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Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality

Overview of attention for article published in Microbiome, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality
Published in
Microbiome, January 2016
DOI 10.1186/s40168-015-0146-x
Pubmed ID
Authors

Yue Jiang, Xuejian Xiong, Jayne Danska, John Parkinson

Abstract

Metatranscriptomics is emerging as a powerful technology for the functional characterization of complex microbial communities (microbiomes). Use of unbiased RNA-sequencing can reveal both the taxonomic composition and active biochemical functions of a complex microbial community. However, the lack of established reference genomes, computational tools and pipelines make analysis and interpretation of these datasets challenging. Systematic studies that compare data across microbiomes are needed to demonstrate the ability of such pipelines to deliver biologically meaningful insights on microbiome function. Here, we apply a standardized analytical pipeline to perform a comparative analysis of metatranscriptomic data from diverse microbial communities derived from mouse large intestine, cow rumen, kimchi culture, deep-sea thermal vent and permafrost. Sequence similarity searches allowed annotation of 19 to 76 % of putative messenger RNA (mRNA) reads, with the highest frequency in the kimchi dataset due to its relatively low complexity and availability of closely related reference genomes. Metatranscriptomic datasets exhibited distinct taxonomic and functional signatures. From a metabolic perspective, we identified a common core of enzymes involved in amino acid, energy and nucleotide metabolism and also identified microbiome-specific pathways such as phosphonate metabolism (deep sea) and glycan degradation pathways (cow rumen). Integrating taxonomic and functional annotations within a novel visualization framework revealed the contribution of different taxa to metabolic pathways, allowing the identification of taxa that contribute unique functions. The application of a single, standard pipeline confirms that the rich taxonomic and functional diversity observed across microbiomes is not simply an artefact of different analysis pipelines but instead reflects distinct environmental influences. At the same time, our findings show how microbiome complexity and availability of reference genomes can impact comprehensive annotation of metatranscriptomes. Consequently, beyond the application of standardized pipelines, additional caution must be taken when interpreting their output and performing downstream, microbiome-specific, analyses. The pipeline used in these analyses along with a tutorial has been made freely available for download from our project website: http://www.compsysbio.org/microbiome .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 412 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 2%
France 3 <1%
Canada 2 <1%
Norway 1 <1%
Italy 1 <1%
India 1 <1%
Netherlands 1 <1%
Mexico 1 <1%
Brazil 1 <1%
Other 0 0%
Unknown 393 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 102 25%
Researcher 74 18%
Student > Master 68 17%
Student > Bachelor 27 7%
Student > Doctoral Student 25 6%
Other 64 16%
Unknown 52 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 140 34%
Biochemistry, Genetics and Molecular Biology 84 20%
Environmental Science 38 9%
Immunology and Microbiology 21 5%
Computer Science 16 4%
Other 39 9%
Unknown 74 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 21 January 2016.
All research outputs
#5,031,395
of 24,058,913 outputs
Outputs from Microbiome
#1,312
of 1,589 outputs
Outputs of similar age
#84,583
of 403,254 outputs
Outputs of similar age from Microbiome
#23
of 35 outputs
Altmetric has tracked 24,058,913 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,589 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.5. This one is in the 17th percentile – i.e., 17% 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 403,254 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 78% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.