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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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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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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: Apr 16, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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rSeqNP: a non-parametric approach for detecting differential expression and splicing from RNA-Seq data.

Yang Shi1, Arul M Chinnaiyan2, Hui Jiang1

  • 1Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Department of Biostatistics, Michigan Center for Translational Pathology, Department of Pathology, Comprehensive Cancer Center, Howard Hughes Medical Institute and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.

Bioinformatics (Oxford, England)
|February 27, 2015
PubMed
Summary

A new R package, rSeqNP, offers a non-parametric method for analyzing RNA-Seq data to detect differential gene expression and splicing. This approach enhances gene discovery by integrating isoform information for greater accuracy.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput RNA sequencing (RNA-Seq) is crucial for gene expression studies.
  • Analyzing differential gene expression and splicing requires robust statistical methods.

Purpose of the Study:

  • Introduce rSeqNP, an R package for non-parametric analysis of RNA-Seq data.
  • To improve the detection of differentially expressed and spliced genes.

Main Methods:

  • Utilizes a non-parametric approach for statistical testing.
  • Employs permutation tests to determine statistical significance.
  • Integrates information across multiple isoforms for enhanced analysis.

Main Results:

  • rSeqNP can be applied to diverse experimental designs.
  • Demonstrates improved detection of differentially expressed and spliced genes compared to existing methods.
  • Provides a freely available R package with source code and documentation.

Conclusions:

  • rSeqNP offers a powerful and flexible tool for RNA-Seq data analysis.
  • The package facilitates more comprehensive gene expression and splicing studies.
  • Enhances the discovery of biologically relevant genes from RNA-Seq data.