| Title |
An improved approach for accurate and efficient calling of structural variations with low-coverage sequence data
|
|---|---|
| Published in |
BMC Bioinformatics, April 2012
|
| DOI | 10.1186/1471-2105-13-s6-s6 |
| Pubmed ID | |
| Authors |
Jin Zhang, Jiayin Wang, Yufeng Wu |
| Abstract |
Recent advances in sequencing technologies make it possible to comprehensively study structural variations (SVs) using sequence data of large-scale populations. Currently, more efforts have been taken to develop methods that call SVs with exact breakpoints. Among these approaches, split-read mapping methods can be applied on low-coverage sequence data. With increasing amount of data generated, more efficient split-read mapping methods are still needed. Also, since sequence errors can not be avoided for the current sequencing technologies, more accurate split-read mapping methods are still needed to better handle sequence errors. |
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 % |
|---|---|---|
| Australia | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 1% |
| Italy | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 75 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 32% |
| Researcher | 13 | 17% |
| Student > Master | 12 | 15% |
| Student > Doctoral Student | 4 | 5% |
| Student > Bachelor | 4 | 5% |
| Other | 7 | 9% |
| Unknown | 13 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 25 | 32% |
| Biochemistry, Genetics and Molecular Biology | 17 | 22% |
| Computer Science | 14 | 18% |
| Neuroscience | 2 | 3% |
| Medicine and Dentistry | 2 | 3% |
| Other | 5 | 6% |
| Unknown | 13 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 29 October 2012.
All research outputs
#16,770,898
of 24,666,614 outputs
Outputs from BMC Bioinformatics
#5,560
of 7,565 outputs
Outputs of similar age
#107,652
of 165,909 outputs
Outputs of similar age from BMC Bioinformatics
#65
of 93 outputs
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