↓ Skip to main content

Exploring the Shallow End; Estimating Information Content in Transcriptomics Studies

Overview of attention for article published in Frontiers in Plant Science, January 2012
Altmetric Badge

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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
14 X users
googleplus
1 Google+ user
f1000
1 research highlight platform

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
78 Mendeley
citeulike
3 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
Exploring the Shallow End; Estimating Information Content in Transcriptomics Studies
Published in
Frontiers in Plant Science, January 2012
DOI 10.3389/fpls.2012.00213
Pubmed ID
Authors

Daniel J. Kliebenstein

Abstract

Transcriptomics is a major platform to study organismal biology. The advent of new parallel sequencing technologies has opened up a new avenue of transcriptomics with ever deeper and deeper sequencing to identify and quantify each and every transcript in a sample. However, this may not be the best usage of the parallel sequencing technology for all transcriptomics experiments. I utilized the Shannon Entropy approach to estimate the information contained within a transcriptomics experiment and tested the ability of shallow RNAseq to capture the majority of this information. This analysis showed that it was possible to capture nearly all of the network or genomic information present in a variety of transcriptomics experiments using a subset of the most abundant 5000 transcripts or less within any given sample. Thus, it appears that it should be possible and affordable to conduct large scale factorial analysis with a high degree of replication using parallel sequencing technologies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 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 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 5 6%
United States 5 6%
United Kingdom 1 1%
Italy 1 1%
Slovenia 1 1%
Canada 1 1%
Unknown 64 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 36%
Student > Ph. D. Student 17 22%
Student > Master 6 8%
Student > Doctoral Student 5 6%
Student > Postgraduate 5 6%
Other 14 18%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 73%
Biochemistry, Genetics and Molecular Biology 5 6%
Engineering 3 4%
Unspecified 2 3%
Computer Science 2 3%
Other 6 8%
Unknown 3 4%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 24 August 2022.
All research outputs
#2,091,170
of 24,598,501 outputs
Outputs from Frontiers in Plant Science
#838
of 23,345 outputs
Outputs of similar age
#15,227
of 253,076 outputs
Outputs of similar age from Frontiers in Plant Science
#7
of 196 outputs
Altmetric has tracked 24,598,501 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 23,345 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 96% 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 253,076 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 93% of its contemporaries.
We're also able to compare this research output to 196 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.