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Comparing Copy Number Variations and SNPs

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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Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

A method for generating new datasets based on copy number for cancer analysis.

Shinuk Kim¹, Mark Kon², Hyunsik Kang³

¹College of Liberal Arts, Sangmyung University, Cheonan, Chungnam 330-720, Republic of Korea.

Biomed Research International

|May 8, 2015

Summary

New software enables analysis of copy number variation (CNV) data for cancer research. This approach enhances prediction of patient survival and identifies gene deletions in ovarian cancer and glioblastoma multiforme.

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Detection of Copy Number Alterations Using Single Cell Sequencing

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Published on: February 17, 2017

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

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Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Gene-expression markers are standard for cancer data analysis.
Copy number variation (CNV) datasets offer a new, rich source of genomic information.
CNV data analysis is less developed than gene-expression data analysis.

Purpose of the Study:

To develop a software package for preprocessing Agilent copy number data.
To adapt existing gene-expression analysis tools for CNV data.
To extract predictive information from CNV data for cancer studies.

Main Methods:

Developed a software package to preprocess Agilent copy number datasets.
Applied standard expression analysis tools to preprocessed CNV data.
Utilized gene set enrichment analysis and phenotype prediction methods.

Main Results:

Demonstrated the utility of the software package for CNV data analysis.
Successfully predicted patient survival times for ovarian cancer and glioblastoma multiforme.
Identified gene- and pathway-level deletions in these cancer types.

Conclusions:

The developed software package effectively preprocesses CNV data for broad analytical applications.
CNV data analysis using expression tools can yield significant predictive insights.
This approach advances the understanding of genomic alterations in cancer.