Title |
Comparison of insertion/deletion calling algorithms on human next-generation sequencing data
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Published in |
BMC Research Notes, December 2014
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DOI | 10.1186/1756-0500-7-864 |
Pubmed ID | |
Authors |
Dalia H Ghoneim, Jason R Myers, Emily Tuttle, Alex R Paciorkowski |
Abstract |
Insertions/deletions (indels) are the second most common type of genomic variant and the most common type of structural variant. Identification of indels in next generation sequencing data is a challenge, and algorithms commonly used for indel detection have not been compared on a research cohort of human subject genomic data. Guidelines for the optimal detection of biologically significant indels are limited. We analyzed three sets of human next generation sequencing data (48 samples of a 200 gene target exon sequencing, 45 samples of whole exome sequencing, and 2 samples of whole genome sequencing) using three algorithms for indel detection (Pindel, Genome Analysis Tool Kit's UnifiedGenotyper and HaplotypeCaller). |
X Demographics
Geographical breakdown
Country | Count | As % |
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Canada | 1 | 50% |
Israel | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Colombia | 1 | <1% |
France | 1 | <1% |
Italy | 1 | <1% |
United Kingdom | 1 | <1% |
China | 1 | <1% |
United States | 1 | <1% |
Unknown | 105 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 23 | 21% |
Researcher | 20 | 18% |
Student > Master | 16 | 14% |
Student > Bachelor | 13 | 12% |
Professor > Associate Professor | 8 | 7% |
Other | 15 | 14% |
Unknown | 16 | 14% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 42 | 38% |
Biochemistry, Genetics and Molecular Biology | 25 | 23% |
Medicine and Dentistry | 14 | 13% |
Computer Science | 4 | 4% |
Neuroscience | 2 | 2% |
Other | 6 | 5% |
Unknown | 18 | 16% |