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ToTem: a tool for variant calling pipeline optimization

Overview of attention for article published in BMC Bioinformatics, June 2018
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Title
ToTem: a tool for variant calling pipeline optimization
Published in
BMC Bioinformatics, June 2018
DOI 10.1186/s12859-018-2227-x
Pubmed ID
Authors

Nikola Tom, Ondrej Tom, Jitka Malcikova, Sarka Pavlova, Blanka Kubesova, Tobias Rausch, Miroslav Kolarik, Vladimir Benes, Vojtech Bystry, Sarka Pospisilova

Abstract

High-throughput bioinformatics analyses of next generation sequencing (NGS) data often require challenging pipeline optimization. The key problem is choosing appropriate tools and selecting the best parameters for optimal precision and recall. Here we introduce ToTem, a tool for automated pipeline optimization. ToTem is a stand-alone web application with a comprehensive graphical user interface (GUI). ToTem is written in Java and PHP with an underlying connection to a MySQL database. Its primary role is to automatically generate, execute and benchmark different variant calling pipeline settings. Our tool allows an analysis to be started from any level of the process and with the possibility of plugging almost any tool or code. To prevent an over-fitting of pipeline parameters, ToTem ensures the reproducibility of these by using cross validation techniques that penalize the final precision, recall and F-measure. The results are interpreted as interactive graphs and tables allowing an optimal pipeline to be selected, based on the user's priorities. Using ToTem, we were able to optimize somatic variant calling from ultra-deep targeted gene sequencing (TGS) data and germline variant detection in whole genome sequencing (WGS) data. ToTem is a tool for automated pipeline optimization which is freely available as a web application at  https://totem.software .

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The data shown below were collected from the profiles of 3 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 27%
Other 5 12%
Researcher 5 12%
Student > Master 4 10%
Professor > Associate Professor 3 7%
Other 7 17%
Unknown 6 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 32%
Agricultural and Biological Sciences 10 24%
Computer Science 4 10%
Neuroscience 2 5%
Engineering 2 5%
Other 3 7%
Unknown 7 17%
Attention Score in Context

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 30 March 2021.
All research outputs
#14,574,276
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#4,835
of 7,387 outputs
Outputs of similar age
#187,764
of 329,796 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 99 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.