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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Coupling SIMD and SIMT architectures to boost performance of a phylogeny-aware alignment kernel
|
|---|---|
| Published in |
BMC Bioinformatics, August 2012
|
| DOI | 10.1186/1471-2105-13-196 |
| Pubmed ID | |
| Authors |
Nikolaos Alachiotis, Simon A Berger, Alexandros Stamatakis |
| Abstract |
Aligning short DNA reads to a reference sequence alignment is a prerequisite for detecting their biological origin and analyzing them in a phylogenetic context. With the PaPaRa tool we introduced a dedicated dynamic programming algorithm for simultaneously aligning short reads to reference alignments and corresponding evolutionary reference trees. The algorithm aligns short reads to phylogenetic profiles that correspond to the branches of such a reference tree. The algorithm needs to perform an immense number of pairwise alignments. Therefore, we explore vector intrinsics and GPUs to accelerate the PaPaRa alignment kernel. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 1 | 33% |
| Sweden | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 6% |
| Sweden | 2 | 6% |
| United Kingdom | 1 | 3% |
| Unknown | 30 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 29% |
| Researcher | 8 | 23% |
| Professor | 3 | 9% |
| Student > Master | 3 | 9% |
| Student > Bachelor | 2 | 6% |
| Other | 7 | 20% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 34% |
| Computer Science | 11 | 31% |
| Biochemistry, Genetics and Molecular Biology | 4 | 11% |
| Engineering | 2 | 6% |
| Nursing and Health Professions | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 3 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 11 August 2012.
All research outputs
#13,365,440
of 22,673,450 outputs
Outputs from BMC Bioinformatics
#4,187
of 7,249 outputs
Outputs of similar age
#92,322
of 166,798 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 104 outputs
Altmetric has tracked 22,673,450 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,249 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 38th percentile – i.e., 38% 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 166,798 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 104 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 52% of its contemporaries.