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Related Concept Videos

Next-generation Sequencing03:00

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.
Comparing Copy Number Variations and SNPs02:26

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%...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Single Nucleotide Polymorphisms-SNPs01:05

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-GWAS01:11

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...
Sanger Sequencing01:57

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...

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Updated: May 9, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

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Published on: April 4, 2018

Computational SNP analysis: current approaches and future prospects.

Ambuj Kumar1, Vidya Rajendran, Rao Sethumadhavan

  • 1Bioinformatics Division, School of Bio Sciences and Technology, Vellore Institute of Technology University, Vellore, 632014, Tamil Nadu, India.

Cell Biochemistry and Biophysics
|July 16, 2013
PubMed
Summary
This summary is machine-generated.

Computational tools rapidly detect disease-associated non-synonymous single nucleotide polymorphisms (nsSNPs). However, accurately linking these nsSNPs to genotype-phenotype associations remains a challenge, requiring further computational refinement.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Computational methods for identifying disease-associated non-synonymous single nucleotide polymorphisms (nsSNPs) have advanced rapidly.
  • Numerous tools exist for detecting deleterious nsSNPs with high reliability.

Purpose of the Study:

  • To address the limitations in accurately determining genotype-phenotype associations for predicted nsSNPs.
  • To highlight outstanding computational questions crucial for understanding nsSNP pathogenicity.

Main Methods:

  • Review of current computational nsSNP characterization techniques.
  • Integration of molecular dynamics simulations to enhance nsSNP analysis accuracy.
  • Assessment of protein phenotype changes linked to computationally predicted disease-associated mutations.

Main Results:

  • Despite high prediction reliability of nsSNP detection tools, genotype-phenotype association accuracy is still lacking.
  • Molecular dynamics simulations were incorporated to improve the accuracy of computational nsSNP analysis.
  • The study identified key questions for a comprehensive understanding of disease-associated mutation pathogenicity.

Conclusions:

  • Further computational development is needed to accurately link nsSNPs to genotype-phenotype associations.
  • Molecular dynamics simulations offer a promising approach to enhance the accuracy of nsSNP pathogenicity prediction.
  • Addressing specific computational challenges is essential for a complete understanding of disease-associated mutations.