↓ Skip to main content

A Hidden Markov Model for identifying essential and growth-defect regions in bacterial genomes from transposon insertion sequencing data

Overview of attention for article published in BMC Bioinformatics, October 2013
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 (80th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
5 tweeters
patent
1 patent
f1000
1 research highlight platform

Citations

dimensions_citation
60 Dimensions

Readers on

mendeley
114 Mendeley
citeulike
1 CiteULike
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
A Hidden Markov Model for identifying essential and growth-defect regions in bacterial genomes from transposon insertion sequencing data
Published in
BMC Bioinformatics, October 2013
DOI 10.1186/1471-2105-14-303
Pubmed ID
Authors

Michael A DeJesus, Thomas R Ioerger

Abstract

Knowledge of which genes are essential to the survival of an organism is critical to understanding the function of genes, and for the identification of potential drug targets for antimicrobial treatment. Previous statistical methods for assessing essentiality based on sequencing of tranposon libraries have usually limited their assessment to strict 'essential' or 'non-essential' categories. However, this binary view of essentiality does not accurately represent the more nuanced ways the growth of an organism might be affected by the disruption of its genes. In addition, these methods often limit their analysis to open-reading frames. We propose a novel method for analyzing sequence data from transposon mutant libraries using a Hidden Markov Model (HMM), along with formulas to adapt the parameters of the model to different datasets for robustness. This approach allows for the clustering of insertion sites into distinct regions of essentiality across the entire genome in a statistically rigorous manner, while also allowing for the detection of growth-defect and growth-advantage regions.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Argentina 1 <1%
Belgium 1 <1%
Canada 1 <1%
Unknown 111 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 32%
Researcher 26 23%
Student > Master 14 12%
Student > Bachelor 8 7%
Other 4 4%
Other 14 12%
Unknown 12 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 38%
Biochemistry, Genetics and Molecular Biology 25 22%
Immunology and Microbiology 9 8%
Computer Science 7 6%
Unspecified 3 3%
Other 10 9%
Unknown 17 15%

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 07 November 2019.
All research outputs
#3,411,758
of 17,351,915 outputs
Outputs from BMC Bioinformatics
#1,457
of 6,150 outputs
Outputs of similar age
#35,122
of 177,604 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 28 outputs
Altmetric has tracked 17,351,915 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,150 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 76% 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 177,604 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 80% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.