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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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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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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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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Updated: Jul 29, 2025

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
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SVcnn: an accurate deep learning-based method for detecting structural variation based on long-read data.

Yan Zheng1, Xuequn Shang2

  • 1School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China. yan.zheng@nwpu-bioinformatics.com.

BMC Bioinformatics
|May 23, 2023
PubMed
Summary
This summary is machine-generated.

SVcnn, a deep learning method, enhances structural variation (SV) detection using long-read sequencing. It improves accuracy, particularly for complex multi-allelic SVs, addressing limitations of current SV callers.

Keywords:
Deep learningLong-read sequencing dataSV callerStructural variations

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Structural variations (SVs) are crucial in genetic diseases and evolution.
  • Long-read sequencing has advanced SV detection, but current methods have suboptimal performance.
  • High error rates in long-read data lead to missed true SVs and false positives, especially in repetitive and multi-allelic regions.

Purpose of the Study:

  • To develop a more accurate method for detecting structural variations using long-read sequencing data.
  • To address the limitations of existing SV callers in handling complex genomic regions.

Main Methods:

  • Developed SVcnn, a deep learning-based method for SV detection.
  • Utilized long-read sequencing data for SV analysis.
  • Compared SVcnn performance against existing SV callers on three real datasets.

Main Results:

  • SVcnn demonstrated improved accuracy, increasing the F1-score by 2-8% compared to the second-best method at read depths >5×.
  • SVcnn exhibited superior performance in detecting multi-allelic structural variations.
  • The deep learning approach effectively handles challenges posed by long-read sequencing data.

Conclusions:

  • SVcnn is an accurate deep learning-based method for structural variation detection.
  • The SVcnn program is publicly available for use in genomic research.
  • This method offers a significant advancement in SV calling from long-read sequencing data.