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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 Splicing

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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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General Transcription Factors01:30

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Non-LTR Retrotransposons03:18

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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
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Chromatin Structure Regulates pre-mRNA Processing02:41

Chromatin Structure Regulates pre-mRNA Processing

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In eukaryotic cells, nascent mRNA transcripts need to undergo many post-transcriptional modifications to reach the cell cytoplasm and translate into functional proteins. For a long time, transcription and pre-mRNA processing were considered two independent events that occur sequentially in the cell. However, it has now been well established that transcription and pre-mRNA processing are two simultaneous processes that are precisely regulated inside the cell.
The chromatin structure, especially...
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Translation01:31

Translation

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Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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Detection of Alternative Splicing During Epithelial-Mesenchymal Transition
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SpliceTransformer predicts tissue-specific splicing linked to human diseases.

Ningyuan You1, Chang Liu1, Yuxin Gu2

  • 1Department of Obstetrics and Gynecology of Sir Run Run Shaw Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, China.

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|October 23, 2024
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SpliceTransformer (SpTransformer), a deep learning tool, accurately predicts RNA splicing changes linked to diseases. This framework reveals splicing alterations in 60% of mutations, offering insights into tissue-specific disease mechanisms.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • RNA splicing alterations are implicated in various human diseases.
  • Predicting tissue-specific splicing events from genomic sequence remains a challenge.
  • Existing methods lack accuracy in identifying disease-associated splicing changes.

Purpose of the Study:

  • To develop a deep-learning framework, SpliceTransformer (SpTransformer), for predicting tissue-specific RNA splicing alterations.
  • To assess the contribution of splicing alterations to pathogenic mutations across different tissues.
  • To investigate the relationship between splicing alterations, clinical manifestations, and gene expression variation.

Main Methods:

  • Developed SpTransformer, a deep-learning framework utilizing genomic sequence data.
  • Applied SpTransformer to analyze ~1.3 million genetic variants in the ClinVar database.
  • Validated SpTransformer on brain disease datasets and whole exome sequencing data from diabetic nephropathy patients.

Main Results:

  • SpTransformer demonstrated superior performance in splicing prediction compared to previous methods.
  • Splicing alterations account for 60% of intronic and synonymous pathogenic mutations.
  • Tissue-specific splicing alterations correlate with clinical manifestations, independent of gene expression.
  • Identified single nucleotide variations causing brain-specific splicing alterations.
  • Achieved 83% accuracy in predicting kidney-specific splicing alterations in diabetic nephropathy.

Conclusions:

  • SpTransformer is a powerful tool for predicting tissue-specific RNA splicing alterations and their link to human diseases.
  • Splicing alterations are a significant contributor to pathogenic mutations, with tissue-specific patterns.
  • The framework has potential for clinical applications in diagnosing and understanding genetic diseases.