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Related Concept Videos

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.

RNA-seq

RNA-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...

Sanger Sequencing

Sanger 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...

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

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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Related Experiment Video

Updated: Jun 8, 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

Massively parallel sequencing and rare disease.

Sarah B Ng¹, Deborah A Nickerson, Michael J Bamshad

¹Department of Genome Sciences, University of Washington School of Medicine, Seattle WA 98195, USA. sarahng@uw.edu

Human Molecular Genetics

|September 18, 2010

Summary

This summary is machine-generated.

Massively parallel sequencing accelerates gene discovery and disease diagnosis for rare disorders. New strategies filter variants by frequency, function, and predicted impact to identify causal mutations.

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Last Updated: Jun 8, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Area of Science:

Genomics and Molecular Biology
Medical Genetics

Background:

Massively parallel sequencing (MPS) enables large-scale variant identification, advancing gene discovery and molecular diagnostics.
MPS is revolutionizing the analysis of Mendelian diseases, allowing variant interrogation with or without linkage data.
A key challenge is differentiating benign polymorphisms from pathogenic mutations.

Purpose of the Study:

To review the literature on high-throughput sequencing data analysis for causal mutation discovery in rare disorders.
To highlight strategies for identifying pathogenic variants from sequencing data.

Main Methods:

Review of recent literature on high-throughput sequencing data analysis.
Discussion of variant filtering strategies based on frequency and function.
Examination of variant ranking based on conservation scores and predicted protein structure impact.

Main Results:

Recent strategies for rare monogenic disorders show early success in identifying causal mutations.
Filtering variants by frequency and function aids in distinguishing pathogenic mutations.
Ranking variants by conservation and predicted deleteriousness improves causal variant identification.

Conclusions:

High-throughput sequencing data analysis is crucial for discovering causal mutations in rare genetic disorders.
Filtering and ranking strategies are effective in identifying disease-causing variants.
Continued development in these analytical methods holds significant potential for molecular diagnostics.