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Amplicon Sequencing using the Long-Read Sequencing Technologies
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NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data.

Li Fang1,2,3, Jiang Hu1, Depeng Wang1

  • 1Grandomics Biosciences, Beijing, 102206, China.

BMC Bioinformatics
|May 25, 2018
PubMed
Summary

NextSV, a novel meta-caller, improves structural variant (SV) detection in low-coverage long-read sequencing data. Approximately 10X coverage offers an optimal balance for accurate SV identification, facilitating disease research.

Keywords:
Long-read sequencingLow coveragePacBioStructural variants

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Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetics

Background:

  • Structural variants (SVs) are crucial in human diseases.
  • Long-read sequencing offers improved SV detection over short-read sequencing.
  • Optimal coverage and tool usage for long-read SV detection remain unclear due to cost.

Purpose of the Study:

  • To develop NextSV, a meta-caller for SV detection from low-coverage long-read data.
  • To evaluate SV calling performance at various coverages using PacBio sequencing.
  • To provide guidelines for effective SV analysis in long-read sequencing.

Main Methods:

  • Developed NextSV, integrating three aligners and three SV callers.
  • Generated sensitive and stringent call sets for different analytical needs.
  • Assessed SV calling performance on NA12878 and HX1 genomes at varying PacBio coverages.

Main Results:

  • NextSV stringent set improved precision and F1 score; sensitive set enhanced recall.
  • At 10X coverage, NextSV sensitive set achieved 93.5-94.1% recall for deletions and 87.9-93.2% for insertions.
  • Mendelian errors were evaluated on an Ashkenazi Jewish trio dataset.

Conclusions:

  • NextSV offers effective SV detection from low-coverage long-read data.
  • Approximately 10X coverage is recommended for practical SV analysis balancing cost and recall.
  • NextSV is expected to aid structural variant analysis in long-read sequencing studies.