Title |
Improving mapping and SNP-calling performance in multiplexed targeted next-generation sequencing
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Published in |
BMC Genomics, August 2012
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DOI | 10.1186/1471-2164-13-417 |
Pubmed ID | |
Authors |
Abdou ElSharawy, Michael Forster, Nadine Schracke, Andreas Keller, Ingo Thomsen, Britt-Sabina Petersen, Björn Stade, Peer Stähler, Stefan Schreiber, Philip Rosenstiel, Andre Franke |
Abstract |
Compared to classical genotyping, targeted next-generation sequencing (tNGS) can be custom-designed to interrogate entire genomic regions of interest, in order to detect novel as well as known variants. To bring down the per-sample cost, one approach is to pool barcoded NGS libraries before sample enrichment. Still, we lack a complete understanding of how this multiplexed tNGS approach and the varying performance of the ever-evolving analytical tools can affect the quality of variant discovery. Therefore, we evaluated the impact of different software tools and analytical approaches on the discovery of single nucleotide polymorphisms (SNPs) in multiplexed tNGS data. To generate our own test model, we combined a sequence capture method with NGS in three experimental stages of increasing complexity (E. coli genes, multiplexed E. coli, and multiplexed HapMap BRCA1/2 regions). |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 3 | 38% |
Sweden | 1 | 13% |
Germany | 1 | 13% |
United Kingdom | 1 | 13% |
Unknown | 2 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 50% |
Scientists | 4 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 9% |
United Kingdom | 3 | 3% |
Netherlands | 2 | 2% |
Brazil | 2 | 2% |
Germany | 1 | <1% |
Finland | 1 | <1% |
Canada | 1 | <1% |
Australia | 1 | <1% |
Denmark | 1 | <1% |
Other | 3 | 3% |
Unknown | 92 | 79% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 30 | 26% |
Researcher | 29 | 25% |
Student > Master | 15 | 13% |
Student > Postgraduate | 9 | 8% |
Professor > Associate Professor | 9 | 8% |
Other | 20 | 17% |
Unknown | 5 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 74 | 63% |
Biochemistry, Genetics and Molecular Biology | 11 | 9% |
Computer Science | 6 | 5% |
Medicine and Dentistry | 6 | 5% |
Engineering | 2 | 2% |
Other | 5 | 4% |
Unknown | 13 | 11% |