↓ Skip to main content

Use of RNA sequencing to evaluate rheumatic disease patients

Overview of attention for article published in Arthritis Research & Therapy, July 2015
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 (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
10 X users

Citations

dimensions_citation
24 Dimensions

Readers on

mendeley
84 Mendeley
citeulike
1 CiteULike
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
Use of RNA sequencing to evaluate rheumatic disease patients
Published in
Arthritis Research & Therapy, July 2015
DOI 10.1186/s13075-015-0677-3
Pubmed ID
Authors

Eugenia G Giannopoulou, Olivier Elemento, Lionel B Ivashkiv

Abstract

Studying the factors that control gene expression is of substantial importance for rheumatic diseases with poorly understood etiopathogenesis. In the past, gene expression microarrays have been used to measure transcript abundance on a genome-wide scale in a particular cell, tissue or organ. Microarray analysis has led to gene signatures that differentiate rheumatic diseases, and stages of a disease, as well as response to treatments. Nowadays, however, with the advent of next-generation sequencing methods, massive parallel sequencing of RNA tends to be the technology of choice for gene expression profiling, due to several advantages over microarrays, as well as for the detection of non-coding transcripts and alternative splicing events. In this review, we describe how RNA sequencing enables unbiased interrogation of the abundance and complexity of the transcriptome, and present a typical experimental workflow and bioinformatics tools that are often used for RNA sequencing analysis. We also discuss different uses of this next-generation sequencing technology to evaluate rheumatic disease patients and investigate the pathogenesis of rheumatic diseases such as rheumatoid arthritis, systemic lupus erythematosus, juvenile idiopathic arthritis and Sjögren's syndrome.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Unknown 83 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 18%
Student > Ph. D. Student 13 15%
Student > Bachelor 11 13%
Student > Master 10 12%
Student > Doctoral Student 7 8%
Other 15 18%
Unknown 13 15%
Readers by discipline Count As %
Medicine and Dentistry 27 32%
Biochemistry, Genetics and Molecular Biology 13 15%
Agricultural and Biological Sciences 12 14%
Immunology and Microbiology 10 12%
Computer Science 2 2%
Other 6 7%
Unknown 14 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 07 July 2015.
All research outputs
#6,298,484
of 25,373,627 outputs
Outputs from Arthritis Research & Therapy
#1,371
of 3,381 outputs
Outputs of similar age
#67,103
of 277,610 outputs
Outputs of similar age from Arthritis Research & Therapy
#24
of 66 outputs
Altmetric has tracked 25,373,627 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 3,381 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 59% 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 277,610 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 75% of its contemporaries.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.