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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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%...
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Single Nucleotide Polymorphisms-SNPs01:05

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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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Genome Copying Errors02:46

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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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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Sanger Sequencing01:57

Sanger Sequencing

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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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Detection of Copy Number Alterations Using Single Cell Sequencing
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cnvOffSeq: detecting intergenic copy number variation using off-target exome sequencing data.

Evangelos Bellos1, Lachlan J M Coin2

  • 1Department of Genomics of Common Disease, Imperial College London, London W12 0NN, UK and Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD 4072, Australia.

Bioinformatics (Oxford, England)
|August 28, 2014
PubMed
Summary
This summary is machine-generated.

We developed cnvOffseq, a novel method using off-target exome sequencing data to detect intergenic copy number variation (CNV). This approach effectively mines discarded data for accurate genetic variant discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Genetic Variation

Background:

  • Exome sequencing efficiently detects variants in protein-coding regions but generates substantial, often discarded, off-target data.
  • Off-target data exhibit low and heterogeneous coverage, posing challenges for analysis.
  • Intergenic copy number variation (CNV) detection remains an underexplored area in exome sequencing analysis.

Purpose of the Study:

  • To introduce cnvOffseq, a novel normalization framework for analyzing off-target read depth.
  • To leverage off-target data for accurate detection and genotyping of intergenic CNVs.
  • To address the heterogeneity inherent in off-target sequencing data.

Main Methods:

  • Development of cnvOffseq, a normalization framework utilizing local adaptive singular value decomposition (SVD).
  • Application of the framework to analyze off-target read depth from whole-exome sequencing data.
  • Benchmarking against gold standard datasets, including intergenic deletions.

Main Results:

  • cnvOffseq achieved 57.5% sensitivity and 99.2% specificity for intergenic deletions.
  • For deletions >5 kb, sensitivity increased to 90.4% with a low false discovery rate (FDR).
  • cnvOffseq significantly outperforms existing whole-genome and whole-exome CNV detection methods.

Conclusions:

  • Off-target exome sequencing data is a valuable resource for intergenic CNV detection.
  • cnvOffseq provides an accurate and precise method for CNV analysis in previously overlooked regions.
  • The method offers a substantial improvement over standard approaches and naive SVD.