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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%...
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.
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,...
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
Fixing Double-strand Breaks02:04

Fixing Double-strand Breaks

The double-stranded structure of DNA has two major advantages. First, it serves as a safe repository of genetic information where one strand serves as the back-up in case the other strand is damaged. Second, the double-helical structure can be wrapped around proteins called histones to form nucleosomes, which can then be tightly wound to form chromosomes. This way, DNA chains up to 2 inches long can be contained within microscopic structures in a cell. A double-stranded break not only damages...

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Related Experiment Video

Updated: May 11, 2026

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

Learning smoothing models of copy number profiles using breakpoint annotations.

Toby Dylan Hocking1, Gudrun Schleiermacher, Isabelle Janoueix-Lerosey

  • 1INRIA Sierra Project-Team, Paris F-75013, France. toby.hocking@inria.fr

BMC Bioinformatics
|May 24, 2013
PubMed
Summary
This summary is machine-generated.

Selecting the best copy number alteration detection model is challenging. This study introduces an annotation-guided approach to quantitatively compare 17 smoothing models, identifying pelt.n and cghseg.k as top performers on neuroblastoma data.

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Related Experiment Videos

Last Updated: May 11, 2026

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

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Numerous models exist for detecting copy number alterations (CNAs) in chromosomal profiles.
  • Model selection and smoothing parameter optimization often rely on heuristics, lacking clear effectiveness guidelines.

Purpose of the Study:

  • To develop a quantitative framework for selecting optimal copy number profile smoothing models.
  • To compare the performance of various smoothing algorithms using real-world data.
  • To establish a public benchmark dataset for CNA detection algorithm evaluation.

Main Methods:

  • Proposing a model selection strategy based on maximizing agreement with visual breakpoint annotations.
  • Developing cross-validation techniques to estimate the error rates of trained CNA detection models.
  • Benchmarking 17 smoothing models on a newly curated database of 575 annotated neuroblastoma copy number profiles.

Main Results:

  • The study quantitatively compares 17 smoothing models for copy number profile analysis.
  • The 'pelt.n' and 'cghseg.k' methods demonstrated superior performance as breakpoint detectors.
  • These leading methods also exhibited efficient computation times on the tested dataset.

Conclusions:

  • The developed annotation-guided approach provides a quantitative assessment of CNA detection algorithms on real data, unlike previous qualitative or simulation-based studies.
  • The 'pelt.n' and 'cghseg.k' algorithms are identified as the most accurate and efficient methods for breakpoint detection in neuroblastoma copy number profiles.
  • The public benchmark dataset facilitates future research and validation of novel CNA detection algorithms.