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Phylo_dCor: distance correlation as a novel metric for phylogenetic profiling

Overview of attention for article published in BMC Bioinformatics, September 2017
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4 tweeters

Citations

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3 Dimensions

Readers on

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17 Mendeley
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Title
Phylo_dCor: distance correlation as a novel metric for phylogenetic profiling
Published in
BMC Bioinformatics, September 2017
DOI 10.1186/s12859-017-1815-5
Pubmed ID
Authors

Gabriella Sferra, Federica Fratini, Marta Ponzi, Elisabetta Pizzi

Abstract

Elaboration of powerful methods to predict functional and/or physical protein-protein interactions from genome sequence is one of the main tasks in the post-genomic era. Phylogenetic profiling allows the prediction of protein-protein interactions at a whole genome level in both Prokaryotes and Eukaryotes. For this reason it is considered one of the most promising methods. Here, we propose an improvement of phylogenetic profiling that enables handling of large genomic datasets and infer global protein-protein interactions. This method uses the distance correlation as a new measure of phylogenetic profile similarity. We constructed robust reference sets and developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation that makes it applicable to large genomic data. Using Saccharomyces cerevisiae and Escherichia coli genome datasets, we showed that Phylo-dCor outperforms phylogenetic profiling methods previously described based on the mutual information and Pearson's correlation as measures of profile similarity. In this work, we constructed and assessed robust reference sets and propose the distance correlation as a measure for comparing phylogenetic profiles. To make it applicable to large genomic data, we developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation. Two R scripts that can be run on a wide range of machines are available upon request.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 53%
Professor > Associate Professor 3 18%
Student > Master 3 18%
Lecturer 1 6%
Unknown 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 53%
Biochemistry, Genetics and Molecular Biology 4 24%
Computer Science 2 12%
Social Sciences 1 6%
Unknown 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 20 December 2017.
All research outputs
#9,587,370
of 15,640,885 outputs
Outputs from BMC Bioinformatics
#3,720
of 5,683 outputs
Outputs of similar age
#149,043
of 273,490 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 28 outputs
Altmetric has tracked 15,640,885 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,683 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 30th percentile – i.e., 30% 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 273,490 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.