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VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology

Overview of attention for article published in BMC Bioinformatics, February 2017
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Mentioned by

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2 tweeters

Citations

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28 Dimensions

Readers on

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67 Mendeley
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Title
VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1540-0
Pubmed ID
Authors

Muhammad Rizwan, Anam Naz, Jamil Ahmad, Kanwal Naz, Ayesha Obaid, Tamsila Parveen, Muhammad Ahsan, Amjad Ali

Abstract

With advances in reverse vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. Reverse vaccinology has changed the way of discovery and provides a mean to propose target identification in reduced time and labour. In this regard, high throughput genomic sequencing technologies and supporting bioinformatics tools have greatly facilitated the prompt analysis of pathogens, where various predicted candidates have been found effective against certain infections and diseases. A pipeline, VacSol, is designed here based on a similar approach to predict putative vaccine candidates both rapidly and efficiently. VacSol, a new pipeline introduced here, is a highly scalable, multi-mode, and configurable software designed to automate the high throughput in silico vaccine candidate prediction process for the identification of putative vaccine candidates against the proteome of bacterial pathogens. Vaccine candidates are screened using integrated, well-known and robust algorithms/tools for proteome analysis, and the results from the VacSol software are presented in five different formats by taking proteome sequence as input in FASTA file format. The utility of VacSol is tested and compared with published data and using the Helicobacter pylori 26695 reference strain as a benchmark. VacSol rapidly and efficiently screens the whole bacterial pathogen proteome to identify a few predicted putative vaccine candidate proteins. This pipeline has the potential to save computational costs and time by efficiently reducing false positive candidate hits. VacSol results do not depend on any universal set of rules and may vary based on the provided input. It is freely available to download from: https://sourceforge.net/projects/vacsol/ .

Twitter Demographics

The data shown below were collected from the profiles of 2 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 67 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 19 28%
Student > Master 17 25%
Student > Ph. D. Student 7 10%
Student > Postgraduate 3 4%
Researcher 3 4%
Other 6 9%
Unknown 12 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 31%
Agricultural and Biological Sciences 16 24%
Immunology and Microbiology 6 9%
Medicine and Dentistry 3 4%
Computer Science 2 3%
Other 0 0%
Unknown 19 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 May 2019.
All research outputs
#9,217,768
of 15,705,594 outputs
Outputs from BMC Bioinformatics
#3,496
of 5,693 outputs
Outputs of similar age
#135,214
of 261,652 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 24 outputs
Altmetric has tracked 15,705,594 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,693 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
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 261,652 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.