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Related Concept Videos

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,...
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%...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...

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Related Experiment Video

Updated: Jun 13, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Mining SNPs from EST sequences using filters and ensemble classifiers.

J Wang1, Q Zou, M Z Guo

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China.

Genetics and Molecular Research : GMR
|May 8, 2010
PubMed
Summary
This summary is machine-generated.

SNPDigger automates single nucleotide polymorphism (SNP) discovery from expressed sequence tag (EST) databases. This computational method improves accuracy and efficiency for genetic research, even without genome data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are crucial for genome-wide association studies.
  • Manual SNP discovery is a bottleneck, hindering efficient genetic research.

Purpose of the Study:

  • To develop an automated, efficient, and accurate computational method for SNP detection.
  • To enable SNP discovery from expressed sequence tag (EST) databases.

Main Methods:

  • Clustering and aligning EST sequences.
  • Identifying SNP candidates using redundant frequency measures.
  • Utilizing novel biological features (e.g., structural neighbor profiles, physical position).
  • Employing an ensemble classifier for imbalanced training data.

Main Results:

  • Achieved sensitivity and specificity exceeding 80% for human genetic data.
  • Demonstrated effectiveness of extracted biological features.
  • Successfully validated the SNPDigger method on cross-validation datasets.

Conclusions:

  • SNPDigger provides an effective computational approach for SNP discovery in ESTs.
  • The method is applicable to species with or without available genome data.
  • SNPDigger can reduce costs and improve efficiency in biological analyses.