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Positional RNA-Seq identifies candidate genes for phenotypic engineering of sexual traits

Overview of attention for article published in Frontiers in Zoology, July 2015
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  • Good Attention Score compared to outputs of the same age (71st percentile)

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5 tweeters


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44 Mendeley
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Positional RNA-Seq identifies candidate genes for phenotypic engineering of sexual traits
Published in
Frontiers in Zoology, July 2015
DOI 10.1186/s12983-015-0106-0
Pubmed ID

Roberto Arbore, Kiyono Sekii, Christian Beisel, Peter Ladurner, Eugene Berezikov, Lukas Schärer


RNA interference (RNAi) of trait-specific genes permits the manipulation of specific phenotypic traits ("phenotypic engineering") and thus represents a powerful tool to test trait function in evolutionary studies. The identification of suitable candidate genes, however, often relies on existing functional gene annotation, which is usually limited in emerging model organisms, especially when they are only distantly related to traditional genetic model organisms. A case in point is the free-living flatworm Macrostomum lignano (Lophotrochozoa: Platyhelminthes: Rhabditophora), an increasingly powerful model organism for evolutionary studies of sex in simultaneous hermaphrodites. To overcome the limitation of sparse functional annotation, we have performed a positional RNA-Seq analysis on different body fragments in order to identify organ-specific candidate transcripts. We then performed gene expression (in situ hybridization) and gene function (RNAi) analyses on 23 candidate transcripts, both to evaluate the predictive potential of this approach and to obtain preliminary functional characterizations of these candidate genes. We identified over 4000 transcripts that could be expected to show specific expression in different reproductive organs (including testis, ovary and the male and female genital systems). The predictive potential of the method could then be verified by confirming organ-specific expression for several candidate transcripts, some of which yielded interesting trait-specific knock-down phenotypes that can now be followed up in future phenotypic engineering studies. Our positional RNA-Seq analysis represents a highly useful resource for the identification of candidate transcripts for functional and phenotypic engineering studies in M. lignano, and it has already been used successfully in several studies. Moreover, this approach can overcome some inherent limitations of homology-based candidate selection and thus should be applicable to a broad range of emerging model organisms.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Cuba 1 2%
Brazil 1 2%
Unknown 42 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 27%
Student > Ph. D. Student 10 23%
Student > Master 9 20%
Student > Bachelor 3 7%
Professor > Associate Professor 2 5%
Other 3 7%
Unknown 5 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 59%
Biochemistry, Genetics and Molecular Biology 7 16%
Environmental Science 3 7%
Arts and Humanities 1 2%
Veterinary Science and Veterinary Medicine 1 2%
Other 0 0%
Unknown 6 14%

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 13 December 2019.
All research outputs
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Outputs from Frontiers in Zoology
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Outputs of similar age
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Outputs of similar age from Frontiers in Zoology
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Altmetric has tracked 16,384,408 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 545 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.9. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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 234,627 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them