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RNA Splicing01:32

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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Methods to study splicing from high-throughput RNA sequencing data.

Gael P Alamancos1, Eneritz Agirre, Eduardo Eyras

  • 1Computational Genomics, Universitat Pompeu Fabra, Barcelona, Spain.

Methods in Molecular Biology (Clifton, N.J.)
|February 20, 2014
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Summary
This summary is machine-generated.

High-throughput sequencing (HTS) for RNA (RNA-Seq) offers deep insights into splicing variations. This review classifies available RNA-Seq analysis tools to aid researchers in selecting appropriate methods for splicing event studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • High-throughput sequencing (HTS) methods, particularly RNA sequencing (RNA-Seq), enable in-depth analysis of RNA splicing.
  • The complexity of RNA-Seq data presents significant analytical challenges for studying splicing events.
  • Numerous bioinformatics tools have emerged to address the analysis of RNA splicing from RNA-Seq data.

Purpose of the Study:

  • To provide a comprehensive overview of available methods for analyzing splicing from short RNA-Seq data.
  • To serve as a guide for researchers in selecting appropriate tools for their specific splicing analysis needs.
  • To propose a classification of splicing analysis tools based on their functionalities.

Main Methods:

  • Literature review and survey of existing bioinformatics tools for RNA-Seq data analysis.
  • Categorization of tools based on their primary functions in splicing event analysis.
  • Comparative analysis of tool capabilities for studying isoform expression and splicing changes.

Main Results:

  • Identification and description of a wide range of tools for RNA splicing analysis.
  • A proposed classification system for RNA-Seq splicing analysis tools.
  • Guidance for researchers on tool selection based on analysis objectives.

Conclusions:

  • The proliferation of RNA-Seq analysis tools necessitates a structured approach to selection.
  • A clear classification of tools aids researchers in navigating the complex landscape of splicing analysis.
  • This overview and classification aim to streamline the process of choosing the right bioinformatics tool for RNA splicing studies.