Title |
A Model-Based Clustering Method for Genomic Structural Variant Prediction and Genotyping Using Paired-End Sequencing Data
|
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Published in |
PLOS ONE, December 2012
|
DOI | 10.1371/journal.pone.0052881 |
Pubmed ID | |
Authors |
Matthew Hayes, Yoon Soo Pyon, Jing Li |
Abstract |
Structural variation (SV) has been reported to be associated with numerous diseases such as cancer. With the advent of next generation sequencing (NGS) technologies, various types of SV can be potentially identified. We propose a model based clustering approach utilizing a set of features defined for each type of SV events. Our method, termed SVMiner, not only provides a probability score for each candidate, but also predicts the heterozygosity of genomic deletions. Extensive experiments on genome-wide deep sequencing data have demonstrated that SVMiner is robust against the variability of a single cluster feature, and it significantly outperforms several commonly used SV detection programs. SVMiner can be downloaded from http://cbc.case.edu/svminer/. |
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Members of the public | 1 | 25% |
Mendeley readers
Geographical breakdown
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United States | 2 | 3% |
France | 1 | 2% |
United Kingdom | 1 | 2% |
Sweden | 1 | 2% |
China | 1 | 2% |
New Zealand | 1 | 2% |
Unknown | 52 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 23 | 39% |
Student > Ph. D. Student | 16 | 27% |
Professor | 4 | 7% |
Student > Bachelor | 4 | 7% |
Student > Master | 4 | 7% |
Other | 5 | 8% |
Unknown | 3 | 5% |
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Biochemistry, Genetics and Molecular Biology | 11 | 19% |
Computer Science | 11 | 19% |
Medicine and Dentistry | 3 | 5% |
Engineering | 2 | 3% |
Other | 3 | 5% |
Unknown | 4 | 7% |