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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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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Rseg--an R package to optimize segmentation of SNP array data.

Philippe Lamy1, Carsten Wiuf, Torben F Ørntoft

  • 1Department of Molecular Medicine, Aarhus University Hospital Skejby, Aarhus N, Denmark. plamy@cs.au.dk

Bioinformatics (Oxford, England)
|December 8, 2010
PubMed
Summary

This study introduces Rseg, an R package for analyzing copy number alterations in tumor samples. Rseg offers flexible thresholding for copy number gains and losses, addressing challenges from tumor heterogeneity and normalization artifacts.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • High-density SNP arrays are crucial for detecting copy number alterations (CNAs) in clinical tumor samples.
  • Tumor heterogeneity and normal cell contamination complicate CNA analysis using standard segmentation algorithms.
  • Normalization artifacts can distort copy-number ratios, further challenging accurate genomic profiling.

Purpose of the Study:

  • To develop a flexible computational tool for analyzing CNAs in challenging clinical tumor samples.
  • To provide a solution for accurate identification of copy number gains and losses despite sample complexities.
  • To enable correction for normalization-induced artifacts in copy-number ratio data.

Main Methods:

  • Development of an open-source R package named Rseg.
  • Implementation of user-defined, sample-specific thresholds for calling copy number gains and losses.
  • Inclusion of functionality to correct for normalization artifacts.

Main Results:

  • The R package Rseg provides a flexible approach to calling copy number alterations.
  • Users can define custom thresholds, enhancing accuracy in the presence of tumor heterogeneity and normal cell contamination.
  • Rseg effectively corrects for artifacts introduced during sample normalization.

Conclusions:

  • Rseg offers a robust and adaptable solution for copy number alteration analysis in clinical tumor genomics.
  • The package facilitates more reliable identification of genomic changes in complex tumor samples.
  • Rseg is available as open-source software for Linux and MS-Windows.