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Related Concept Videos

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.
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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...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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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 stands for...
Ribosomal RNA Synthesis02:53

Ribosomal RNA Synthesis

Ribosome synthesis is a highly complex and coordinated process involving more than 200 assembly factors. The synthesis and processing of ribosomal components occurs not only in the nucleolus but also in the nucleoplasm and the cytoplasm of eukaryotic cells.
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RNA Structure01:23

RNA Structure

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The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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Related Experiment Video

Updated: Jul 10, 2026

Single Nucleotide Polymorphism-sensitive FISH Detection of Locus-specific Ribosomal RNA Transcription in Drosophila melanogaster
04:59

Single Nucleotide Polymorphism-sensitive FISH Detection of Locus-specific Ribosomal RNA Transcription in Drosophila melanogaster

Published on: March 28, 2025

Locality and gaps in RNA comparison.

Rolf Backofen1, Shihyen Chen, Danny Hermelin

  • 1Institute of Computer Science, Albert-Ludwigs Universität Freiburg, Freiburg, Germany.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 8, 2007
PubMed
Summary

This study introduces new local and global RNA alignment algorithms. These methods improve RNA sequence comparison by incorporating structural features and affine gap penalties, offering robust and efficient analysis.

Related Experiment Videos

Last Updated: Jul 10, 2026

Single Nucleotide Polymorphism-sensitive FISH Detection of Locus-specific Ribosomal RNA Transcription in Drosophila melanogaster
04:59

Single Nucleotide Polymorphism-sensitive FISH Detection of Locus-specific Ribosomal RNA Transcription in Drosophila melanogaster

Published on: March 28, 2025

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Comparative analysis of biological sequences relies on locality.
  • Affine gap penalties enhance biological sequence alignment accuracy.
  • RNA analysis requires considering both sequential and structural features, complicating comparisons.

Purpose of the Study:

  • To develop novel local metrics for RNA comparison, extending the Smith-Waterman algorithm.
  • To introduce a global RNA alignment algorithm that incorporates affine gap penalties.
  • To provide efficient and robust algorithms for RNA sequence and structure comparison.

Main Methods:

  • Introduced two local metrics for RNA comparison.
  • Developed a global RNA alignment algorithm handling affine gap penalties.
  • Analyzed algorithm time complexities: global O(m^2n(1 + lg n/m)), local O(m^2n(1 + lg n/m)) and O(n^2m).

Main Results:

  • The proposed local and global RNA alignment algorithms are efficient.
  • The algorithms achieve time complexities comparable to existing RNA alignment methods.
  • Both global and local algorithms demonstrate robustness to arbitrary scoring schemes.

Conclusions:

  • The new algorithms provide effective tools for comparing RNA molecules, considering both sequence and structure.
  • The computational efficiency and robustness make these algorithms suitable for analyzing larger RNA datasets.
  • This work advances the field of RNA bioinformatics by offering improved comparative analysis techniques.