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A sequence-based method to predict the impact of regulatory variants using random forest

Overview of attention for article published in BMC Systems Biology, March 2017
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Title
A sequence-based method to predict the impact of regulatory variants using random forest
Published in
BMC Systems Biology, March 2017
DOI 10.1186/s12918-017-0389-1
Pubmed ID
Authors

Qiao Liu, Mingxin Gan, Rui Jiang

Abstract

Most disease-associated variants identified by genome-wide association studies (GWAS) exist in noncoding regions. In spite of the common agreement that such variants may disrupt biological functions of their hosting regulatory elements, it remains a great challenge to characterize the risk of a genetic variant within the implicated genome sequence. Therefore, it is essential to develop an effective computational model that is not only capable of predicting the potential risk of a genetic variant but also valid in interpreting how the function of the genome is affected with the occurrence of the variant. We developed a method named kmerForest that used a random forest classifier with k-mer counts to predict accessible chromatin regions purely based on DNA sequences. We demonstrated that our method outperforms existing methods in distinguishing known accessible chromatin regions from random genomic sequences. Furthermore, the performance of our method can further be improved with the incorporation of sequence conservation features. Based on this model, we assessed importance of the k-mer features by a series of permutation experiments, and we characterized the risk of a single nucleotide polymorphism (SNP) on the function of the genome using the difference between the importance of the k-mer features affected by the occurrence of the SNP. We conducted a series of experiments and showed that our model can well discriminate between pathogenic and normal SNPs. Particularly, our model correctly prioritized SNPs that are proved to be enriched for the binding sites of FOXA1 in breast cancer cell lines from previous studies. We presented a novel method to interpret functional genetic variants purely base on DNA sequences. The proposed k-mer based score offers an effective means of measuring the impact of SNPs on the function of the genome, and thus shedding light on the identification of genetic risk factors underlying complex traits and diseases.

Twitter Demographics

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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 %
Student > Master 7 18%
Student > Ph. D. Student 6 15%
Student > Bachelor 5 13%
Researcher 5 13%
Professor 3 8%
Other 6 15%
Unknown 8 20%
Readers by discipline Count As %
Computer Science 9 23%
Biochemistry, Genetics and Molecular Biology 5 13%
Agricultural and Biological Sciences 5 13%
Medicine and Dentistry 4 10%
Mathematics 2 5%
Other 4 10%
Unknown 11 28%

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 01 April 2017.
All research outputs
#7,067,092
of 9,274,545 outputs
Outputs from BMC Systems Biology
#668
of 914 outputs
Outputs of similar age
#188,484
of 260,859 outputs
Outputs of similar age from BMC Systems Biology
#21
of 26 outputs
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We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.