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Whole-genome CNV analysis: advances in computational approaches
Mehdi Pirooznia1, Fernando S Goes1, Peter P Zandi2
1Mood Disorders Center, Department of Psychiatry and Behavioral Sciences, School of Medicine, Johns Hopkins University Baltimore, MD, USA.
Frontiers in Genetics
|April 29, 2015
Summary
DNA copy number variation (CNV) influences human diversity and disease susceptibility. This review covers bioinformatics tools for detecting CNVs using whole genome sequencing, aiding researchers in selecting optimal methods.
Area of Science:
- Genomics
- Bioinformatics
- Human Genetics
Background:
- DNA copy number variation (CNV) is increasingly recognized for its role in human genetic diversity and disease predisposition.
- Advances in genome sequencing technologies facilitate the comprehensive characterization of genomic variations like CNVs.
Purpose of the Study:
- To review recent advancements in detecting DNA copy number variations (CNVs) from whole genome sequencing data.
- To discuss current bioinformatics approaches and computational tools for CNV detection.
Main Methods:
- Literature review of recent studies on CNV detection.
- Analysis of various bioinformatics algorithms and software tools for CNV identification.
- Evaluation of the strengths and limitations of existing CNV detection methodologies.
Main Results:
- Several bioinformatics approaches and computational tools have been developed for CNV detection from next-generation sequencing data.
- The review details the methodologies, capabilities, and constraints of these tools.
Conclusions:
- The selection of appropriate CNV analysis tools is crucial for researchers and analysts.
- Future development should focus on enhancing the accuracy and efficiency of CNV detection methods.
Keywords:
CNVscomputational modelingcopy number variationnext generation sequencingstructural variation (SV)whole-genome sequencingMore Related Videos
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