Title |
Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the Challenges
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Published in |
Frontiers in Genetics, February 2016
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DOI | 10.3389/fgene.2016.00002 |
Pubmed ID | |
Authors |
Louise B. Thingholm, Lars Andersen, Enes Makalic, Melissa C. Southey, Mads Thomassen, Lise Lotte Hansen |
Abstract |
The development and progression of cancer, a collection of diseases with complex genetic architectures, is facilitated by the interplay of multiple etiological factors. This complexity challenges the traditional single-platform study design and calls for an integrated approach to data analysis. However, integration of heterogeneous measurements of biological variation is a non-trivial exercise due to the diversity of the human genome and the variety of output data formats and genome coverage obtained from the commonly used molecular platforms. This review article will provide an introduction to integration strategies used for analyzing genetic risk factors for cancer. We critically examine the ability of these strategies to handle the complexity of the human genome and also accommodate information about the biological and functional interactions between the elements that have been measured-making the assessment of disease risk against a composite genomic factor possible. The focus of this review is to provide an overview and introduction to the main strategies and to discuss where there is a need for further development. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
United States | 1 | <1% |
Brazil | 1 | <1% |
Unknown | 103 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 26 | 25% |
Researcher | 25 | 24% |
Student > Master | 10 | 9% |
Professor > Associate Professor | 8 | 8% |
Student > Bachelor | 7 | 7% |
Other | 20 | 19% |
Unknown | 10 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 37 | 35% |
Agricultural and Biological Sciences | 22 | 21% |
Computer Science | 11 | 10% |
Engineering | 6 | 6% |
Mathematics | 5 | 5% |
Other | 12 | 11% |
Unknown | 13 | 12% |