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Twitter Demographics
Mendeley readers
Attention Score in Context
| 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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 40% |
| Poland | 1 | 10% |
| Germany | 1 | 10% |
| France | 1 | 10% |
| Côte d'Ivoire | 1 | 10% |
| Montenegro | 1 | 10% |
| Unknown | 1 | 10% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 6 | 60% |
| Members of the public | 4 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 82 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 | 69 | 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 > Master | 6 | 7% |
| Other | 18 | 22% |
| Unknown | 8 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 29 | 35% |
| Biochemistry, Genetics and Molecular Biology | 19 | 23% |
| Medicine and Dentistry | 9 | 11% |
| Computer Science | 6 | 7% |
| Psychology | 2 | 2% |
| Other | 7 | 9% |
| Unknown | 10 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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
#2,554,046
of 24,335,784 outputs
Outputs from BMC Medical Genomics
#81
of 1,311 outputs
Outputs of similar age
#21,352
of 196,712 outputs
Outputs of similar age from BMC Medical Genomics
#2
of 16 outputs
Altmetric has tracked 24,335,784 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,311 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 93% 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 196,712 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 89% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.