| Title |
Stargazer: a software tool for calling star alleles from next-generation sequencing data using CYP2D6 as a model
|
|---|---|
| Published in |
Genetics in Medicine, June 2018
|
| DOI | 10.1038/s41436-018-0054-0 |
| Pubmed ID | |
| Authors |
Seung-been Lee, Marsha M. Wheeler, Karynne Patterson, Sean McGee, Rachel Dalton, Erica L. Woodahl, Andrea Gaedigk, Kenneth E. Thummel, Deborah A. Nickerson |
| Abstract |
Genotyping CYP2D6 is important for precision drug therapy because the enzyme it encodes metabolizes approximately 25% of drugs, and its activity varies considerably among individuals. Genotype analysis of CYP2D6 is challenging due to its highly polymorphic nature. Over 100 haplotypes (star alleles) have been defined for CYP2D6, some involving a gene conversion with its nearby nonfunctional but highly homologous paralog CYP2D7. We present Stargazer, a new bioinformatics tool that uses next-generation sequencing (NGS) data to call star alleles for CYP2D6 ( https://stargazer.gs.washington.edu/stargazerweb/ ). Stargazer is currently being extended for other pharmacogenes. Stargazer identifies star alleles from NGS data by detecting single nucleotide variants, insertion-deletion variants, and structural variants. Stargazer detects structural variation, including gene deletions, duplications, and conversions, by calculating paralog-specific copy numbers from read depths. We applied Stargazer to the NGS data of 32 ethnically diverse HapMap trios that were genotyped by TaqMan assays, long-range polymerase chain reaction, quantitative multiplex polymerase chain reaction, high-resolution melting analysis, and/or Sanger sequencing. CYP2D6 genotyping by Stargazer was 99.0% concordant with the data obtained by these methods, and showed that 28.1% of the samples had structural variation including CYP2D6/CYP2D7 hybrids. Accurate genotyping of pharmacogenes with NGS and subsequent allele calling with Stargazer will aid the implementation of precision drug therapy. |
X Demographics
The data shown below were collected from the profiles of 22 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 11 | 50% |
| Canada | 1 | 5% |
| Taiwan | 1 | 5% |
| United Kingdom | 1 | 5% |
| Mexico | 1 | 5% |
| Indonesia | 1 | 5% |
| China | 1 | 5% |
| Unknown | 5 | 23% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 64% |
| Scientists | 7 | 32% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 114 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 25% |
| Student > Ph. D. Student | 15 | 13% |
| Student > Bachelor | 12 | 11% |
| Student > Master | 10 | 9% |
| Other | 8 | 7% |
| Other | 14 | 12% |
| Unknown | 27 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 34 | 30% |
| Medicine and Dentistry | 13 | 11% |
| Agricultural and Biological Sciences | 13 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 9 | 8% |
| Computer Science | 4 | 4% |
| Other | 9 | 8% |
| Unknown | 32 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 March 2021.
All research outputs
#2,125,554
of 25,826,146 outputs
Outputs from Genetics in Medicine
#722
of 2,965 outputs
Outputs of similar age
#42,786
of 343,883 outputs
Outputs of similar age from Genetics in Medicine
#31
of 82 outputs
Altmetric has tracked 25,826,146 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,965 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.1. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 343,883 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 82 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.