↓ Skip to main content

An heuristic filtering tool to identify phenotype-associated genetic variants applied to human intellectual disability and canine coat colors

Overview of attention for article published in BMC Bioinformatics, November 2015
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 X users

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
17 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
An heuristic filtering tool to identify phenotype-associated genetic variants applied to human intellectual disability and canine coat colors
Published in
BMC Bioinformatics, November 2015
DOI 10.1186/s12859-015-0822-7
Pubmed ID
Authors

Bart J. G. Broeckx, Frank Coopman, Geert Verhoeven, Tim Bosmans, Ingrid Gielen, Walter Dingemanse, Jimmy H. Saunders, Dieter Deforce, Filip Van Nieuwerburgh

Abstract

Identification of one or several disease causing variant(s) from the large collection of variants present in an individual is often achieved by the sequential use of heuristic filters. The recent development of whole exome sequencing enrichment designs for several non-model species created the need for a species-independent, fast and versatile analysis tool, capable of tackling a wide variety of standard and more complex inheritance models. With this aim, we developed "Mendelian", an R-package that can be used for heuristic variant filtering. The R-package Mendelian offers fast and convenient filters to analyze putative variants for both recessive and dominant models of inheritance, with variable degrees of penetrance and detectance. Analysis of trios is supported. Filtering against variant databases and annotation of variants is also included. This package is not species specific and supports parallel computation. We validated this package by reanalyzing data from a whole exome sequencing experiment on intellectual disability in humans. In a second example, we identified the mutations responsible for coat color in the dog. This is the first example of whole exome sequencing without prior mapping in the dog. We developed an R-package that enables the identification of disease-causing variants from the long list of variants called in sequencing experiments. The software and a detailed manual are available at https://github.com/BartBroeckx/Mendelian .

X Demographics

X Demographics

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 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 %
Student > Master 3 18%
Researcher 3 18%
Student > Ph. D. Student 3 18%
Professor > Associate Professor 2 12%
Other 1 6%
Other 2 12%
Unknown 3 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 35%
Veterinary Science and Veterinary Medicine 3 18%
Biochemistry, Genetics and Molecular Biology 2 12%
Medicine and Dentistry 2 12%
Nursing and Health Professions 1 6%
Other 1 6%
Unknown 2 12%
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 24 November 2015.
All research outputs
#14,222,096
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#4,541
of 7,418 outputs
Outputs of similar age
#195,910
of 390,067 outputs
Outputs of similar age from BMC Bioinformatics
#85
of 135 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% 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 390,067 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 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.