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Related Concept Videos

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%...
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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,...
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...
Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.

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Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Simple binary segmentation frameworks for identifying variation in DNA copy number.

Tae Young Yang1

  • 1Department of Mathematics, Myongji University, Yongin, Kyonggi, 449-728, Korea. tyang@mju.ac.kr

BMC Bioinformatics
|October 31, 2012
PubMed
Summary
This summary is machine-generated.

We developed a flexible and simple circular binary segmentation procedure to identify DNA copy number variations. This method aids in pinpointing amplified or deleted regions for both individual genome analysis and cancer cohort studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • DNA copy number variation (CNV) involves gains or losses of chromosome segments.
  • Identifying amplified or deleted regions is crucial for analyzing CNV data.

Purpose of the Study:

  • To introduce a novel circular binary segmentation procedure for DNA copy number analysis.
  • To provide frameworks for analyzing data from multiple sources and for cohort analysis in cancer.

Main Methods:

  • The procedure utilizes a sequence of nested hypothesis tests.
  • The Bayesian information criterion is employed within the hypothesis testing framework.
  • Statistical frameworks are provided for integrating multiple data sources and standardizing cohort data.

Main Results:

  • The method enables higher-resolution genome-wide surveys by integrating data from diverse sources.
  • It facilitates the derivation of consensus molecular signatures from multiple data inputs.
  • Cohort analysis allows for the identification of common aberrant regions across patients, potentially indicating roles in cancer pathogenesis.

Conclusions:

  • The proposed circular binary segmentation procedure offers flexibility and simplicity.
  • It is applicable to both single-individual multi-source data analysis and multi-patient cohort studies.