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OVAS: an open-source variant analysis suite with inheritance modelling

Overview of attention for article published in BMC Bioinformatics, February 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
5 tweeters
facebook
1 Facebook page

Citations

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1 Dimensions

Readers on

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17 Mendeley
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1 CiteULike
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Title
OVAS: an open-source variant analysis suite with inheritance modelling
Published in
BMC Bioinformatics, February 2018
DOI 10.1186/s12859-018-2030-8
Pubmed ID
Authors

Monika Mozere, Mehmet Tekman, Jameela Kari, Detlef Bockenhauer, Robert Kleta, Horia Stanescu

Abstract

The advent of modern high-throughput genetics continually broadens the gap between the rising volume of sequencing data, and the tools required to process them. The need to pinpoint a small subset of functionally important variants has now shifted towards identifying the critical differences between normal variants and disease-causing ones. The ever-increasing reliance on cloud-based services for sequence analysis and the non-transparent methods they utilize has prompted the need for more in-situ services that can provide a safer and more accessible environment to process patient data, especially in circumstances where continuous internet usage is limited. To address these issues, we herein propose our standalone Open-source Variant Analysis Sequencing (OVAS) pipeline; consisting of three key stages of processing that pertain to the separate modes of annotation, filtering, and interpretation. Core annotation performs variant-mapping to gene-isoforms at the exon/intron level, append functional data pertaining the type of variant mutation, and determine hetero/homozygosity. An extensive inheritance-modelling module in conjunction with 11 other filtering components can be used in sequence ranging from single quality control to multi-file penetrance model specifics such as X-linked recessive or mosaicism. Depending on the type of interpretation required, additional annotation is performed to identify organ specificity through gene expression and protein domains. In the course of this paper we analysed an autosomal recessive case study. OVAS made effective use of the filtering modules to recapitulate the results of the study by identifying the prescribed compound-heterozygous disease pattern from exome-capture sequence input samples. OVAS is an offline open-source modular-driven analysis environment designed to annotate and extract useful variants from Variant Call Format (VCF) files, and process them under an inheritance context through a top-down filtering schema of swappable modules, run entirely off a live bootable medium and accessed locally through a web-browser.

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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 24%
Student > Ph. D. Student 4 24%
Student > Bachelor 3 18%
Other 2 12%
Student > Master 2 12%
Other 1 6%
Unknown 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 29%
Computer Science 3 18%
Medicine and Dentistry 3 18%
Engineering 2 12%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 2 12%
Unknown 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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
#6,590,834
of 12,516,641 outputs
Outputs from BMC Bioinformatics
#2,410
of 4,664 outputs
Outputs of similar age
#137,724
of 343,556 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 17 outputs
Altmetric has tracked 12,516,641 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,664 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 47th percentile – i.e., 47% 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 343,556 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 59% of its contemporaries.
We're also able to compare this research output to 17 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 70% of its contemporaries.