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Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
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A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer.
Chen Chi1, Rasif Ajwad2, Qin Kuang3
1Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Canada; Centre for Healthcare Innovation, Winnipeg Regional Health Authority/University of Manitoba, Winnipeg, Canada.
Cancer Informatics
|October 25, 2016
Summary
This study introduces a novel graph-based algorithm to identify common copy number variations (CNVs) across multiple tumor samples. The method efficiently pinpoints recurrent CNV regions, aiding in the discovery of cancer-associated genes.
Area of Science:
- Genomics
- Cancer Research
- Bioinformatics
Background:
- Copy number variations (CNVs) in genomic DNA are implicated in numerous cancers.
- Identifying recurrent CNVs across multiple tumor samples is crucial for discovering cancer-associated genes but presents computational and conceptual challenges.
Purpose of the Study:
- To develop and present a novel graph-based algorithm for the efficient identification of recurrent CNV regions.
- To address the computational and conceptual challenges in integrating multiple samples for CNV analysis.
Main Methods:
- A new graph-based algorithm utilizing maximal clique detection to identify recurrent CNVs.
- The algorithm guarantees optimal solutions, identifying all maximal cliques and ensuring the most frequent and minimally delineated CNV regions.
Main Results:
- The algorithm was successfully applied to a large cohort of breast cancer samples.
- The analysis identified several genes and pathways associated with breast cancer.
Conclusions:
- The proposed graph-based algorithm is an effective tool for identifying recurrent CNVs in cancer genomics.
- This method facilitates the discovery of novel cancer-associated genes and pathways by analyzing common CNV regions across tumor samples.
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