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Transcriptomics technologies

Overview of attention for article published in PLoS Computational Biology, May 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
1 policy source
twitter
36 X users
patent
1 patent
facebook
2 Facebook pages
wikipedia
8 Wikipedia pages
reddit
1 Redditor

Citations

dimensions_citation
780 Dimensions

Readers on

mendeley
2261 Mendeley
citeulike
2 CiteULike
Title
Transcriptomics technologies
Published in
PLoS Computational Biology, May 2017
DOI 10.1371/journal.pcbi.1005457
Pubmed ID
Authors

Rohan Lowe, Neil Shirley, Mark Bleackley, Stephen Dolan, Thomas Shafee

Abstract

Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst noncoding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. The first attempts to study the whole transcriptome began in the early 1990s, and technological advances since the late 1990s have made transcriptomics a widespread discipline. Transcriptomics has been defined by repeated technological innovations that transform the field. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to capture all sequences. Measuring the expression of an organism's genes in different tissues, conditions, or time points gives information on how genes are regulated and reveals details of an organism's biology. It can also help to infer the functions of previously unannotated genes. Transcriptomic analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human disease. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted assays.

Timeline
X Demographics

X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2,261 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 <1%
Australia 1 <1%
Brazil 1 <1%
Mexico 1 <1%
United States 1 <1%
Unknown 2256 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 393 17%
Student > Master 341 15%
Student > Bachelor 336 15%
Researcher 181 8%
Student > Doctoral Student 101 4%
Other 228 10%
Unknown 681 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 554 25%
Agricultural and Biological Sciences 423 19%
Medicine and Dentistry 102 5%
Immunology and Microbiology 68 3%
Engineering 55 2%
Other 302 13%
Unknown 757 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 46. 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 11 June 2024.
All research outputs
#964,199
of 26,567,854 outputs
Outputs from PLoS Computational Biology
#692
of 9,205 outputs
Outputs of similar age
#18,508
of 332,651 outputs
Outputs of similar age from PLoS Computational Biology
#21
of 153 outputs
Altmetric has tracked 26,567,854 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,205 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.2. This one has done particularly well, scoring higher than 92% 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 332,651 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.