Related Experiment Video
Updated: Jun 16, 2026
14:06
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
Published on: June 23, 2012
Accurate detection and genotyping of SNPs utilizing population sequencing data
Vikas Bansal1, Olivier Harismendy, Ryan Tewhey
1Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA. vbansal@scripps.edu
Genome Research
|February 13, 2010
Summary
SNIP-Seq is a novel method for identifying single nucleotide polymorphisms (SNPs) and genotypes from population sequencing data. This approach significantly reduces false positives, improving variant detection accuracy in genomic intervals.
Area of Science:
- Genomics
- Bioinformatics
- Human Genetics
Background:
- Next-generation sequencing enables deep sequencing of targeted human genome regions.
- Existing single nucleotide polymorphism (SNP) detection methods primarily use single individual data.
- Accurate SNP and genotype identification is crucial for understanding human genetic variation.
Purpose of the Study:
- To introduce SNIP-Seq, a novel method for SNP and genotype identification using population sequencing data.
- To evaluate the accuracy and performance of SNIP-Seq compared to existing methods.
- To demonstrate the utility of analyzing population sequencing data for variant discovery.
Main Methods:
- Developed SNIP-Seq, a method leveraging population sequence data for SNP detection and genotype assignment.
- Applied SNIP-Seq to sequence data from a 200-kb region on human chromosome 9p21 across 48 individuals.
- Validated SNIP-Seq findings using pooled sequencing and Sanger sequencing.
Main Results:
- SNIP-Seq demonstrated high accuracy in detecting variants and filtering sequencing errors.
- Concordance of genotype assignments between duplicate samples reached 98.8%.
- SNIP-Seq achieved an estimated false-positive rate of approximately 2%, outperforming existing methods.
Conclusions:
- SNIP-Seq is an accurate and efficient method for SNP and genotype identification from population sequencing data.
- Analyzing population sequencing data offers a powerful approach for robust variant detection.
- The SNIP-Seq method improves the reliability of genetic variant discovery in large-scale studies.
Related Concept Videos
01:05Single 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,...
02:26Comparing 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%...
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%...
01:11Genome-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...
GWAS does not require the identification of the target gene involved in...
01:57Sanger Sequencing
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...
03:00Next-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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
03:21RNA-seq
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...