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Mendeley readers
Attention Score in Context
| Title |
transAlign: using amino acids to facilitate the multiple alignment of protein-coding DNA sequences
|
|---|---|
| Published in |
BMC Bioinformatics, June 2005
|
| DOI | 10.1186/1471-2105-6-156 |
| Pubmed ID | |
| Authors |
Olaf RP Bininda-Emonds |
| Abstract |
Alignments of homologous DNA sequences are crucial for comparative genomics and phylogenetic analysis. However, multiple alignment represents a computationally difficult problem. For protein-coding DNA sequences, it is more advantageous in terms of both speed and accuracy to align the amino-acid sequences specified by the DNA sequences rather than the DNA sequences themselves. Many implementations making use of this concept of "translated alignments" are incomplete in the sense that they require the user to manually translate the DNA sequences and to perform the amino-acid alignment. As such, they are not well suited to large-scale automated alignments of large and/or numerous DNA data sets. |
Mendeley readers
The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 3% |
| Germany | 2 | 1% |
| United Kingdom | 2 | 1% |
| Chile | 1 | <1% |
| France | 1 | <1% |
| Japan | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 146 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 41 | 26% |
| Student > Ph. D. Student | 28 | 18% |
| Student > Master | 19 | 12% |
| Professor > Associate Professor | 11 | 7% |
| Student > Doctoral Student | 10 | 6% |
| Other | 30 | 19% |
| Unknown | 20 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 90 | 57% |
| Biochemistry, Genetics and Molecular Biology | 24 | 15% |
| Computer Science | 5 | 3% |
| Earth and Planetary Sciences | 3 | 2% |
| Immunology and Microbiology | 2 | 1% |
| Other | 10 | 6% |
| Unknown | 25 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 July 2017.
All research outputs
#5,842,899
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#2,168
of 7,234 outputs
Outputs of similar age
#17,184
of 56,646 outputs
Outputs of similar age from BMC Bioinformatics
#12
of 29 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,234 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 gotten more attention than average, scoring higher than 69% 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 56,646 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 69% of its contemporaries.
We're also able to compare this research output to 29 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 58% of its contemporaries.