| Title |
Quantitative group testing-based overlapping pool sequencing to identify rare variant carriers
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-195 |
| Pubmed ID | |
| Authors |
Chang-Chang Cao, Cheng Li, Xiao Sun |
| Abstract |
Genome-wide association studies have revealed that rare variants are responsible for a large portion of the heritability of some complex human diseases. This highlights the increasing importance of detecting and screening for rare variants. Although the massively parallel sequencing technologies have greatly reduced the cost of DNA sequencing, the identification of rare variant carriers by large-scale re-sequencing remains prohibitively expensive because of the huge challenge of constructing libraries for thousands of samples. Recently, several studies have reported that techniques from group testing theory and compressed sensing could help identify rare variant carriers in large-scale samples with few pooled sequencing experiments and a dramatically reduced cost. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 20% |
| Unknown | 4 | 80% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 45% |
| Student > Ph. D. Student | 5 | 25% |
| Student > Master | 2 | 10% |
| Unknown | 4 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 30% |
| Agricultural and Biological Sciences | 6 | 30% |
| Computer Science | 2 | 10% |
| Economics, Econometrics and Finance | 1 | 5% |
| Engineering | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 4 | 20% |
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 05 May 2020.
All research outputs
#2,597,523
of 22,757,090 outputs
Outputs from BMC Bioinformatics
#821
of 7,272 outputs
Outputs of similar age
#27,146
of 228,185 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 154 outputs
Altmetric has tracked 22,757,090 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 7,272 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 well, scoring higher than 88% 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 228,185 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 88% of its contemporaries.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.