| Title |
Bellerophon: a hybrid method for detecting interchromo-somal rearrangements at base pair resolution using next-generation sequencing data
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-s5-s6 |
| Pubmed ID | |
| Authors |
Matthew Hayes, Jing Li |
| Abstract |
Somatically-acquired translocations may serve as important markers for assessing the cause and nature of diseases like cancer. Algorithms to locate translocations may use next-generation sequencing (NGS) platform data. However, paired-end strategies do not accurately predict precise translocation breakpoints, and "split-read" methods may lose sensitivity if a translocation boundary is not captured by many sequenced reads. To address these challenges, we have developed "Bellerophon", a method that uses discordant read pairs to identify potential translocations, and subsequently uses "soft-clipped" reads to predict the location of the precise breakpoints. Furthermore, for each chimeric breakpoint, our method attempts to classify it as a participant in an unbalanced translocation, balanced translocation, or interchromosomal insertion. |
X Demographics
The data shown below were collected from the profiles of 2 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 | 1 | 50% |
| China | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 3% |
| United States | 2 | 3% |
| United Kingdom | 2 | 3% |
| Germany | 1 | 2% |
| Netherlands | 1 | 2% |
| Mexico | 1 | 2% |
| Iran, Islamic Republic of | 1 | 2% |
| Unknown | 55 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 31% |
| Researcher | 17 | 26% |
| Professor | 4 | 6% |
| Other | 4 | 6% |
| Professor > Associate Professor | 4 | 6% |
| Other | 8 | 12% |
| Unknown | 8 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 28 | 43% |
| Biochemistry, Genetics and Molecular Biology | 10 | 15% |
| Computer Science | 9 | 14% |
| Medicine and Dentistry | 3 | 5% |
| Engineering | 2 | 3% |
| Other | 3 | 5% |
| Unknown | 10 | 15% |
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 04 December 2019.
All research outputs
#1,689,375
of 22,708,120 outputs
Outputs from BMC Bioinformatics
#383
of 7,256 outputs
Outputs of similar age
#14,685
of 199,476 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 135 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,256 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 94% of its peers.
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We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.