Related Experiment Video
Updated: Jul 10, 2026
09:34
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
F-SNP: computationally predicted functional SNPs for disease association studies
Phil Hyoun Lee1, Hagit Shatkay
1Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, ON, Canada. lee@cs.queensu.ca
Nucleic Acids Research
|November 8, 2007
Summary
The Functional Single Nucleotide Polymorphism (F-SNP) database aids researchers in identifying harmful genetic variations. It predicts the functional impact of SNPs on human health across multiple biological levels.
Area of Science:
- Bioinformatics
- Genomics
- Molecular Biology
Background:
- Single Nucleotide Polymorphisms (SNPs) are common genetic variations.
- Understanding the functional impact of SNPs is crucial for human health research.
- Existing tools often provide fragmented information on SNP effects.
Purpose of the Study:
- To create a comprehensive database integrating functional SNP information.
- To facilitate the identification of SNPs with potential deleterious effects on human health.
- To provide a user-friendly interface for exploring SNP data.
Main Methods:
- Integration of data from 16 bioinformatics tools and databases.
- Prediction of SNP effects at splicing, transcriptional, translational, and post-translational levels.
- Development of a web interface for multi-point data retrieval.
Main Results:
- The F-SNP database provides unified information on SNP functional effects.
- Users can identify SNPs disrupting functional genomic regions like splice sites.
- Non-synonymous SNPs affecting protein structure, function, or modification are identifiable.
Conclusions:
- The F-SNP database serves as a valuable resource for genetic variation analysis.
- It aids in prioritizing SNPs with potential health implications.
- The integrated approach enhances the study of SNP-associated diseases.
More Related Videos
Related Concept Videos
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...
01:05Single 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%...
01:29Pharmacogenomics: 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...