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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Leaky Scanning02:28

Leaky Scanning

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 stands for...
Translational Regulation01:29

Translational Regulation

Translational regulation in prokaryotes ensures efficient protein synthesis by controlling ribosome access to mRNA. This regulation is mediated by secondary RNA structures, including translational riboswitches, RNA thermometers, and small RNAs (sRNAs), which respond to intracellular and environmental signals to modulate gene expression.Translational RiboswitchesRiboswitches in the leader region of mRNAs can regulate translation by altering the accessibility of the Shine-Dalgarno (SD) sequence,...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
Cotranslational Protein Translocation01:20

Cotranslational Protein Translocation

Translocation of proteins across membranes is an ancient process that occurs even in bacteria and archaebacteria. In fact, the components of the translocation machinery are still conserved between prokaryotes and eukaryotes.
Sec61 channel partners for cotranslational translocation
During cotranslational translocation, the Sec61 channel partners with the signal recognition particle (SRP), the signal recognition particle receptor (SR), and the ribosomes to transport the nascent polypeptide chain...

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Rapid In Vivo Fixation and Isolation of Translational Complexes from Eukaryotic Cells
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Published on: December 25, 2021

Translational informatics: enabling high-throughput research paradigms.

Philip R O Payne1, Peter J Embi, Chandan K Sen

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA. philip.payne@osumc.edu

Physiological Genomics
|September 10, 2009
PubMed
Summary
This summary is machine-generated.

Biomedical informatics lacks standardized frameworks for managing large datasets, hindering clinical and translational research. This study presents a conceptual and practical framework to address data silos and improve research efficiency.

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Area of Science:

  • Biomedical Informatics
  • Translational Research
  • Data Management

Background:

  • Clinical and translational research requires managing large, heterogeneous biomedical datasets.
  • Existing literature lacks established theoretical and practical frameworks for this data management.
  • Data and expertise silos impede the development and execution of multidisciplinary studies.

Purpose of the Study:

  • To address the absence of standardized frameworks in biomedical informatics for large-scale data management.
  • To present a conceptual and practical framework for informatics-enabled clinical and translational research.
  • To provide a common reference for the research community to discuss and advance data management methodologies.

Main Methods:

  • Review of the current state and evolution of biomedical informatics.
  • Development of a conceptual framework for informatics-enabled research.
  • Presentation of a practical framework for designing and executing studies.

Main Results:

  • Identification of data and expertise silos as a significant limitation.
  • Proposal of a novel framework to integrate and manage biomedical data.
  • Demonstration of a structured approach for informatics-enabled research.

Conclusions:

  • A standardized framework is crucial for efficient clinical and translational research.
  • The proposed framework aims to overcome data silos and enhance data integration.
  • This work provides a foundation for advancing biomedical informatics methodologies.