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A biologically informed method for detecting rare variant associations

Overview of attention for article published in BioData Mining, August 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
A biologically informed method for detecting rare variant associations
Published in
BioData Mining, August 2016
DOI 10.1186/s13040-016-0107-3
Pubmed ID
Authors

Carrie Colleen Buchanan Moore, Anna Okula Basile, John Robert Wallace, Alex Thomas Frase, Marylyn DeRiggi Ritchie

Abstract

BioBin is a bioinformatics software package developed to automate the process of binning rare variants into groups for statistical association analysis using a biological knowledge-driven framework. BioBin collapses variants into biological features such as genes, pathways, evolutionary conserved regions (ECRs), protein families, regulatory regions, and others based on user-designated parameters. BioBin provides the infrastructure to create complex and interesting hypotheses in an automated fashion thereby circumventing the necessity for advanced and time consuming scripting. In this manuscript, we describe the software package for BioBin, along with type I error and power simulations to demonstrate the strengths and various customizable features and analysis options of this variant binning tool. Simulation testing highlights the utility of BioBin as a fast, comprehensive and expandable tool for the biologically-inspired binning and analysis of low-frequency variants in sequence data. The BioBin software package has the capability to transform and streamline the analysis pipelines for researchers analyzing rare variants. This automated bioinformatics tool minimizes the manual effort of creating genomic regions for binning such that time can be spent on the much more interesting task of statistical analyses. This software package is open source and freely available from http://ritchielab.com/software/biobin-download.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 17%
Professor > Associate Professor 3 17%
Student > Doctoral Student 2 11%
Lecturer 2 11%
Student > Master 2 11%
Other 4 22%
Unknown 2 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 28%
Agricultural and Biological Sciences 2 11%
Medicine and Dentistry 2 11%
Computer Science 2 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Other 4 22%
Unknown 2 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 January 2017.
All research outputs
#4,923,328
of 24,598,501 outputs
Outputs from BioData Mining
#105
of 320 outputs
Outputs of similar age
#79,061
of 343,835 outputs
Outputs of similar age from BioData Mining
#7
of 10 outputs
Altmetric has tracked 24,598,501 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 320 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.5. This one has gotten more attention than average, scoring higher than 67% 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 343,835 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 76% 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.