| Title |
How do alignment programs perform on sequencing data with varying qualities and from repetitive regions?
|
|---|---|
| Published in |
BioData Mining, June 2012
|
| DOI | 10.1186/1756-0381-5-6 |
| Pubmed ID | |
| Authors |
Xiaoqing Yu, Kishore Guda, Joseph Willis, Martina Veigl, Zhenghe Wang, Sanford Markowitz, Mark D Adams, Shuying Sun |
| Abstract |
Next-generation sequencing technologies generate a significant number of short reads that are utilized to address a variety of biological questions. However, quite often, sequencing reads tend to have low quality at the 3' end and are generated from the repetitive regions of a genome. It is unclear how different alignment programs perform under these different cases. In order to investigate this question, we use both real data and simulated data with the above issues to evaluate the performance of four commonly used algorithms: SOAP2, Bowtie, BWA, and Novoalign. |
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 % |
|---|---|---|
| Norway | 2 | 29% |
| Canada | 1 | 14% |
| United States | 1 | 14% |
| United Kingdom | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 140 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 4% |
| Netherlands | 2 | 1% |
| Sweden | 2 | 1% |
| Italy | 2 | 1% |
| Germany | 1 | <1% |
| Norway | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 126 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 47 | 34% |
| Student > Ph. D. Student | 41 | 29% |
| Student > Master | 15 | 11% |
| Other | 6 | 4% |
| Professor > Associate Professor | 6 | 4% |
| Other | 19 | 14% |
| Unknown | 6 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 74 | 53% |
| Biochemistry, Genetics and Molecular Biology | 31 | 22% |
| Computer Science | 9 | 6% |
| Medicine and Dentistry | 6 | 4% |
| Engineering | 3 | 2% |
| Other | 6 | 4% |
| Unknown | 11 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 April 2013.
All research outputs
#7,440,602
of 24,666,614 outputs
Outputs from BioData Mining
#151
of 319 outputs
Outputs of similar age
#49,946
of 168,158 outputs
Outputs of similar age from BioData Mining
#4
of 6 outputs
Altmetric has tracked 24,666,614 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 319 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.5. This one has gotten more attention than average, scoring higher than 52% 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 168,158 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 70% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.