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AgriSeqDB: an online RNA-Seq database for functional studies of agriculturally relevant plant species

Overview of attention for article published in BMC Plant Biology, September 2018
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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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

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2 blogs
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10 X users

Citations

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18 Dimensions

Readers on

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56 Mendeley
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Title
AgriSeqDB: an online RNA-Seq database for functional studies of agriculturally relevant plant species
Published in
BMC Plant Biology, September 2018
DOI 10.1186/s12870-018-1406-2
Pubmed ID
Authors

Andrew J. Robinson, Muluneh Tamiru, Rachel Salby, Clayton Bolitho, Andrew Williams, Simon Huggard, Eva Fisch, Kathryn Unsworth, James Whelan, Mathew G. Lewsey

Abstract

The genome-wide expression profile of genes in different tissues/cell types and developmental stages is a vital component of many functional genomic studies. Transcriptome data obtained by RNA-sequencing (RNA-Seq) is often deposited in public databases that are made available via data portals. Data visualization is one of the first steps in assessment and hypothesis generation. However, these databases do not typically include visualization tools and establishing one is not trivial for users who are not computational experts. This, as well as the various formats in which data is commonly deposited, makes the processes of data access, sharing and utility more difficult. Our goal was to provide a simple and user-friendly repository that meets these needs for data-sets from major agricultural crops. AgriSeqDB ( https://expression.latrobe.edu.au/agriseqdb ) is a database for viewing, analysing and interpreting developmental and tissue/cell-specific transcriptome data from several species, including major agricultural crops such as wheat, rice, maize, barley and tomato. The disparate manner in which public transcriptome data is often warehoused and the challenge of visualizing raw data are both major hurdles to data reuse. The popular eFP browser does an excellent job of presenting transcriptome data in an easily interpretable view, but previous implementation has been mostly on a case-by-case basis. Here we present an integrated visualisation database of transcriptome data-sets from six species that did not previously have public-facing visualisations. We combine the eFP browser, for gene-by-gene investigation, with the Degust browser, which enables visualisation of all transcripts across multiple samples. The two visualisation interfaces launch from the same point, enabling users to easily switch between analysis modes. The tools allow users, even those without bioinformatics expertise, to mine into data-sets and understand the behaviour of transcripts of interest across samples and time. We have also incorporated an additional graphic download option to simplify incorporation into presentations or publications. Powered by eFP and Degust browsers, AgriSeqDB is a quick and easy-to-use platform for data analysis and visualization in five crops and Arabidopsis. Furthermore, it provides a tool that makes it easy for researchers to share their data-sets, promoting research collaborations and data-set reuse.

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

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Researcher 9 16%
Student > Doctoral Student 6 11%
Student > Master 4 7%
Student > Bachelor 3 5%
Other 8 14%
Unknown 14 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 41%
Biochemistry, Genetics and Molecular Biology 8 14%
Unspecified 3 5%
Environmental Science 1 2%
Business, Management and Accounting 1 2%
Other 3 5%
Unknown 17 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 08 October 2018.
All research outputs
#1,737,440
of 23,321,213 outputs
Outputs from BMC Plant Biology
#66
of 3,315 outputs
Outputs of similar age
#38,766
of 342,597 outputs
Outputs of similar age from BMC Plant Biology
#1
of 79 outputs
Altmetric has tracked 23,321,213 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,315 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done particularly well, scoring higher than 98% 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 342,597 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 88% of its contemporaries.
We're also able to compare this research output to 79 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 99% of its contemporaries.