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A bioinformatics approach to distinguish plant parasite and host transcriptomes in interface tissue by classifying RNA-Seq reads

Overview of attention for article published in Plant Methods, May 2015
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  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Title
A bioinformatics approach to distinguish plant parasite and host transcriptomes in interface tissue by classifying RNA-Seq reads
Published in
Plant Methods, May 2015
DOI 10.1186/s13007-015-0066-6
Pubmed ID
Authors

Daisuke Ikeue, Christian Schudoma, Wenna Zhang, Yoshiyuki Ogata, Tomoaki Sakamoto, Tetsuya Kurata, Takeshi Furuhashi, Friedrich Kragler, Koh Aoki

Abstract

The genus Cuscuta is a group of parasitic plants that are distributed world-wide. The process of parasitization starts with a Cuscuta plant coiling around the host stem. The parasite's haustorial organs then establish a vascular connection allowing for access to the phloem content. The host and the parasite form new cellular connections, suggesting coordination of developmental and biochemical processes. Simultaneous monitoring of gene expression in the parasite's and host's tissues may shed light on the complex events occurring between the parasitic and host cells and may help to overcome experimental limitations (i.e. how to separate host tissue from Cuscuta tissue at the haustorial connection). A novel approach is to use bioinformatic analysis to classify sequencing reads as either belonging to the host or to the parasite and to characterize the expression patterns. Owing to the lack of a comprehensive genomic dataset from Cuscuta spp., such a classification has not been performed previously. We first classified RNA-Seq reads from an interface region between the non-model parasitic plant Cuscuta japonica and the non-model host plant Impatiens balsamina. Without established reference sequences, we classified reads as originating from either of the plants by stepwise similarity search against de novo assembled transcript sets of C. japonica and I. balsamina, unigene sets of the same genus, and cDNA sequences of the same family. We then assembled de novo transcriptomes from the classified read sets. We assessed the quality of the classification by mapping reads to contigs of both plants, achieving a misclassification rate low enough (0.22-0.39%) to be used reliably for differential gene expression analysis. Finally, we applied our read classification method to RNA-Seq data from the interface between the non-model parasitic plant C. japonica and the model host plant Glycine max. Analysis of gene expression profiles at 5 parasitizing stages revealed differentially expressed genes from both C. japonica and G. max, and uncovered the coordination of cellular processes between the two plants. We demonstrated that reliable identification of differentially expressed transcripts in undissected interface region of the parasite-host association is feasible and informative with respect to differential-expression patterns.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 53 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 20%
Student > Master 9 17%
Researcher 7 13%
Student > Doctoral Student 6 11%
Student > Bachelor 4 7%
Other 3 6%
Unknown 14 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 46%
Biochemistry, Genetics and Molecular Biology 11 20%
Computer Science 2 4%
Chemical Engineering 1 2%
Chemistry 1 2%
Other 1 2%
Unknown 13 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 09 June 2015.
All research outputs
#6,391,095
of 23,567,572 outputs
Outputs from Plant Methods
#384
of 1,120 outputs
Outputs of similar age
#73,446
of 265,052 outputs
Outputs of similar age from Plant Methods
#5
of 14 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,120 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has gotten more attention than average, scoring higher than 65% 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 265,052 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 71% of its contemporaries.
We're also able to compare this research output to 14 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 64% of its contemporaries.