| Title |
Detection of internal exon deletion with exon Del
|
|---|---|
| Published in |
BMC Bioinformatics, October 2014
|
| DOI | 10.1186/1471-2105-15-332 |
| Pubmed ID | |
| Authors |
Yan Guo, Shilin Zhao, Brian D Lehmann, Quanhu Sheng, Timothy M Shaver, Thomas P Stricker, Jennifer A Pietenpol, Yu Shyr |
| Abstract |
Exome sequencing allows researchers to study the human genome in unprecedented detail. Among the many types of variants detectable through exome sequencing, one of the most over looked types of mutation is internal deletion of exons. Internal exon deletions are the absence of consecutive exons in a gene. Such deletions have potentially significant biological meaning, and they are often too short to be considered copy number variation. Therefore, to the need for efficient detection of such deletions using exome sequencing data exists. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 14% |
| United Kingdom | 1 | 14% |
| United States | 1 | 14% |
| France | 1 | 14% |
| Israel | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| Unknown | 28 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 34% |
| Student > Ph. D. Student | 7 | 24% |
| Student > Master | 6 | 21% |
| Student > Bachelor | 2 | 7% |
| Other | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 11 | 38% |
| Agricultural and Biological Sciences | 10 | 34% |
| Computer Science | 5 | 17% |
| Unknown | 3 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 19 October 2014.
All research outputs
#12,710,880
of 22,766,595 outputs
Outputs from BMC Bioinformatics
#3,621
of 7,273 outputs
Outputs of similar age
#113,445
of 255,780 outputs
Outputs of similar age from BMC Bioinformatics
#55
of 126 outputs
Altmetric has tracked 22,766,595 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 255,780 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 126 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 54% of its contemporaries.