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Next-generation Sequencing

Next-generation Sequencing

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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Updated: Jan 14, 2026

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

SVUPP: Pre-phasing long reads improves structural variant genotyping.

Zilong Li¹, Frederik Filip Stæger¹, Robert W Davies^2,3

¹Section for Computational and RNA Biology, University of Copenhagen, Copenhagen 2200, Denmark.

Bioinformatics (Oxford, England)

|October 24, 2025

Summary

This summary is machine-generated.

SVUPP enhances structural variant (SV) genotyping by integrating read phasing information, improving accuracy on long-read sequencing data. This method outperforms existing tools for SVs without nearby variants.

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Last Updated: Jan 14, 2026

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

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

Amplicon Sequencing using the Long-Read Sequencing Technologies

Amplicon Sequencing using the Long-Read Sequencing Technologies

Published on: August 29, 2025

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate genotyping of structural variants (SVs) is crucial for understanding genetic diversity and disease.
Existing SV callers face challenges with complex genomic regions and accurate genotype likelihood estimation.

Purpose of the Study:

To introduce SVUPP, a novel approach for improving structural variant genotyping.
To enhance the accuracy of SV genotyping by incorporating read phasing information.

Main Methods:

SVUPP integrates per-read phasing information into genotype likelihood calculations.
The approach was benchmarked using long-read (Oxford Nanopore Technologies and Pacific Biosciences HiFi) sequencing data.
SVUPP was evaluated against established SV callers including cuteSV2, Sniffles2, and kanpig.

Main Results:

SVUPP demonstrated superior accuracy in genotyping structural variants compared to existing methods.
The improvement was particularly notable for structural variants located without close neighboring variants.
SVUPP showed high performance across both long and ultra-long Oxford Nanopore Technologies data, as well as PacBio HiFi data.

Conclusions:

SVUPP offers a significant advancement in structural variant genotyping accuracy.
The method effectively leverages read phasing information to improve genotype likelihoods.
SVUPP is compatible with existing SV callers and can utilize phasing information from various methods.