Valection: Design optimization for validation and verification studies

Christopher I. Cooper, Delia Yao, Dorota H. Sendorek, Takafumi N. Yamaguchi, Christine P'ng, Kathleen E. Houlahan, Cristian Caloian, Michael Fraser, Kyle Ellrott, Adam A. Margolin, Robert G. Bristow, Joshua M. Stuart, Paul C. Boutros

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Background: Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile. Results: To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets. Conclusions: Valection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection.

Original languageEnglish (US)
Article number339
JournalBMC bioinformatics
Volume19
Issue number1
DOIs
StatePublished - Sep 25 2018

Keywords

  • Candidate-selection
  • DNA sequencing
  • Validation
  • Verification

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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  • Cite this

    Cooper, C. I., Yao, D., Sendorek, D. H., Yamaguchi, T. N., P'ng, C., Houlahan, K. E., Caloian, C., Fraser, M., Ellrott, K., Margolin, A. A., Bristow, R. G., Stuart, J. M., & Boutros, P. C. (2018). Valection: Design optimization for validation and verification studies. BMC bioinformatics, 19(1), [339]. https://doi.org/10.1186/s12859-018-2391-z