Regulatory Single-Nucleotide Variant Predictor Increases Predictive Performance of Functional Regulatory Variants.

@article{Peterson2016RegulatorySV,
  title={Regulatory Single-Nucleotide Variant Predictor Increases Predictive Performance of Functional Regulatory Variants.},
  author={Thomas A. Peterson and Matthew E. Mort and David N. Cooper and Predrag Radivojac and Maricel G. Kann and Sean D. Mooney},
  journal={Human mutation},
  year={2016},
  volume={37 11},
  pages={
          1137-1143
        }
}
In silico methods for detecting functionally relevant genetic variants are important for identifying genetic markers of human inherited disease. Much research has focused on protein-coding variants since coding regions have well-defined physicochemical and functional properties. However, many bioinformatics tools are not applicable to variants outside coding regions. Here, we increase the classification performance of our regulatory single-nucleotide variant predictor (RSVP) for variants that… CONTINUE READING
Recent Discussions
This paper has been referenced on Twitter 2 times over the past 90 days. VIEW TWEETS

References

Publications referenced by this paper.
Showing 1-10 of 41 references

Predicting effects of noncoding variants with deep learning-based sequence model

  • J Zhou, Troyanskaya OG.
  • Nat Methods 12(10):931–934. HUMAN MUTATION, Vol…
  • 2015

Similar Papers

Loading similar papers…