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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.
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.
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...

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Related Experiment Video

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

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Published on: February 17, 2017

VEGA: variational segmentation for copy number detection.

Sandro Morganella1, Luigi Cerulo, Giuseppe Viglietto

  • 1Department of Biological and Environmental Studies, University of Sannio, Benevento, Italy.

Bioinformatics (Oxford, England)
|October 21, 2010
PubMed
Summary

Variational Estimator for Genomic Aberrations (VEGA) accurately segments copy number data from array comparative genomic hybridization (aCGH). This bioinformatics tool demonstrates robustness against noise and effectively identifies chromosomal aberrations in real biological samples.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Genomic copy number (CN) analysis is crucial for understanding genetic disease traits.
  • Array comparative genomic hybridization (aCGH) enables high-throughput measurement of DNA copy numbers.
  • Developing efficient algorithms for detecting aberrant chromosomal regions is a key bioinformatics challenge.

Purpose of the Study:

  • To introduce Variational Estimator for Genomic Aberrations (VEGA), a novel algorithm for segmenting aCGH data.
  • To evaluate VEGA's performance against existing state-of-the-art algorithms.
  • To demonstrate VEGA's utility in identifying chromosomal aberrations in cancer cell lines.

Main Methods:

  • VEGA employs a variational model inspired by image segmentation techniques.
  • The algorithm minimizes an energy functional balancing data interpolation quality and solution complexity.
  • A data-driven region growing process is used to achieve optimal segmentation.

Main Results:

  • VEGA exhibits robustness to noise, as shown by performance on synthetic data.
  • The algorithm achieves high accuracy in terms of recall and precision.
  • VEGA successfully identifies known chromosomal aberrations in mantle cell lymphoma and glioblastoma multiforme samples.

Conclusions:

  • VEGA is an effective and accurate tool for copy number segmentation of aCGH data.
  • The algorithm's performance is comparable or superior to existing methods.
  • VEGA provides a valuable resource for genomic aberration detection in disease research.