↓ Skip to main content

StrainSeeker: fast identification of bacterial strains from raw sequencing reads using user-provided guide trees

Overview of attention for article published in PeerJ, May 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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
39 X users
facebook
1 Facebook page

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
136 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
StrainSeeker: fast identification of bacterial strains from raw sequencing reads using user-provided guide trees
Published in
PeerJ, May 2017
DOI 10.7717/peerj.3353
Pubmed ID
Authors

Märt Roosaare, Mihkel Vaher, Lauris Kaplinski, Märt Möls, Reidar Andreson, Maarja Lepamets, Triinu Kõressaar, Paul Naaber, Siiri Kõljalg, Maido Remm

Abstract

Fast, accurate and high-throughput identification of bacterial isolates is in great demand. The present work was conducted to investigate the possibility of identifying isolates from unassembled next-generation sequencing reads using custom-made guide trees. A tool named StrainSeeker was developed that constructs a list of specific k-mers for each node of any given Newick-format tree and enables the identification of bacterial isolates in 1-2 min. It uses a novel algorithm, which analyses the observed and expected fractions of node-specific k-mers to test the presence of each node in the sample. This allows StrainSeeker to determine where the isolate branches off the guide tree and assign it to a clade whereas other tools assign each read to a reference genome. Using a dataset of 100 Escherichia coli isolates, we demonstrate that StrainSeeker can predict the clades of E. coli with 92% accuracy and correct tree branch assignment with 98% accuracy. Twenty-five thousand Illumina HiSeq reads are sufficient for identification of the strain. StrainSeeker is a software program that identifies bacterial isolates by assigning them to nodes or leaves of a custom-made guide tree. StrainSeeker's web interface and pre-computed guide trees are available at http://bioinfo.ut.ee/strainseeker. Source code is stored at GitHub: https://github.com/bioinfo-ut/StrainSeeker.

X Demographics

X Demographics

The data shown below were collected from the profiles of 39 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 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
France 1 <1%
Egypt 1 <1%
Estonia 1 <1%
United States 1 <1%
Unknown 130 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 24%
Student > Ph. D. Student 30 22%
Student > Bachelor 15 11%
Student > Master 12 9%
Student > Postgraduate 7 5%
Other 19 14%
Unknown 21 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 28%
Biochemistry, Genetics and Molecular Biology 29 21%
Computer Science 14 10%
Immunology and Microbiology 8 6%
Medicine and Dentistry 6 4%
Other 11 8%
Unknown 30 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 24 January 2018.
All research outputs
#1,322,251
of 24,885,505 outputs
Outputs from PeerJ
#1,385
of 14,829 outputs
Outputs of similar age
#25,995
of 318,826 outputs
Outputs of similar age from PeerJ
#43
of 359 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,829 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.0. This one has done particularly well, scoring higher than 90% 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 318,826 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 359 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.