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CoVaCS: a consensus variant calling system

Overview of attention for article published in BMC Genomics, February 2018
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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6 X users

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Title
CoVaCS: a consensus variant calling system
Published in
BMC Genomics, February 2018
DOI 10.1186/s12864-018-4508-1
Pubmed ID
Authors

Matteo Chiara, Silvia Gioiosa, Giovanni Chillemi, Mattia D’Antonio, Tiziano Flati, Ernesto Picardi, Federico Zambelli, David Stephen Horner, Graziano Pesole, Tiziana Castrignanò

Abstract

The advent and ongoing development of next generation sequencing technologies (NGS) has led to a rapid increase in the rate of human genome re-sequencing data, paving the way for personalized genomics and precision medicine. The body of genome resequencing data is progressively increasing underlining the need for accurate and time-effective bioinformatics systems for genotyping - a crucial prerequisite for identification of candidate causal mutations in diagnostic screens. Here we present CoVaCS, a fully automated, highly accurate system with a web based graphical interface for genotyping and variant annotation. Extensive tests on a gold standard benchmark data-set -the NA12878 Illumina platinum genome- confirm that call-sets based on our consensus strategy are completely in line with those attained by similar command line based approaches, and far more accurate than call-sets from any individual tool. Importantly our system exhibits better sensitivity and higher specificity than equivalent commercial software. CoVaCS offers optimized pipelines integrating state of the art tools for variant calling and annotation for whole genome sequencing (WGS), whole-exome sequencing (WES) and target-gene sequencing (TGS) data. The system is currently hosted at Cineca, and offers the speed of a HPC computing facility, a crucial consideration when large numbers of samples must be analysed. Importantly, all the analyses are performed automatically allowing high reproducibility of the results. As such, we believe that CoVaCS can be a valuable tool for the analysis of human genome resequencing studies. CoVaCS is available at: https://bioinformatics.cineca.it/covacs .

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X Demographics

The data shown below were collected from the profiles of 6 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 20%
Student > Master 11 17%
Researcher 10 16%
Student > Bachelor 9 14%
Professor 4 6%
Other 8 13%
Unknown 9 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 23%
Computer Science 12 19%
Agricultural and Biological Sciences 9 14%
Engineering 4 6%
Nursing and Health Professions 2 3%
Other 10 16%
Unknown 12 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 15 February 2018.
All research outputs
#6,856,518
of 24,226,848 outputs
Outputs from BMC Genomics
#2,953
of 10,925 outputs
Outputs of similar age
#134,495
of 445,124 outputs
Outputs of similar age from BMC Genomics
#64
of 205 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 10,925 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 72% 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 445,124 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 205 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.