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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Streamlined analysis of duplex sequencing data with Du Novo
|
|---|---|
| Published in |
Genome Biology, August 2016
|
| DOI | 10.1186/s13059-016-1039-4 |
| Pubmed ID | |
| Authors |
Nicholas Stoler, Barbara Arbeithuber, Wilfried Guiblet, Kateryna D. Makova, Anton Nekrutenko |
| Abstract |
Duplex sequencing was originally developed to detect rare nucleotide polymorphisms normally obscured by the noise of high-throughput sequencing. Here we describe a new, streamlined, reference-free approach for the analysis of duplex sequencing data. We show the approach performs well on simulated data and precisely reproduces previously published results and apply it to a newly produced dataset, enabling us to type low-frequency variants in human mitochondrial DNA. Finally, we provide all necessary tools as stand-alone components as well as integrate them into the Galaxy platform. All analyses performed in this manuscript can be repeated exactly as described at http://usegalaxy.org/duplex . |
X Demographics
The data shown below were collected from the profiles of 15 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 | 5 | 33% |
| Ireland | 1 | 7% |
| Australia | 1 | 7% |
| France | 1 | 7% |
| Unknown | 7 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 60% |
| Scientists | 6 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Unknown | 62 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 22% |
| Student > Bachelor | 10 | 16% |
| Student > Ph. D. Student | 9 | 14% |
| Other | 6 | 10% |
| Professor | 4 | 6% |
| Other | 11 | 17% |
| Unknown | 9 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 21 | 33% |
| Agricultural and Biological Sciences | 14 | 22% |
| Computer Science | 8 | 13% |
| Medicine and Dentistry | 4 | 6% |
| Engineering | 3 | 5% |
| Other | 2 | 3% |
| Unknown | 11 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 20 June 2023.
All research outputs
#2,977,136
of 25,373,627 outputs
Outputs from Genome Biology
#2,232
of 4,467 outputs
Outputs of similar age
#48,737
of 349,724 outputs
Outputs of similar age from Genome Biology
#29
of 55 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 50% 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 349,724 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 86% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.