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Related Concept Videos

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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.
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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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SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks.

Shaya Akbarinejad1, Mostafa Hadadian Nejad Yousefi1, Maziar Goudarzi2

  • 1Department of Computer Engineering, Sharif University of Technology, Azadi Ave., Tehran, Iran.

BMC Bioinformatics
|June 20, 2021
PubMed
Summary
This summary is machine-generated.

SVNN is a new pipeline that accurately detects structural variations in long-read sequencing data. This method is significantly faster and more sensitive, especially for low-coverage data, overcoming limitations of current approaches.

Keywords:
Long readsNeural networksPacBioStructural variation calling

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Long-read sequencing offers valuable structural variation (SV) information but faces precision challenges in low-coverage scenarios due to high error rates.
  • Current SV detection methods require high-coverage data, leading to time-intensive pipelines, particularly in the read alignment phase.

Purpose of the Study:

  • To develop a fast and precise structural variation calling pipeline for PacBio long-reads.
  • To improve SV detection sensitivity and efficiency in low-coverage sequencing data.

Main Methods:

  • The SVNN pipeline integrates state-of-the-art long-read aligners (NGMLR, Minimap2) and SV callers (Sniffle, SVIM).
  • A neural network is employed to identify informative reads from Minimap2 output for targeted, high-accuracy alignment with NGMLR.
  • This selective alignment strategy enhances sensitivity without compromising speed.

Main Results:

  • SVNN achieves up to a 20 percentage point improvement in sensitivity compared to existing state-of-the-art methods.
  • The pipeline is three times faster than naive combinations of tools while maintaining comparable accuracy.
  • SVNN effectively detects structural variants larger than 50 bp from raw PacBio reads.

Conclusions:

  • SVNN provides a significantly faster and more precise platform for PacBio long-read structural variation detection.
  • This advancement addresses the cost and time barriers associated with high-coverage data, enabling broader application of long-read sequencing.
  • The pipeline offers high precision and sensitivity in low-coverage scenarios, making it valuable for various genomic studies.