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Related Concept Videos

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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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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Since the discovery of the two BER pathways, there has been a debate about how a cell chooses one pathway over the other and the factors determining this selection. Numerous in vitro experiments have pointed out multiple determinants for the sub-pathway selection. These are:
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Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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Deep Splicer: A CNN Model for Splice Site Prediction in Genetic Sequences.

Elisa Fernandez-Castillo1, Liliana Ibeth Barbosa-Santillán1, Luis Falcon-Morales1

  • 1School of Engineering and Sciences, Monterrey Institute of Technology and Higher Education, Guadalajara 45201, Mexico.

Genes
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

Deep Splicer, a new deep learning model, accurately identifies splice sites in DNA across various species. It offers improved performance and a lower false-positive rate compared to existing computational tools.

Keywords:
CNNdeep learning modelsgenetic sequencessplice sites

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • DNA contains genes, which are sequences for protein synthesis, including coding exons and non-coding introns.
  • Splice sites are crucial for removing introns during RNA transcription, a key step in gene expression.
  • Current biochemical methods for splice site detection are time-consuming and expensive, necessitating computational approaches.

Purpose of the Study:

  • To develop an advanced deep learning model, Deep Splicer, for accurate splice site identification in genomic sequences.
  • To improve upon the accuracy and reduce false-positive rates of existing computational splice site prediction tools.

Main Methods:

  • Development of Deep Splicer, a deep learning model utilizing genomic sequence data.
  • Evaluation of Deep Splicer's performance against existing tools like Splice2Deep and Splice Finder.
  • Comparative analysis of accuracy and false-positive rates across various species, including humans.

Main Results:

  • Deep Splicer demonstrated high accuracy, ranging from 93.55% to 99.66% across different organisms.
  • The model achieved significantly lower false-positive rates (0.11% for human, 0.25% for other species) compared to Splice Finder (1-10%).
  • Deep Splicer outperformed Splice2Deep in accuracy for human and fruit fly sequences but was slightly surpassed in worm and thale cress.

Conclusions:

  • Deep Splicer represents a significant advancement in computational splice site detection.
  • The model's high accuracy and low false-positive rate make it a valuable tool for genome annotation.
  • Deep Splicer has the potential to accelerate genomic research by improving the efficiency of gene structure analysis.