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BigQ: a NoSQL based framework to handle genomic variants in i2b2

Overview of attention for article published in BMC Bioinformatics, December 2015
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

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12 X users
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1 Facebook page
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1 Google+ user

Citations

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26 Dimensions

Readers on

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79 Mendeley
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2 CiteULike
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Title
BigQ: a NoSQL based framework to handle genomic variants in i2b2
Published in
BMC Bioinformatics, December 2015
DOI 10.1186/s12859-015-0861-0
Pubmed ID
Authors

Matteo Gabetta, Ivan Limongelli, Ettore Rizzo, Alberto Riva, Daniele Segagni, Riccardo Bellazzi

Abstract

Precision medicine requires the tight integration of clinical and molecular data. To this end, it is mandatory to define proper technological solutions able to manage the overwhelming amount of high throughput genomic data needed to test associations between genomic signatures and human phenotypes. The i2b2 Center (Informatics for Integrating Biology and the Bedside) has developed a widely internationally adopted framework to use existing clinical data for discovery research that can help the definition of precision medicine interventions when coupled with genetic data. i2b2 can be significantly advanced by designing efficient management solutions of Next Generation Sequencing data. We developed BigQ, an extension of the i2b2 framework, which integrates patient clinical phenotypes with genomic variant profiles generated by Next Generation Sequencing. A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations. We report an evaluation of the query performance of our system on more than 11 million variants, showing that the implemented solution scales linearly in terms of query time and disk space with the number of variants. In this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data. The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations.

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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 25%
Researcher 16 20%
Other 5 6%
Professor 5 6%
Student > Master 5 6%
Other 12 15%
Unknown 16 20%
Readers by discipline Count As %
Computer Science 28 35%
Agricultural and Biological Sciences 11 14%
Biochemistry, Genetics and Molecular Biology 7 9%
Engineering 4 5%
Medicine and Dentistry 3 4%
Other 7 9%
Unknown 19 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 09 January 2016.
All research outputs
#3,730,556
of 22,836,570 outputs
Outputs from BMC Bioinformatics
#1,404
of 7,288 outputs
Outputs of similar age
#64,689
of 392,772 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 141 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,288 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 80% 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 392,772 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 83% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.