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Mining, analyzing, and integrating viral signals from metagenomic data

Overview of attention for article published in Microbiome, March 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

twitter
45 tweeters

Citations

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34 Dimensions

Readers on

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169 Mendeley
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Title
Mining, analyzing, and integrating viral signals from metagenomic data
Published in
Microbiome, March 2019
DOI 10.1186/s40168-019-0657-y
Pubmed ID
Authors

Tingting Zheng, Jun Li, Yueqiong Ni, Kang, Maria-Anna Misiakou, Lejla Imamovic, Billy K. C. Chow, Anne A. Rode, Peter Bytzer, Morten Sommer, Gianni Panagiotou

Abstract

Viruses are important components of microbial communities modulating community structure and function; however, only a couple of tools are currently available for phage identification and analysis from metagenomic sequencing data. Here we employed the random forest algorithm to develop VirMiner, a web-based phage contig prediction tool especially sensitive for high-abundances phage contigs, trained and validated by paired metagenomic and phagenomic sequencing data from the human gut flora. VirMiner achieved 41.06% ± 17.51% sensitivity and 81.91% ± 4.04% specificity in the prediction of phage contigs. In particular, for the high-abundance phage contigs, VirMiner outperformed other tools (VirFinder and VirSorter) with much higher sensitivity (65.23% ± 16.94%) than VirFinder (34.63% ± 17.96%) and VirSorter (18.75% ± 15.23%) at almost the same specificity. Moreover, VirMiner provides the most comprehensive phage analysis pipeline which is comprised of metagenomic raw reads processing, functional annotation, phage contig identification, and phage-host relationship prediction (CRISPR-spacer recognition) and supports two-group comparison when the input (metagenomic sequence data) includes different conditions (e.g., case and control). Application of VirMiner to an independent cohort of human gut metagenomes obtained from individuals treated with antibiotics revealed that 122 KEGG orthology and 118 Pfam groups had significantly differential abundance in the pre-treatment samples compared to samples at the end of antibiotic administration, including clustered regularly interspaced short palindromic repeats (CRISPR), multidrug resistance, and protein transport. The VirMiner webserver is available at http://sbb.hku.hk/VirMiner/ . We developed a comprehensive tool for phage prediction and analysis for metagenomic samples. Compared to VirSorter and VirFinder-the most widely used tools-VirMiner is able to capture more high-abundance phage contigs which could play key roles in infecting bacteria and modulating microbial community dynamics. The European Union Clinical Trials Register, EudraCT Number: 2013-003378-28 . Registered on 9 April 2014.

Twitter Demographics

The data shown below were collected from the profiles of 45 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 169 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 169 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 24%
Student > Ph. D. Student 38 22%
Student > Master 23 14%
Student > Bachelor 12 7%
Student > Doctoral Student 11 7%
Other 17 10%
Unknown 28 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 42 25%
Agricultural and Biological Sciences 34 20%
Immunology and Microbiology 17 10%
Environmental Science 14 8%
Engineering 7 4%
Other 14 8%
Unknown 41 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 26 June 2019.
All research outputs
#939,959
of 15,329,228 outputs
Outputs from Microbiome
#345
of 877 outputs
Outputs of similar age
#28,591
of 265,759 outputs
Outputs of similar age from Microbiome
#1
of 1 outputs
Altmetric has tracked 15,329,228 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 877 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.9. This one has gotten more attention than average, scoring higher than 60% 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 265,759 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 89% 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