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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: Sep 6, 2025

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

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RDscan: A New Method for Improving Germline and Somatic Variant Calling Based on Read Depth Distribution.

Sunho Lee^1,2, Seokchol Hong¹, Jonathan Woo¹

¹Genome Data Integration Centre, Syntekabio, Inc., Daejeon, Republic of Korea.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|June 24, 2022

Summary

RDscan is a new method that improves the accuracy of germline and somatic variant calling in next-generation sequencing (NGS) data. It enhances variant caller precision by removing false positives, especially in low read depth datasets.

Keywords:

germline variant next-generation sequencing read depth distribution somatic variant variant calling variant filtering

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Published on: March 22, 2018

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Next-generation sequencing (NGS) variant calling tools face challenges with low read depth data.
Concordance rates for somatic variants predicted by current callers are often low.

Purpose of the Study:

To develop a novel method, RDscan, for enhancing germline and somatic variant calling accuracy in NGS data.
To improve the precision of variant callers by reducing false-positive calls.

Main Methods:

RDscan preprocesses NGS data by removing misaligned reads and repositioning reads.
It calculates an RDscore based on read depth distribution to identify and filter false positives.

Main Results:

RDscan improved accuracy for most tested variant calling algorithms on public datasets.
Calling accuracy increased for 11 of 12 germline variant cases and 21 of 24 somatic variant cases after RDscan screening.
RDscan demonstrated effectiveness in removing false-positive variants with a low computational load.

Conclusions:

RDscan is a user-friendly tool that effectively enhances variant calling precision in NGS data.
The method shows significant improvements in both germline and somatic variant detection.
RDscan is expected to contribute to advancements in genome analysis when used with existing variant callers.