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Identification of natural antimicrobial peptides from bacteria through metagenomic and metatranscriptomic analysis of high-throughput transcriptome data of Taiwanese oolong teas

Overview of attention for article published in BMC Systems Biology, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters

Citations

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10 Dimensions

Readers on

mendeley
79 Mendeley
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Title
Identification of natural antimicrobial peptides from bacteria through metagenomic and metatranscriptomic analysis of high-throughput transcriptome data of Taiwanese oolong teas
Published in
BMC Systems Biology, December 2017
DOI 10.1186/s12918-017-0503-4
Pubmed ID
Authors

Kai-Yao Huang, Tzu-Hao Chang, Jhih-Hua Jhong, Yu-Hsiang Chi, Wen-Chi Li, Chien-Lung Chan, K. Robert Lai, Tzong-Yi Lee

Abstract

Anti-microbial peptides (AMPs), naturally encoded by genes and generally containing 12-100 amino acids, are crucial components of the innate immune system and can protect the host from various pathogenic bacteria and viruses. In recent years, the widespread use of antibiotics has resulted in the rapid growth of antibiotic-resistant microorganisms that often induce critical infection and pathogenesis. Recently, the advent of high-throughput technologies has led molecular biology into a data surge in both the amount and scope of data. For instance, next-generation sequencing technology has been applied to generate large-scale sequencing reads from foods, water, soil, air, and specimens to identify microbiota and their functions based on metagenomics and metatranscriptomics, respectively. In addition, oolong tea is partially fermented and is the most widely produced tea in Taiwan. Many studies have shown the benefits of oolong tea in inhibiting obesity, reducing dental plaque deposition, antagonizing allergic immune responses, and alleviating the effects of aging. However, the microbes and their functions present in oolong tea remain unknown. To understand the relationship between Taiwanese oolong teas and bacterial communities, we designed a novel bioinformatics scheme to identify AMPs and their functional types based on metagenomics and metatranscriptomic analysis of high-throughput transcriptome data. Four types of oolong teas (Dayuling tea, Alishan tea, Jinxuan tea, and Oriental Beauty tea) were subjected to 16S ribosomal DNA and total RNA extraction and sequencing. Metagenomics analysis results revealed that Oriental Beauty tea exhibited greater bacterial diversity than other teas. The most common bacterial families across all tea types were Bacteroidaceae (21.7%), Veillonellaceae (22%), and Fusobacteriaceae (12.3%). Metatranscriptomics analysis results revealed that the dominant bacteria species across all tea types were Escherichia coli, Bacillus subtilis, and Chryseobacterium sp. StRB126, which were subjected to further functional analysis. A total of 8194 (6.5%), 26,220 (6.1%), 5703 (5.8%), and 106,183 (7.8%) reads could be mapped to AMPs. We found that the distribution of anti-gram-positive and anti-gram-negative AMPs is highly correlated with the distribution of gram-positive and gram-negative bacteria in Taiwanese oolong tea samples.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 19%
Researcher 12 15%
Student > Bachelor 12 15%
Student > Master 11 14%
Lecturer > Senior Lecturer 3 4%
Other 11 14%
Unknown 15 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 24%
Biochemistry, Genetics and Molecular Biology 15 19%
Medicine and Dentistry 6 8%
Immunology and Microbiology 5 6%
Chemistry 4 5%
Other 10 13%
Unknown 20 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 January 2018.
All research outputs
#1,785,239
of 12,358,022 outputs
Outputs from BMC Systems Biology
#91
of 1,047 outputs
Outputs of similar age
#70,920
of 355,258 outputs
Outputs of similar age from BMC Systems Biology
#10
of 53 outputs
Altmetric has tracked 12,358,022 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,047 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 91% 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 355,258 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 79% of its contemporaries.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.