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Related Concept Videos

RNA Splicing01:32

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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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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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Point and Frameshift Mutations01:30

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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
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PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing.

Ryo Kurosawa1, Kei Iida2,3, Masahiko Ajiro4,5

  • 1Department of Anatomy and Developmental Biology, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan. kurosawa.ryo.43r@st.kyoto-u.ac.jp.

BMC Genomics
|October 10, 2023
PubMed
Summary
This summary is machine-generated.

We developed PDIVAS, a tool to identify disease-causing deep-intronic variants affecting RNA splicing. This predictor efficiently detects pathogenic splice-altering variants (SAVs), improving genetic disease diagnosis.

Keywords:
Deep intronGenomicsMachine learningNon-coding regionPathogenicity predictionRNA splicingVariant interpretation

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Genetics

Background:

  • Deep-intronic variants are challenging to evaluate for genetic disease causation.
  • Identifying pathogenic variants among millions of deep-intronic variants poses a significant technical hurdle for researchers.

Purpose of the Study:

  • To develop a computational tool, PDIVAS, for efficient detection of pathogenic deep-intronic variants.
  • To address the challenge of evaluating deep-intronic variants in genetic disease diagnostics.

Main Methods:

  • Developed PDIVAS using an ensemble machine-learning algorithm trained on curated pathogenic and benign splice-altering variants (SAVs).
  • Utilized splicing features and a splicing constraint metric to optimize predictive performance.
  • Evaluated PDIVAS performance against previous predictors for variant classification and prioritization.

Main Results:

  • PDIVAS achieved high accuracy, with an average precision of 0.92 and a maximum Matthews Correlation Coefficient (MCC) of 0.88, outperforming existing methods.
  • Application of PDIVAS to genome sequencing identified an average of 27 pathogenic candidates per individual at 95% sensitivity for known pathogenic SAVs.
  • PDIVAS demonstrated more efficient prioritization of causative variants in simulated patient genomes compared to prior predictors.

Conclusions:

  • PDIVAS facilitates the efficient detection of disease-causing deep-intronic SAVs, enhancing diagnostic yield in genetic disease research.
  • Integration of PDIVAS into variant interpretation pipelines is recommended for improved diagnostic capabilities.
  • The PDIVAS tool is publicly accessible for research use.