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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...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Translation01:31

Translation

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.
Translation Produces the Building Blocks of Life
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...

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An intuitive Python interface for Bioconductor libraries demonstrates the utility of language translators.

Laurent Gautier1

  • 1DMAC, Centre for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Matematiktorvet, 2100 Lyngby, Denmark. laurent@cbs.dtu.dk

BMC Bioinformatics
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Bioconductor, a powerful R-based tool for biological data analysis, is now accessible directly within Python. This integration allows Python programmers to leverage Bioconductor

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Multidisciplinary projects require expertise in multiple programming languages.
  • Bioconductor, an R-based project, is a leading resource for high-throughput biological data analysis.
  • Python is a versatile language for data handling and rapid development.

Purpose of the Study:

  • To enable Python users to access Bioconductor's functionalities.
  • To bridge the gap between R and Python in bioinformatics research.
  • To demonstrate the seamless integration of Bioconductor within a Python environment.

Main Methods:

  • Exposing Bioconductor data structures and functions as a Python library.
  • Implementing Python representations for key Bioconductor infrastructure packages.
  • Developing tools for handling annotation, microarray, and next-generation sequencing data in Python.

Main Results:

  • Bioconductor functionalities can be used natively within Python.
  • Python programmers can manage biological data without R knowledge.
  • Demonstrated Python integration for annotation, microarray, and NGS data.

Conclusions:

  • Bioconductor is no longer exclusive to R users.
  • Python applications can now utilize Bioconductor features as if it were a native Python package.
  • The integration principles are applicable to other languages and libraries.