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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

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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Updated: Aug 12, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

HQAlign: Aligning nanopore reads for SV detection using current-level modeling.

Dhaivat Joshi¹, Suhas Diggavi, Mark J P Chaisson²

¹University of California, Los Angeles.

Biorxiv : the Preprint Server for Biology

|January 30, 2023

Summary

This summary is machine-generated.

HQAlign improves structural variant (SV) detection from nanopore sequencing data by enhancing alignment accuracy. This new tool captures missed SVs and refines breakpoint accuracy, outperforming existing methods for long-read alignment.

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Last Updated: Aug 12, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate detection of structural variants (SVs) is crucial for understanding human diseases.
Long reads from nanopore sequencing aid SV identification but present alignment challenges due to high error rates.
Leveraging nanopore error characteristics is key to developing robust SV detection aligners.

Approach:

HQAlign is a novel aligner designed for SV detection using nanopore sequencing reads.
It utilizes basecalled nanopore reads and sequencing physics to enhance SV alignment.
The approach incorporates SV-specific modifications into the minimap2 alignment pipeline.

Key Points:

HQAlign identifies 4-6% more SVs missed by minimap2, with comparable standalone performance.
It improves breakpoint accuracy by 10-50% for common SV calls.
Alignment rates are boosted for nanopore reads against CHM13 and GRCh37 genomes.

Conclusions:

HQAlign offers improved SV detection sensitivity and accuracy for nanopore data.
The tool enhances alignment rates, contributing to more comprehensive genomic analyses.
HQAlign represents a significant advancement in SV detection for long-read sequencing.