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Related Concept Videos

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RNA-seq

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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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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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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.
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Pre-mRNA Processing: Modification of pre-mRNA Ends01:35

Pre-mRNA Processing: Modification of pre-mRNA Ends

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In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a cap to the 5' end of the growing transcript. In this process, a 5' phosphate is replaced by modified guanosine that has a methyl group attached (7-methyl guanosine). This 5' cap helps...
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pre-mRNA Processing02:01

pre-mRNA Processing

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In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
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RNA Editing02:23

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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Updated: May 13, 2025

Author Spotlight: Decoding RNA Methylation's Role in Pancreatic Cancer - A Single-Base Resolution Study
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Author Spotlight: Decoding RNA Methylation's Role in Pancreatic Cancer - A Single-Base Resolution Study

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The RMaP challenge of predicting RNA modifications by nanopore sequencing.

Jannes Spangenberg1, Stefan Mündnich2, Anne Busch3

  • 1RNA Bioinformatics, Friedrich-Schiller-University Jena, Leutragraben 1, 07743, Jena, Germany.

Communications Chemistry
|April 12, 2025
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Summary
This summary is machine-generated.

Epitranscriptomics research is advancing with new computational methods for detecting RNA modifications like N6-methyladenosine (m6A) and pseudouridine (ψ). The RMaP challenge improved RNA modification prediction accuracy and highlighted areas for future development.

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Area of Science:

  • Epitranscriptomics
  • Computational Biology
  • Bioinformatics

Background:

  • RNA modifications play critical roles in cellular processes.
  • Direct RNA sequencing technologies enable detection of these modifications in native RNA.
  • The integration of computer science is crucial for advancing epitranscriptomics.

Purpose of the Study:

  • To bring scientists together to advance RNA modification detection solutions.
  • To discuss ideas, problems, and approaches in RNA modification detection.
  • To improve the comparability, reliability, and consistency of RNA modification prediction algorithms.

Main Methods:

  • Utilized direct RNA sequencing data from Oxford Nanopore Technologies (ONT).
  • Applied and compared several computational methods for detecting mRNA modifications.
  • Focused on N6-methyladenosine (m6A), pseudouridine (ψ), and 5-methylcytosine (m5C).

Main Results:

  • Achieved low prediction error and high prediction accuracy for m6A, ψ, and m5C.
  • Demonstrated the effectiveness of various computational approaches and algorithms.
  • Highlighted the potential of computational methods in epitranscriptomics.

Conclusions:

  • The RMaP challenge significantly advanced RNA modification prediction.
  • Computational methods show high accuracy in detecting key mRNA modifications.
  • Further challenges are needed to address deficits in the young field of epitranscriptomics.