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CLAP: A web-server for automatic classification of proteins with special reference to multi-domain proteins

Overview of attention for article published in BMC Bioinformatics, October 2014
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Title
CLAP: A web-server for automatic classification of proteins with special reference to multi-domain proteins
Published in
BMC Bioinformatics, October 2014
DOI 10.1186/1471-2105-15-343
Pubmed ID
Authors

Mutharasu Gnanavel, Prachi Mehrotra, Ramaswamy Rakshambikai, Juliette Martin, Narayanaswamy Srinivasan, Ramachandra M Bhaskara

Abstract

The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 3%
Denmark 1 3%
Brazil 1 3%
Unknown 37 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 9 23%
Student > Bachelor 6 15%
Other 3 8%
Professor > Associate Professor 3 8%
Other 6 15%
Unknown 4 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 33%
Biochemistry, Genetics and Molecular Biology 8 20%
Computer Science 5 13%
Medicine and Dentistry 3 8%
Social Sciences 2 5%
Other 4 10%
Unknown 5 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 October 2014.
All research outputs
#20,238,443
of 22,765,347 outputs
Outputs from BMC Bioinformatics
#6,845
of 7,273 outputs
Outputs of similar age
#212,288
of 254,034 outputs
Outputs of similar age from BMC Bioinformatics
#99
of 109 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.