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GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases

Overview of attention for article published in BMC Bioinformatics, January 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
21 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
86 Mendeley
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Title
GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-018-2027-3
Pubmed ID
Authors

Daniel Koile, Marta Cordoba, Maximiliano de Sousa Serro, Marcelo Andres Kauffman, Patricio Yankilevich

Abstract

GenIO is a novel web-server, designed to assist clinical genomics researchers and medical doctors in the diagnostic process of rare genetic diseases. The tool identifies the most probable variants causing a rare disease, using the genomic and clinical information provided by a medical practitioner. Variants identified in a whole-genome, whole-exome or target sequencing studies are annotated, classified and filtered by clinical significance. Candidate genes associated with the patient's symptoms, suspected disease and complementary findings are identified to obtain a small manageable number of the most probable recessive and dominant candidate gene variants associated with the rare disease case. Additionally, following the American College of Medical Genetics and Genomics and the Association of Molecular Pathology (ACMG-AMP) guidelines and recommendations, all potentially pathogenic variants that might be contributing to disease and secondary findings are identified. A retrospective study was performed on 40 patients with a diagnostic rate of 40%. All the known genes that were previously considered as disease causing were correctly identified in the final inherit model output lists. In previously undiagnosed cases, we had no additional yield. This unique, intuitive and user-friendly tool to assists medical doctors in the clinical genomics diagnostic process is openly available at https://bioinformatics.ibioba-mpsp-conicet.gov.ar/GenIO/ .

Twitter Demographics

The data shown below were collected from the profiles of 21 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 24%
Researcher 16 19%
Other 9 10%
Student > Master 8 9%
Student > Doctoral Student 4 5%
Other 11 13%
Unknown 17 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 24%
Medicine and Dentistry 17 20%
Agricultural and Biological Sciences 11 13%
Computer Science 8 9%
Nursing and Health Professions 3 3%
Other 5 6%
Unknown 21 24%

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 17 August 2018.
All research outputs
#2,215,971
of 21,028,682 outputs
Outputs from BMC Bioinformatics
#733
of 6,863 outputs
Outputs of similar age
#57,523
of 399,291 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 18 outputs
Altmetric has tracked 21,028,682 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 6,863 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 89% 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 399,291 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 85% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.