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Lack of correlation between in silico projection of function and quantitative real-time PCR-determined gene expression levels in colon tissue

Overview of attention for article published in Pharmacogenomics and Personalized Medicine, September 2013
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2 tweeters

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5 Mendeley
Title
Lack of correlation between in silico projection of function and quantitative real-time PCR-determined gene expression levels in colon tissue
Published in
Pharmacogenomics and Personalized Medicine, September 2013
DOI 10.2147/pgpm.s49199
Pubmed ID
Authors

Susan Kadlubar, Rosalind Penney, Abbie Lundgreen, Aiwei Yao-Borengassar, Vineetha Koroth-Edavana, Suzanne Williams, Roger Wolff, Marty Slattery

Abstract

There are a number of in silico programs that use algorithms and external web sources to predict the effect of single nucleotide polymorphisms (SNPs). While many of these programs have been shown to predict accurately the effect of SNPs in functional areas of the gene, such as 5' upstream or coding regions, empiric research may be warranted to confirm the functional consequences of SNPs that are predicted to have little to no effect. We compared predictions from FASTSNP (Function Analysis and Selection Tool for Single Nucleotide Polymorphism) and F-SNP (Functional Single Nucleotide Polymorphism) with experimentally derived genotype-phenotype correlations to determine the accuracy of these programs in predicting SNP functionality. We used normal colon tissue to evaluate 24 TagSNPs within six genes. Two of 16 SNPs that were predicted to have no functional effect in FASTSNP were significantly associated with gene expression. Only one of the eight SNPs that were predicted to have a low to high effect was significantly associated with gene expression. While the two in silico programs that were used were similar in their results for the SNPs predicted by FASTSNP to have no effect, of SNPs with scores from low to high, there were three that received an F-SNP score below what is considered functionally significant. In silico programs can fail to identify functional SNPs, supporting a continuing role for empiric analysis of SNP function. Laboratory analysis is necessary to identify causal SNPs accurately, establish biological plausibility of the effect, and ultimately inform cancer prevention strategies.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 20%
Professor 1 20%
Student > Ph. D. Student 1 20%
Student > Master 1 20%
Unknown 1 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 40%
Unspecified 1 20%
Engineering 1 20%
Unknown 1 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 09 September 2013.
All research outputs
#3,182,701
of 4,759,378 outputs
Outputs from Pharmacogenomics and Personalized Medicine
#4
of 4 outputs
Outputs of similar age
#62,006
of 97,101 outputs
Outputs of similar age from Pharmacogenomics and Personalized Medicine
#171
of 240 outputs
Altmetric has tracked 4,759,378 research outputs across all sources so far. This one is in the 29th percentile – i.e., 29% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4 research outputs from this source. They receive a mean Attention Score of 0.2. This one scored the same or higher as 0 of them.
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 97,101 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 240 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.