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DAPPER: a data-mining resource for protein-protein interactions

Overview of attention for article published in BioData Mining, September 2015
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2 tweeters

Citations

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4 Dimensions

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27 Mendeley
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3 CiteULike
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Title
DAPPER: a data-mining resource for protein-protein interactions
Published in
BioData Mining, September 2015
DOI 10.1186/s13040-015-0063-3
Pubmed ID
Authors

Syed Haider, Zoltan Lipinszki, Marcin R. Przewloka, Yaseen Ladak, Pier Paolo D’Avino, Yuu Kimata, Pietro Lio’, David M. Glover

Abstract

The identification of interaction networks between proteins and complexes holds the promise of offering novel insights into the molecular mechanisms that regulate many biological processes. With increasing volumes of such datasets, especially in model organisms such as Drosophila melanogaster, there exists a pressing need for specialised tools, which can seamlessly collect, integrate and analyse these data. Here we describe a database coupled with a mining tool for protein-protein interactions (DAPPER), developed as a rich resource for studying multi-protein complexes in Drosophila melanogaster. This proteomics database is compiled through mass spectrometric analyses of many protein complexes affinity purified from Drosophila tissues and cultured cells. The web access to DAPPER is provided via an accelerated version of BioMart software enabling data-mining through customised querying and output formats. The protein-protein interaction dataset is annotated with FlyBase identifiers, and further linked to the Ensembl database using BioMart's data-federation model, thereby enabling complex multi-dataset queries. DAPPER is open source, with all its contents and source code are freely available. DAPPER offers an easy-to-navigate and extensible platform for real-time integration of diverse resources containing new and existing protein-protein interaction datasets of Drosophila melanogaster.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Bangladesh 1 4%
Unknown 26 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 33%
Student > Ph. D. Student 4 15%
Student > Bachelor 4 15%
Lecturer 2 7%
Student > Master 2 7%
Other 5 19%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 33%
Agricultural and Biological Sciences 5 19%
Computer Science 4 15%
Engineering 4 15%
Medicine and Dentistry 2 7%
Other 2 7%
Unknown 1 4%

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 07 October 2015.
All research outputs
#13,663,454
of 17,800,904 outputs
Outputs from BioData Mining
#230
of 277 outputs
Outputs of similar age
#165,703
of 256,662 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 277 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them