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MESSA: MEta-Server for protein Sequence Analysis

Overview of attention for article published in BMC Biology, October 2012
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

6 tweeters


36 Dimensions

Readers on

63 Mendeley
1 CiteULike
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MESSA: MEta-Server for protein Sequence Analysis
Published in
BMC Biology, October 2012
DOI 10.1186/1741-7007-10-82
Pubmed ID

Qian Cong, Nick V Grishin


Computational sequence analysis, that is, prediction of local sequence properties, homologs, spatial structure and function from the sequence of a protein, offers an efficient way to obtain needed information about proteins under study. Since reliable prediction is usually based on the consensus of many computer programs, meta-severs have been developed to fit such needs. Most meta-servers focus on one aspect of sequence analysis, while others incorporate more information, such as PredictProtein for local sequence feature predictions, SMART for domain architecture and sequence motif annotation, and GeneSilico for secondary and spatial structure prediction. However, as predictions of local sequence properties, three-dimensional structure and function are usually intertwined, it is beneficial to address them together.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 5 8%
United States 3 5%
Germany 1 2%
Unknown 54 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 33%
Student > Ph. D. Student 14 22%
Student > Master 7 11%
Other 3 5%
Student > Postgraduate 3 5%
Other 8 13%
Unknown 7 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 41%
Biochemistry, Genetics and Molecular Biology 12 19%
Computer Science 7 11%
Medicine and Dentistry 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 4 6%
Unknown 9 14%

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 17 October 2012.
All research outputs
of 12,434,464 outputs
Outputs from BMC Biology
of 1,118 outputs
Outputs of similar age
of 128,341 outputs
Outputs of similar age from BMC Biology
of 10 outputs
Altmetric has tracked 12,434,464 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 1,118 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.1. This one is in the 37th percentile – i.e., 37% 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 128,341 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.