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Related Concept Videos

Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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Announcing the Biomedical Data Translator: Initial Public Release.

Karamarie Fecho1,2, Gwênlyn Glusman3, Sergio E Baranzini4

  • 1Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

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The Biomedical Data Translator (Translator) system integrates diverse biomedical data using a knowledge graph. This open-source tool aids researchers in generating hypotheses and accelerating translational research for improved patient care.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Translational Science

Background:

  • Biomedical data complexity and lack of integration limit its utility for improving human health.
  • A need exists for systems that can integrate, harmonize, and infer knowledge from diverse biomedical datasets.

Purpose of the Study:

  • To announce the initial public release of the open-source Biomedical Data Translator (Translator) system.
  • To provide an overview of Translator's architecture, standards, user interface, and core features.
  • To demonstrate Translator's application in real-world use cases for accelerating translational research.

Main Methods:

  • Development of a scalable, federated, knowledge graph framework.
  • Integration of diverse biomedical knowledge sources (clinical, genomic, pharmacological).
  • Design of a user-friendly interface for knowledge exploration and insight generation.

Main Results:

  • Initial public release of the Translator system.
  • Demonstration of Translator's utility in suggesting therapeutics for rare diseases, explaining drug mechanisms, and screening drug candidates.
  • Highlighting the system's ability to retrieve queries, generate inferences, and support hypothesis generation.

Conclusions:

  • Translator represents a significant advancement in making complex biomedical knowledge accessible and actionable.
  • The system aims to accelerate translational research and improve patient care by facilitating data integration and knowledge discovery.
  • Future directions include enhancing functionality and expanding data sources for broader impact.