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Related Concept Videos

Ribosome Profiling02:24

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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.
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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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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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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data.

Yaxin Xue1, Anders Lanzén2,3, Inge Jonassen1

  • 1Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.

Bioinformatics (Oxford, England)
|March 14, 2020
PubMed
Summary
This summary is machine-generated.

MetaRib reconstructs ribosomal gene sequences from total RNA meta-transcriptomic data, improving speed and accuracy for microbial community analysis. This tool handles large datasets, enabling deeper insights into microbial structure and function.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Meta-transcriptomics offers insights into microbial community structure and function.
  • Reconstructing full-length taxonomic marker genes, like ribosomal RNA, is crucial for this analysis.
  • Existing tools struggle with the large, complex datasets generated by total RNA meta-transcriptomics.

Purpose of the Study:

  • To introduce MetaRib, a novel computational tool for reconstructing ribosomal gene sequences from total RNA meta-transcriptomic data.
  • To address the limitations of current tools in handling massive and complex datasets.

Main Methods:

  • MetaRib is an improved version of the EMIRGE rRNA assembly program.
  • It integrates sub-assembly, dereplication, and mapping in an iterative approach with post-processing.
  • The method was validated using both simulated and real-world total RNA meta-transcriptomic datasets.

Main Results:

  • MetaRib demonstrates a significant speedup (approximately 60x) and higher F1 score compared to EMIRGE on simulated data.
  • It effectively reconstructs more ribosomal RNA genes from large, complex datasets.
  • Analysis of a real-world dataset showed MetaRib recovered more contigs and enabled comparative abundance analysis across samples.

Conclusions:

  • MetaRib is an efficient and accurate tool for ribosomal gene reconstruction from total RNA meta-transcriptomic data.
  • It overcomes the challenges posed by large datasets, facilitating comprehensive microbial community analysis.
  • The tool enhances the understanding of microbial community structure and function across all domains of life.