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Related Concept Videos

RNA-seq03:21

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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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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Related Experiment Video

Updated: Aug 19, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Bioinformatic tools for epitranscriptomics.

Y-H Taguchi1

  • 1Department of Physics, Chuo University, Tokyo, Japan.

American Journal of Physiology. Cell Physiology
|December 5, 2022
PubMed
Summary

The epitranscriptome, RNA modifications without sequence changes, requires advanced computational tools for analysis. This review covers new methods for detecting and analyzing epitranscriptomic data from high-throughput sequencing.

Area of Science:

  • Genomic Sciences
  • Molecular Biology
  • Bioinformatics

Background:

  • The epitranscriptome refers to RNA modifications that do not alter the nucleotide sequence.
  • Identifying epitranscriptomic sites necessitates sophisticated computational approaches.
  • High-throughput sequencing generates large datasets for epitranscriptome analysis.

Purpose of the Study:

  • To review recent advancements in the spatial detection of epitranscriptomes.
  • To summarize new tools and techniques for analyzing epitranscriptomic data.
  • To discuss the progression and future directions in the field.

Main Methods:

  • Review of current literature on epitranscriptomic detection methods.
  • Analysis of computational tools for inferring epitranscriptomic sites.
Keywords:
bioinformatic toolsepitranscriptomics

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  • Examination of data processing techniques for high-throughput sequencing datasets.

    • Main Results:

    • Recent developments have improved the spatial resolution of epitranscriptome detection.
    • Novel computational tools facilitate the inference and analysis of epitranscriptomic data.
    • The field is rapidly progressing with new methodologies emerging.

    Conclusions:

    • Advanced computational techniques are crucial for understanding the epitranscriptome.
    • Continued development in detection and data analysis tools is essential.
    • The study of RNA modifications is a dynamic and expanding area within genomic sciences.