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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%...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Feb 20, 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

Lightning-fast genome variant detection with GROM.

Sean D Smith¹, Joseph K Kawash¹, Andrey Grigoriev¹

¹Department of Biology, Center for Computational and Integrative Biology, Rutgers University, 315 Penn St, Camden 08102, NJ, USA.

|October 20, 2017

Summary

Genome Rearrangement OmniMapper (GROM) is a new algorithm that detects multiple types of genetic variants, including single nucleotide variants (SNVs), indels, structural variants (SVs), and copy number variants (CNVs). GROM offers superior speed and accuracy for whole genome sequencing data analysis.

Keywords:

GROM SNVs copy number variants indels structural variants variant detection whole genome sequencing

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Published on: August 21, 2016

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Last Updated: Feb 20, 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

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Published on: August 21, 2016

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Whole genome sequencing (WGS) generates massive datasets, posing significant computational challenges.
Current variant detection methods often require multiple algorithms for different variant types (SNVs, indels, SVs, CNVs).
This fragmentation necessitates complex and time-consuming analysis pipelines.

Purpose of the Study:

To introduce Genome Rearrangement OmniMapper (GROM), a unified algorithm for comprehensive variant detection.
To demonstrate GROM's ability to detect SNVs, indels, SVs, and CNVs from aligned read files.
To evaluate GROM's performance against existing state-of-the-art methods.

Main Methods:

GROM accepts aligned read files as input for variant detection.
The algorithm was validated on 7 benchmarks using 2 WGS datasets.
Performance metrics including speed, sensitivity, and precision were assessed.

Main Results:

GROM outperforms existing methods on validated benchmarks.
GROM achieves rapid analysis times, processing a 50× WGS dataset in 11 minutes.
The algorithm demonstrates up to a 72-fold speed increase compared to specialized tools.
GROM successfully detects CNVs, SNVs, and indels in non-paired-read WGS libraries and SNVs/indels in exome/RNA-seq data.

Conclusions:

GROM provides a comprehensive, fast, and accurate solution for detecting multiple variant types in large-scale sequencing data.
The algorithm addresses big data challenges in genomics by integrating diverse variant detection capabilities.
GROM's efficiency and versatility make it a valuable tool for genomic research across various sequencing data types.