National Science Foundation [548123]
  3. NIH [P41EB023912]
  4. Icahn Institute of Data Science and Genomic Technology

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The development of Datanator, an integrated database and tools for biochemical research, aims to facilitate the search for various types of data related to specific molecules and reactions in different organisms. Currently, the database includes data such as metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction rate constants for a wide range of organisms. Future plans include launching a community initiative to curate additional data.
Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering and medicine. However, it is difficult to obtain the diverse data needed for integrative research. To facilitate biochemical research, we developed Datanator (https://datanator.info), an integrated database and set of tools for finding clouds of multiple types of molecular data about specific molecules and reactions in specific organisms and environments, as well as data about chemically-similar molecules and reactions in phylogenetically-similar organisms in similar environments. Currently, Datanator includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction rate constants about a broad range of organisms. Going forward, we aim to launch a community initiative to curate additional data. Datanator also provides tools for filtering, visualizing and exporting these data clouds. We believe that Datanator can facilitate a wide range of research from integrative mechanistic models, such as whole-cell models, to comparative data-driven analyses of multiple organisms.

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