↓ Skip to main content

Thinking BIG rheumatology: how to make functional genomics data work for you

Overview of attention for article published in Arthritis Research & Therapy, February 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
23 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Thinking BIG rheumatology: how to make functional genomics data work for you
Published in
Arthritis Research & Therapy, February 2018
DOI 10.1186/s13075-017-1504-9
Pubmed ID
Authors

Deborah R. Winter

Abstract

High-throughput sequencing assays have become an increasingly common part of biological research across multiple fields. Even as the resulting sequences pile up in public databases, it is not always obvious how to make use of these data sets. Functional genomics offers approaches to integrate these "big" data into our understanding of rheumatic diseases. This review aims to provide a primer on thinking about big data from functional genomics in the context of rheumatology, using examples from the field's literature as well as the author's own work to illustrate the execution of functional genomics research. Study design is crucial to ensure the right samples are used to address the question of interest. In addition, sequencing assays produce a variety of data types, from gene expression to 3D chromatin structure and single-cell technologies, that can be integrated into a model of the underlying gene regulatory networks. The best approach for this analysis uses the scientific process: bioinformatic methods should be used in an iterative, hypothesis-driven manner to uncover the disease mechanism. Finally, the future of functional genomics will see big data fully integrated into rheumatology, leading to computationally trained researchers and interactive databases. The goal of this review is not to provide a manual, but to enhance the familiarity of readers with functional genomic approaches and provide a better sense of the challenges and possibilities.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 17%
Other 3 13%
Student > Ph. D. Student 3 13%
Student > Master 3 13%
Lecturer 2 9%
Other 6 26%
Unknown 2 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 22%
Computer Science 3 13%
Neuroscience 3 13%
Nursing and Health Professions 2 9%
Medicine and Dentistry 2 9%
Other 3 13%
Unknown 5 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 27 September 2019.
All research outputs
#3,487,277
of 14,064,239 outputs
Outputs from Arthritis Research & Therapy
#867
of 2,269 outputs
Outputs of similar age
#103,348
of 361,318 outputs
Outputs of similar age from Arthritis Research & Therapy
#1
of 1 outputs
Altmetric has tracked 14,064,239 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,269 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has gotten more attention than average, scoring higher than 61% 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 361,318 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 1 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