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TOGGLE: toolbox for generic NGS analyses

Overview of attention for article published in BMC Bioinformatics, November 2015
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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17 X users
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1 Google+ user

Citations

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24 Dimensions

Readers on

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105 Mendeley
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9 CiteULike
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Title
TOGGLE: toolbox for generic NGS analyses
Published in
BMC Bioinformatics, November 2015
DOI 10.1186/s12859-015-0795-6
Pubmed ID
Authors

Cécile Monat, Christine Tranchant-Dubreuil, Ayité Kougbeadjo, Cédric Farcy, Enrique Ortega-Abboud, Souhila Amanzougarene, Sébastien Ravel, Mawussé Agbessi, Julie Orjuela-Bouniol, Maryline Summo, François Sabot

Abstract

The explosion of NGS (Next Generation Sequencing) sequence data requires a huge effort in Bioinformatics methods and analyses. The creation of dedicated, robust and reliable pipelines able to handle dozens of samples from raw FASTQ data to relevant biological data is a time-consuming task in all projects relying on NGS. To address this, we created a generic and modular toolbox for developing such pipelines. TOGGLE (TOolbox for Generic nGs anaLysEs) is a suite of tools able to design pipelines that manage large sets of NGS softwares and utilities. Moreover, TOGGLE offers an easy way to manipulate the various options of the different softwares through the pipelines in using a single basic configuration file, which can be changed for each assay without having to change the code itself. We also describe one implementation of TOGGLE in a complete analysis pipeline designed for SNP discovery for large sets of genomic data, ready to use in different environments (from a single machine to HPC clusters). TOGGLE speeds up the creation of robust pipelines with reliable log tracking and data flow, for a large range of analyses. Moreover, it enables Biologists to concentrate on the biological relevance of results, and change the experimental conditions easily. The whole code and test data are available at https://github.com/SouthGreenPlatform/TOGGLE .

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 3 3%
Germany 1 <1%
Indonesia 1 <1%
United Kingdom 1 <1%
Benin 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 96 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 46 44%
Student > Ph. D. Student 19 18%
Student > Master 13 12%
Professor > Associate Professor 7 7%
Professor 3 3%
Other 5 5%
Unknown 12 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 51 49%
Biochemistry, Genetics and Molecular Biology 18 17%
Computer Science 10 10%
Engineering 3 3%
Environmental Science 2 2%
Other 4 4%
Unknown 17 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 09 December 2015.
All research outputs
#3,373,825
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#1,188
of 7,454 outputs
Outputs of similar age
#48,234
of 287,598 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 145 outputs
Altmetric has tracked 23,881,329 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 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 84% 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 287,598 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 83% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.