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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

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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...
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Translational Regulation01:29

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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,...
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Leaky Scanning02:28

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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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Next-generation Sequencing03:00

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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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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Updated: Mar 25, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Translational Bioinformatics: Past, Present, and Future.

Jessica D Tenenbaum1

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA.

Genomics, Proteomics & Bioinformatics
|February 16, 2016
PubMed
Summary
This summary is machine-generated.

Translational bioinformatics (TBI) is crucial for precision medicine, transforming large datasets into actionable knowledge. This review defines TBI, outlines its history, and predicts future directions in biomedical research.

Keywords:
BiomarkersGenomicsPersonalized medicinePrecision medicineTranslational bioinformatics

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Area of Science:

  • Biomedical research
  • Bioinformatics
  • Precision medicine

Background:

  • High-throughput technologies and electronic health records are revolutionizing healthcare and biomedical research.
  • There is a growing need for advanced methods to manage and interpret vast biological datasets.
  • Translational bioinformatics (TBI) has emerged as a critical field to bridge this gap.

Purpose of the Study:

  • To define and contextualize translational bioinformatics (TBI).
  • To review the history, accomplishments, and current focus of TBI.
  • To predict future trends and directions in the field of TBI.

Main Methods:

  • Literature review
  • Historical analysis
  • Trend analysis

Main Results:

  • TBI is a rapidly evolving discipline essential for precision medicine.
  • Significant advancements have been made in applying bioinformatics to clinical data.
  • The field is poised for continued growth with new technological integration.

Conclusions:

  • Translational bioinformatics is fundamental to leveraging big data in modern biomedical research.
  • Future directions include integrating diverse data types and developing more sophisticated analytical tools.
  • TBI will continue to drive innovation in personalized healthcare and therapeutic development.