↓ Skip to main content

myVCF: a desktop application for high-throughput mutations data management.

Overview of attention for article published in Bioinformatics, July 2017
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
7 X users

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
40 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
myVCF: a desktop application for high-throughput mutations data management.
Published in
Bioinformatics, July 2017
DOI 10.1093/bioinformatics/btx475
Pubmed ID
Authors

Alessandro Pietrelli, Luca Valenti

Abstract

Next-generation sequencing technologies have become the most powerful tool to discover genetic variants associated with human diseases. Although the dramatic reductions in the costs facilitate the use in the wet-lab and clinics, the huge amount of data generated renders their management by non-expert researchers and physicians extremely difficult. Therefore, there is an urgent need of novel approaches and tools aimed at getting the 'end-users' closer to the sequencing data, facilitating the access by non-bioinformaticians, and to speed-up the functional interpretation of genetic variants. We developed myVCF, a standalone, easy-to-use desktop application, which is based on a browser interface and is suitable for Windows, Mac and UNIX systems. myVCF is an efficient platform that is able to manage multiple sequencing projects created from VCF files within the system; stores genetic variants and samples genotypes from an annotated VCF files into a SQLite database; implements a flexible search engine for data exploration, allowing to query for chromosomal region, gene, single variant or dbSNP ID. Besides, myVCF generates a summary statistics report about mutations distribution across samples and across the genome/exome by aggregating the information within the VCF file. In summary, the myVCF platform allows end-users without strong programming and bioinformatics skills to explore, query, visualize and export mutations data in a simple and straightforward way. https://apietrelli.github.io/myVCF/. [email protected]. Supplementary data are available at Bioinformatics online.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 23%
Student > Ph. D. Student 6 15%
Other 4 10%
Student > Master 3 8%
Student > Doctoral Student 2 5%
Other 6 15%
Unknown 10 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 30%
Biochemistry, Genetics and Molecular Biology 7 18%
Computer Science 6 15%
Medicine and Dentistry 2 5%
Unspecified 1 3%
Other 1 3%
Unknown 11 28%
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 10 November 2017.
All research outputs
#6,781,689
of 24,313,168 outputs
Outputs from Bioinformatics
#5,560
of 12,082 outputs
Outputs of similar age
#102,287
of 319,940 outputs
Outputs of similar age from Bioinformatics
#99
of 219 outputs
Altmetric has tracked 24,313,168 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 12,082 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 53% 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 319,940 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 67% of its contemporaries.
We're also able to compare this research output to 219 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 55% of its contemporaries.