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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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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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xGAP: a python based efficient, modular, extensible and fault tolerant genomic analysis pipeline for variant

Aditya Gorla1, Brandon Jew2, Luke Zhang3

  • 1Department of Bioengineering, University of California, Los Angeles, CA 90095, USA.

Bioinformatics (Oxford, England)
|January 8, 2021
PubMed
Summary
This summary is machine-generated.

xGAP is a new automated pipeline for analyzing next-generation sequencing (NGS) data. It efficiently calls genetic variants from whole-genome sequencing (WGS) data, offering speed and accuracy for disease research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) generates vast amounts of data crucial for identifying genetic variants linked to diseases.
  • Current bioinformatic pipelines for variant calling are often not fully automated, efficient, or scalable.
  • A need exists for a robust, user-friendly pipeline for comprehensive DNA-seq analysis.

Purpose of the Study:

  • Introduce xGAP, an extensible Genome Analysis Pipeline.
  • Provide an automated, efficient, rapid, scalable, modular, user-friendly, and fault-tolerant solution for NGS data analysis.
  • Implement modified GATK best practices for DNA-seq variant calling.

Main Methods:

  • xGAP utilizes massive parallelization by dividing the genome into smaller regions with load balancing.
  • The pipeline is built upon modified GATK best practices for variant calling.
  • It is designed for high-performance computing clusters and cloud environments.

Main Results:

  • xGAP processes 30x whole-genome sequencing (WGS) data in approximately 90 minutes.
  • Achieved high accuracy with average F1 scores of 99.37% for single nucleotide variants and 99.20% for insertions/deletions.
  • Demonstrated 20% faster analysis compared to the Churchill pipeline on AWS and consistent results across clusters.

Conclusions:

  • xGAP offers a scalable and efficient solution for genetic variant calling from NGS data.
  • The pipeline's user-friendly and fault-tolerant design minimizes intervention.
  • xGAP facilitates rapid and accurate genetic analysis for research and clinical applications.