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Related Concept Videos

Alternative RNA Splicing02:18

Alternative RNA Splicing

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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.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...
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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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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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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Clustering of mRNA-Seq data based on alternative splicing patterns.

Marla Johnson, Elizabeth Purdom

    Biostatistics (Oxford, England)
    |October 27, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for clustering messenger RNA sequencing (mRNA-Seq) data based on isoform usage. The approach enhances the detection of sample clusters distinguished by specific isoform patterns, outperforming standard techniques.

    Keywords:
    Alternative splicingClusteringmRNA-Seq

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Messenger RNA (mRNA) sequencing provides isoform-level expression data.
    • Standard statistical methods for gene expression analysis do not fully leverage isoform information.
    • Clustering samples based on isoform usage patterns is an underexplored area.

    Purpose of the Study:

    • To develop a novel statistical approach for clustering mRNA sequencing data based on isoform usage.
    • To identify sample clusters distinguished by isoform usage rather than overall gene expression.
    • To improve the sensitivity of cluster detection using isoform-specific information.

    Main Methods:

    • Proposed a novel clustering algorithm tailored for mRNA sequencing data.
    • Utilized isoform usage profiles as the primary features for clustering.
    • Validated the method through simulations and analysis of The Cancer Genome Atlas (TCGA) datasets.

    Main Results:

    • The proposed method demonstrated higher sensitivity in detecting clusters based on isoform usage compared to standard clustering techniques.
    • Successfully identified a technical artifact related to batch effects that influenced isoform usage patterns.
    • Applied the method to TCGA datasets, showcasing its practical utility in cancer genomics.

    Conclusions:

    • The novel clustering approach effectively identifies sample groups based on distinct isoform usage patterns.
    • This method offers a sensitive tool for discovering biological or technical variations in transcriptomic data.
    • The approach has significant implications for analyzing complex datasets like those from TCGA.