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Related Concept Videos

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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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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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SNPlice: variants that modulate Intron retention from RNA-sequencing data.

Prakriti Mudvari1, Mercedeh Movassagh1, Kamran Kowsari1

  • 1McCormick Genomics and Proteomics Center, Department of Biochemistry and Molecular Medicine and Department of Pharmacology and Physiology, The George Washington University, Washington, DC 20037, USA and Department of Ophthalmology, Department of Neurology and Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, School of Medicine, Washington, DC 20057, USA McCormick Genomics and Proteomics Center, Department of Biochemistry and Molecular Medicine and Department of Pharmacology and Physiology, The George Washington University, Washington, DC 20037, USA and Department of Ophthalmology, Department of Neurology and Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, School of Medicine, Washington, DC 20057, USA.

Bioinformatics (Oxford, England)
|December 7, 2014
PubMed
Summary

SNPlice is a new computational tool that identifies splice-modulating variants from RNA-sequencing data. This approach enhances the analysis of whole-transcriptome splice profiles and detects splicing elements missed by other algorithms.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • The importance of RNA splicing is increasingly recognized.
  • Vast amounts of RNA-sequencing data necessitate efficient analysis tools.
  • Analyzing whole-transcriptome splice profiles is crucial for understanding gene expression.

Purpose of the Study:

  • To develop a high-throughput computational approach for analyzing RNA-sequencing data.
  • To identify cis-acting, splice-modulating variants.
  • To assess the splice-modulating potential of single-nucleotide variants (SNVs).

Main Methods:

  • Developed SNPlice, a computational tool for RNA-seq data analysis.
  • Mined RNA-seq datasets to find reads spanning SNV loci and splice junctions.
  • Assessed co-occurrence of variants and unspliced molecules at exon-intron boundaries.

Main Results:

  • SNPlice identifies splice-modulating variants by assessing co-occurrence.
  • Results are consistent with splice-prediction tools but identify novel elements.
  • Demonstrated the ability to detect variants correlating with splicing events.

Conclusions:

  • SNPlice provides a robust method for identifying splice-modulating variants from RNA-seq data.
  • The tool can uncover splicing elements missed by existing algorithms.
  • SNPlice aids in understanding the impact of variants on RNA splicing.