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A gene expression microarray for Nicotiana benthamiana based on de novo transcriptome sequence assembly

Overview of attention for article published in Plant Methods, May 2016
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  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

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Title
A gene expression microarray for Nicotiana benthamiana based on de novo transcriptome sequence assembly
Published in
Plant Methods, May 2016
DOI 10.1186/s13007-016-0128-4
Pubmed ID
Authors

Michal Goralski, Paula Sobieszczanska, Aleksandra Obrepalska-Steplowska, Aleksandra Swiercz, Agnieszka Zmienko, Marek Figlerowicz

Abstract

Nicotiana benthamiana has been widely used in laboratories around the world for studying plant-pathogen interactions and posttranscriptional gene expression silencing. Yet the exploration of its transcriptome has lagged behind due to the lack of both adequate sequence information and genome-wide analysis tools, such as DNA microarrays. Despite the increasing use of high-throughput sequencing technologies, the DNA microarrays still remain a popular gene expression tool, because they are cheaper and less demanding regarding bioinformatics skills and computational effort. We designed a gene expression microarray with 103,747 60-mer probes, based on two recently published versions of N. benthamiana transcriptome (v.3 and v.5). Both versions were reconstructed from RNA-Seq data of non-strand-specific pooled-tissue libraries, so we defined the sense strand of the contigs prior to designing the probe. To accomplish this, we combined a homology search against Arabidopsis thaliana proteins and hybridization to a test 244k microarray containing pairs of probes, which represented individual contigs. We identified the sense strand in 106,684 transcriptome contigs and used this information to design an Nb-105k microarray on an Agilent eArray platform. Following hybridization of RNA samples from N. benthamiana roots and leaves we demonstrated that the new microarray had high specificity and sensitivity for detection of differentially expressed transcripts. We also showed that the data generated with the Nb-105k microarray may be used to identify incorrectly assembled contigs in the v.5 transcriptome, by detecting inconsistency in the gene expression profiles, which is indicated using multiple microarray probes that match the same v.5 primary transcripts. We provided a complete design of an oligonucleotide microarray that may be applied to the research of N. benthamiana transcriptome. This, in turn, will allow the N. benthamiana research community to take full advantage of microarray capabilities for studying gene expression in this plant. Additionally, by defining the sense orientation of over 106,000 contigs, we substantially improved the functional information on the N. benthamiana transcriptome. The simple hybridization-based approach for detecting the sense orientation of computationally assembled sequences can be used for updating the transcriptomes of other non-model organisms, including cases where no significant homology to known proteins exists.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 7%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 37%
Student > Ph. D. Student 7 23%
Professor > Associate Professor 2 7%
Student > Master 2 7%
Librarian 1 3%
Other 3 10%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 37%
Biochemistry, Genetics and Molecular Biology 7 23%
Engineering 2 7%
Chemical Engineering 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 7 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 May 2016.
All research outputs
#8,261,756
of 25,373,627 outputs
Outputs from Plant Methods
#544
of 1,262 outputs
Outputs of similar age
#122,242
of 348,653 outputs
Outputs of similar age from Plant Methods
#5
of 11 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 1,262 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 55% 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 348,653 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 63% of its contemporaries.
We're also able to compare this research output to 11 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 54% of its contemporaries.