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Establishing and validating regulatory regions for variant annotation and expression analysis

Overview of attention for article published in BMC Genomics, June 2016
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Title
Establishing and validating regulatory regions for variant annotation and expression analysis
Published in
BMC Genomics, June 2016
DOI 10.1186/s12864-016-2724-0
Pubmed ID
Authors

Alexander Kaplun, Mathias Krull, Karthick Lakshman, Volker Matys, Birgit Lewicki, Jennifer D. Hogan

Abstract

The regulatory effect of inherited or de novo genetic variants occurring in promoters as well as in transcribed or even coding gene regions is gaining greater recognition as a contributing factor to disease processes in addition to mutations affecting protein functionality. Thousands of such regulatory mutations are already recorded in HGMD, OMIM, ClinVar and other databases containing published disease causing and associated mutations. It is therefore important to properly annotate genetic variants occurring in experimentally verified and predicted transcription factor binding sites (TFBS) that could thus influence the factor binding event. Selection of the promoter sequence used is an important factor in the analysis as it directly influences the composition of the sequence available for transcription factor binding analysis. In this study we first establish genomic regions likely to be involved in regulation of gene expression. TRANSFAC uses a method of virtual transcription start sites (vTSS) calculation to define the best supported promoter for a gene. We have performed a comparison of the virtually calculated promoters between the best supported and secondary promoters in hg19 and hg38 reference genomes to test and validate the approach. Next we create and utilize a workflow for systematic analysis of casual disease associated variants in TFBS using Genome Trax and TRANSFAC databases. A total of 841 and 736 experimentally verified TFBSs within best supported promoters were mapped over HGMD and ClinVar mutation sites respectively. Tens of thousands of predicted ChIP-Seq derived TFBSs were mapped over mutations as well. We have further analyzed some of these mutations for potential gain or loss in transcription factor binding. We have confirmed the validity of TRANSFAC's approach to define the best supported promoters and established a workflow of their use in annotation of regulatory genetic variants.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 3%
Italy 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 20%
Researcher 4 13%
Student > Master 4 13%
Student > Bachelor 3 10%
Other 3 10%
Other 5 17%
Unknown 5 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 27%
Agricultural and Biological Sciences 5 17%
Medicine and Dentistry 3 10%
Computer Science 3 10%
Engineering 3 10%
Other 2 7%
Unknown 6 20%

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 01 July 2016.
All research outputs
#14,856,117
of 22,879,161 outputs
Outputs from BMC Genomics
#6,147
of 10,666 outputs
Outputs of similar age
#212,905
of 352,801 outputs
Outputs of similar age from BMC Genomics
#111
of 174 outputs
Altmetric has tracked 22,879,161 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,666 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 37th percentile – i.e., 37% 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 352,801 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 174 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.