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A de novo approach to disentangle partner identity and function in holobiont systems

Overview of attention for article published in Microbiome, June 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
A de novo approach to disentangle partner identity and function in holobiont systems
Published in
Microbiome, June 2018
DOI 10.1186/s40168-018-0481-9
Pubmed ID
Authors

Arnaud Meng, Camille Marchet, Erwan Corre, Pierre Peterlongo, Adriana Alberti, Corinne Da Silva, Patrick Wincker, Eric Pelletier, Ian Probert, Johan Decelle, Stéphane Le Crom, Fabrice Not, Lucie Bittner

Abstract

Study of meta-transcriptomic datasets involving non-model organisms represents bioinformatic challenges. The production of chimeric sequences and our inability to distinguish the taxonomic origins of the sequences produced are inherent and recurrent difficulties in de novo assembly analyses. As the study of holobiont meta-transcriptomes is affected by challenges invoked above, we propose an innovative bioinformatic approach to tackle such difficulties and tested it on marine models as a proof of concept. We considered three holobiont models, of which two transcriptomes were previously published and a yet unpublished transcriptome, to analyze and sort their raw reads using Short Read Connector, a k-mer based similarity method. Before assembly, we thus defined four distinct categories for each holobiont meta-transcriptome: host reads, symbiont reads, shared reads, and unassigned reads. Afterwards, we observed that independent de novo assemblies for each category led to a diminution of the number of chimeras compared to classical assembly methods. Moreover, the separation of each partner's transcriptome offered the independent and comparative exploration of their functional diversity in the holobiont. Finally, our strategy allowed to propose new functional annotations for two well-studied holobionts (a Cnidaria-Dinophyta, a Porifera-Bacteria) and a first meta-transcriptome from a planktonic Radiolaria-Dinophyta system forming widespread symbiotic association for which our knowledge is considerably limited. In contrast to classical assembly approaches, our bioinformatic strategy generates less de novo assembled chimera and allows biologists to study separately host and symbiont data from a holobiont mixture. The pre-assembly separation of reads using an efficient tool as Short Read Connector is an effective way to tackle meta-transcriptomic challenges and offers bright perpectives to study holobiont systems composed of either well-studied or poorly characterized symbiotic lineages and ultimately expand our knowledge about these associations.

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The data shown below were collected from the profiles of 27 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 28%
Researcher 14 16%
Student > Master 11 13%
Student > Bachelor 6 7%
Professor > Associate Professor 5 6%
Other 12 14%
Unknown 14 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 42%
Biochemistry, Genetics and Molecular Biology 14 16%
Environmental Science 6 7%
Immunology and Microbiology 4 5%
Computer Science 2 2%
Other 6 7%
Unknown 18 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 12 July 2019.
All research outputs
#2,246,177
of 24,846,849 outputs
Outputs from Microbiome
#908
of 1,698 outputs
Outputs of similar age
#45,874
of 334,934 outputs
Outputs of similar age from Microbiome
#36
of 54 outputs
Altmetric has tracked 24,846,849 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,698 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.7. This one is in the 46th percentile – i.e., 46% 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 334,934 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 86% of its contemporaries.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.