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Mendeley readers
Attention Score in Context
| Title |
Tools for integrated sequence-structure analysis with UCSF Chimera
|
|---|---|
| Published in |
BMC Bioinformatics, July 2006
|
| DOI | 10.1186/1471-2105-7-339 |
| Pubmed ID | |
| Authors |
Elaine C Meng, Eric F Pettersen, Gregory S Couch, Conrad C Huang, Thomas E Ferrin |
| Abstract |
Comparing related structures and viewing the structures in the context of sequence alignments are important tasks in protein structure-function research. While many programs exist for individual aspects of such work, there is a need for interactive visualization tools that: (a) provide a deep integration of sequence and structure, far beyond mapping where a sequence region falls in the structure and vice versa; (b) facilitate changing data of one type based on the other (for example, using only sequence-conserved residues to match structures, or adjusting a sequence alignment based on spatial fit); (c) can be used with a researcher's own data, including arbitrary sequence alignments and annotations, closely or distantly related sets of proteins, etc.; and (d) interoperate with each other and with a full complement of molecular graphics features. We describe enhancements to UCSF Chimera to achieve these goals. |
Mendeley readers
The data shown below were compiled from readership statistics for 407 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 1% |
| Australia | 2 | <1% |
| United Kingdom | 2 | <1% |
| France | 1 | <1% |
| South Africa | 1 | <1% |
| Germany | 1 | <1% |
| Canada | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 393 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 74 | 18% |
| Researcher | 55 | 14% |
| Student > Bachelor | 41 | 10% |
| Student > Master | 37 | 9% |
| Student > Doctoral Student | 25 | 6% |
| Other | 76 | 19% |
| Unknown | 99 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 125 | 31% |
| Agricultural and Biological Sciences | 79 | 19% |
| Chemistry | 42 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 13 | 3% |
| Computer Science | 10 | 2% |
| Other | 33 | 8% |
| Unknown | 105 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 06 March 2023.
All research outputs
#2,359,675
of 23,495,502 outputs
Outputs from BMC Bioinformatics
#672
of 7,397 outputs
Outputs of similar age
#4,581
of 67,009 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 40 outputs
Altmetric has tracked 23,495,502 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,397 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 particularly well, scoring higher than 90% 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 67,009 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.