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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.
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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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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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PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

Chen Wang1, Jared M Evans1, Aditya V Bhagwate1

  • 1Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Division of Epidemiology, Department of Health Sciences Research, Division of Hematology, Department of Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905 and Department of Health Sciences Research, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL 32224, USA.

Bioinformatics (Oxford, England)
|May 31, 2014
PubMed
Summary
This summary is machine-generated.

PatternCNV is a novel algorithm for detecting DNA copy number variations (CNVs) from exome sequencing data. It offers higher sensitivity and specificity than existing methods, with built-in quality control and no need for paired references.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Exome sequencing (exome-seq) data are increasingly used for DNA copy number variation (CNV) detection.
  • Existing CNV detection tools often lack sensitivity, accuracy, or require paired references.
  • There is a need for robust CNV-calling algorithms with integrated quality control.

Purpose of the Study:

  • To develop a novel, sensitive, and accurate algorithm for CNV detection from exome-seq data.
  • To create a CNV-calling tool that incorporates quality control measures and does not require paired references.
  • To improve the efficiency and accuracy of CNV detection in genomic studies.

Main Methods:

  • Developed PatternCNV, a novel method leveraging inter-sample read coverage consistencies.
  • Reduced computational time by converting alignment BAM files to WIG format.
  • Integrated multiple quality control (QC) measures to identify outlier samples and batch effects.
  • Provided comprehensive visualization options from chromosome to exon levels.

Main Results:

  • PatternCNV demonstrated higher sensitivity and specificity compared to existing CNV-calling algorithms in a lymphoma exome-seq study.
  • The algorithm effectively accounts for inter-exon read coverage variations.
  • Incorporated QC measures successfully identified outlier samples and batch effects.
  • Offered detailed visualization and summarization of CNVs at various genomic levels.

Conclusions:

  • PatternCNV is a sensitive and accurate tool for CNV detection from exome-seq data.
  • The algorithm's design addresses limitations of existing CNV callers, including QC and reference requirements.
  • PatternCNV offers a valuable resource for genomic research requiring precise CNV identification.