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BioBin: a bioinformatics tool for automating the binning of rare variants using publicly available biological knowledge

Overview of attention for article published in BMC Medical Genomics, May 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Citations

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36 Dimensions

Readers on

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81 Mendeley
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1 CiteULike
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Title
BioBin: a bioinformatics tool for automating the binning of rare variants using publicly available biological knowledge
Published in
BMC Medical Genomics, May 2013
DOI 10.1186/1755-8794-6-s2-s6
Pubmed ID
Authors

Carrie B Moore, John R Wallace, Alex T Frase, Sarah A Pendergrass, Marylyn D Ritchie

Abstract

With the recent decreasing cost of genome sequence data, there has been increasing interest in rare variants and methods to detect their association to disease. We developed BioBin, a flexible collapsing method inspired by biological knowledge that can be used to automate the binning of low frequency variants for association testing. We also built the Library of Knowledge Integration (LOKI), a repository of data assembled from public databases, which contains resources such as: dbSNP and gene Entrez database information from the National Center for Biotechnology (NCBI), pathway information from Gene Ontology (GO), Protein families database (Pfam), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, NetPath - signal transduction pathways, Open Regulatory Annotation Database (ORegAnno), Biological General Repository for Interaction Datasets (BioGrid), Pharmacogenomics Knowledge Base (PharmGKB), Molecular INTeraction database (MINT), and evolutionary conserved regions (ECRs) from UCSC Genome Browser. The novelty of BioBin is access to comprehensive knowledge-guided multi-level binning. For example, bin boundaries can be formed using genomic locations from: functional regions, evolutionary conserved regions, genes, and/or pathways.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 9%
Korea, Republic of 1 1%
Italy 1 1%
United Kingdom 1 1%
Hong Kong 1 1%
Denmark 1 1%
Canada 1 1%
Unknown 68 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 27%
Student > Ph. D. Student 14 17%
Student > Bachelor 8 10%
Professor > Associate Professor 6 7%
Student > Doctoral Student 5 6%
Other 18 22%
Unknown 8 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 36%
Biochemistry, Genetics and Molecular Biology 19 23%
Medicine and Dentistry 9 11%
Computer Science 6 7%
Psychology 2 2%
Other 6 7%
Unknown 10 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 15 November 2013.
All research outputs
#1,746,554
of 21,298,857 outputs
Outputs from BMC Medical Genomics
#56
of 1,129 outputs
Outputs of similar age
#14,950
of 173,307 outputs
Outputs of similar age from BMC Medical Genomics
#1
of 10 outputs
Altmetric has tracked 21,298,857 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,129 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done particularly well, scoring higher than 95% 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 173,307 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% 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 all of them