↓ Skip to main content

Short reads from honey bee (Apis sp.) sequencing projects reflect microbial associate diversity

Overview of attention for article published in PeerJ, July 2017
Altmetric Badge

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)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

blogs
1 blog
twitter
22 X users
facebook
1 Facebook page

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
90 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Short reads from honey bee (Apis sp.) sequencing projects reflect microbial associate diversity
Published in
PeerJ, July 2017
DOI 10.7717/peerj.3529
Pubmed ID
Authors

Michael Gerth, Gregory D.D. Hurst

Abstract

High throughput (or 'next generation') sequencing has transformed most areas of biological research and is now a standard method that underpins empirical study of organismal biology, and (through comparison of genomes), reveals patterns of evolution. For projects focused on animals, these sequencing methods do not discriminate between the primary target of sequencing (the animal genome) and 'contaminating' material, such as associated microbes. A common first step is to filter out these contaminants to allow better assembly of the animal genome or transcriptome. Here, we aimed to assess if these 'contaminations' provide information with regard to biologically important microorganisms associated with the individual. To achieve this, we examined whether the short read data from Apis retrieved elements of its well established microbiome. To this end, we screened almost 1,000 short read libraries of honey bee (Apis sp.) DNA sequencing project for the presence of microbial sequences, and find sequences from known honey bee microbial associates in at least 11% of them. Further to this, we screened ∼500 Apis RNA sequencing libraries for evidence of viral infections, which were found to be present in about half of them. We then used the data to reconstruct draft genomes of three Apis associated bacteria, as well as several viral strains de novo. We conclude that 'contamination' in short read sequencing libraries can provide useful genomic information on microbial taxa known to be associated with the target organisms, and may even lead to the discovery of novel associations. Finally, we demonstrate that RNAseq samples from experiments commonly carry uneven viral loads across libraries. We note variation in viral presence and load may be a confounding feature of differential gene expression analyses, and as such it should be incorporated as a random factor in analyses.

X Demographics

X Demographics

The data shown below were collected from the profiles of 22 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 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 90 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 32%
Researcher 14 16%
Student > Master 12 13%
Student > Bachelor 10 11%
Professor > Associate Professor 4 4%
Other 12 13%
Unknown 9 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 37%
Biochemistry, Genetics and Molecular Biology 32 36%
Environmental Science 3 3%
Immunology and Microbiology 2 2%
Engineering 2 2%
Other 7 8%
Unknown 11 12%
Attention Score in Context

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 23 July 2017.
All research outputs
#1,746,752
of 25,332,933 outputs
Outputs from PeerJ
#1,840
of 15,089 outputs
Outputs of similar age
#32,745
of 318,592 outputs
Outputs of similar age from PeerJ
#55
of 342 outputs
Altmetric has tracked 25,332,933 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 15,089 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.1. This one has done well, scoring higher than 87% 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 318,592 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 342 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.