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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,...
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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%...
Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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...
Nonsense-mediated mRNA Decay02:27

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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information.

Jing Hu1, Changhui Yan

  • 1Department of Computer Science, Utah State University, Logan, UT 84322, USA. jinghu@cc.usu.edu

BMC Bioinformatics
|July 1, 2008
PubMed
Summary
This summary is machine-generated.

This study presents a new computational method to identify disease-causing single amino acid polymorphisms (SAPs) using only protein sequence information. The developed model achieves high accuracy, offering a valuable tool when protein structures are unavailable.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Non-synonymous single nucleotide polymorphisms (nsSNPs), or single amino acid polymorphisms (SAPs), are increasing rapidly.
  • Existing computational methods for distinguishing disease-causing SAPs often rely on protein structure, limiting their applicability.
  • There is a need for methods that can classify SAPs using only protein sequence data.

Purpose of the Study:

  • To explore the feasibility of classifying SAPs into disease-causing and neutral mutations using solely protein sequence information.
  • To develop and validate a computational method for SAP classification applicable even when protein structures are unavailable.

Main Methods:

  • Compiled 686 features derived from protein sequences.
  • Utilized a greedy approach to select 10 informative features for SAP classification.
  • Employed a decision tree algorithm for classification, trained and validated using selected features.

Main Results:

  • The decision tree method achieved 82.6% overall accuracy and 0.607 Matthews Correlation Coefficient (MCC) in cross-validation.
  • Independent testing on unseen data yielded 82.6% accuracy and 0.604 MCC.
  • Evaluation on Swiss-Prot SAPs resulted in 73.2% accuracy and 0.42 MCC, demonstrating reliable predictions without protein structures.

Conclusions:

  • The proposed method effectively classifies single amino acid polymorphisms (SAPs) using only protein sequence data.
  • This approach provides a valuable tool for SAP classification, particularly when protein structures are not accessible.