Title |
Sequencing technologies and tools for short tandem repeat variation detection
|
---|---|
Published in |
Briefings in Bioinformatics, February 2014
|
DOI | 10.1093/bib/bbu001 |
Pubmed ID | |
Authors |
Minh Duc Cao, Sureshkumar Balasubramanian, Mikael Bodén |
Abstract |
Short tandem repeats are highly polymorphic and associated with a wide range of phenotypic variation, some of which cause neurodegenerative disease in humans. With advances in high-throughput sequencing technologies, there are novel opportunities to study genetic variation. While available sequencing technologies and bioinformatics tools provide options for mining high-throughput sequencing data, their suitability for analysis of repeat variation is an open question, with tools for quantifying variability in repetitive sequence still in their infancy. We present here a comprehensive survey and empirical evaluation of current sequencing technologies and bioinformatics tools in all stages of an analysis pipeline. While there is not one optimal pipeline to suit all circumstances, we find that the choice of alignment and repeat genotyping tools greatly impacts the accuracy and efficiency by which short tandem repeat variation can be detected. We further note that to detect variation relevant to many repeat diseases, it is essential to choose technologies that offer either long read-lengths or paired-end sequencing, coupled with specific genotyping tools. |
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Geographical breakdown
Country | Count | As % |
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United States | 1 | 25% |
Israel | 1 | 25% |
Germany | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Scientists | 3 | 75% |
Members of the public | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 2 | 2% |
Australia | 2 | 2% |
Switzerland | 1 | <1% |
Hong Kong | 1 | <1% |
Italy | 1 | <1% |
Netherlands | 1 | <1% |
Sweden | 1 | <1% |
Brazil | 1 | <1% |
Canada | 1 | <1% |
Other | 1 | <1% |
Unknown | 95 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 32 | 30% |
Student > Ph. D. Student | 18 | 17% |
Student > Bachelor | 10 | 9% |
Student > Master | 6 | 6% |
Other | 6 | 6% |
Other | 18 | 17% |
Unknown | 17 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 48 | 45% |
Biochemistry, Genetics and Molecular Biology | 20 | 19% |
Medicine and Dentistry | 6 | 6% |
Computer Science | 6 | 6% |
Business, Management and Accounting | 1 | <1% |
Other | 6 | 6% |
Unknown | 20 | 19% |