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 Margolin, Robert G. Bristow, Joshua M. Stuart, Paul C. Boutros

Research output: Contribution to journalArticle

1 Citation (Scopus)

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

Fingerprint

Verification and Validation
Validation Studies
Software
Programming Languages
Technology
Prediction
Subset
Set theory
Computer programming languages
Maximise
Testing
Processing
Estimate
Profile
Design optimization
Strategy
Datasets

Keywords

  • Candidate-selection
  • DNA sequencing
  • Validation
  • Verification

ASJC Scopus subject areas

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

Cite this

Cooper, C. I., Yao, D., Sendorek, D. H., Yamaguchi, T. N., P'ng, C., Houlahan, K. E., ... 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

Valection : Design optimization for validation and verification studies. / Cooper, Christopher I.; Yao, Delia; Sendorek, Dorota H.; Yamaguchi, Takafumi N.; P'ng, Christine; Houlahan, Kathleen E.; Caloian, Cristian; Fraser, Michael; Ellrott, Kyle; Margolin, Adam; Bristow, Robert G.; Stuart, Joshua M.; Boutros, Paul C.

In: BMC Bioinformatics, Vol. 19, No. 1, 339, 25.09.2018.

Research output: Contribution to journalArticle

Cooper, CI, Yao, D, Sendorek, DH, Yamaguchi, TN, P'ng, C, Houlahan, KE, Caloian, C, Fraser, M, Ellrott, K, Margolin, A, Bristow, RG, Stuart, JM & Boutros, PC 2018, 'Valection: Design optimization for validation and verification studies', BMC Bioinformatics, vol. 19, no. 1, 339. https://doi.org/10.1186/s12859-018-2391-z
Cooper CI, Yao D, Sendorek DH, Yamaguchi TN, P'ng C, Houlahan KE et al. Valection: Design optimization for validation and verification studies. BMC Bioinformatics. 2018 Sep 25;19(1). 339. https://doi.org/10.1186/s12859-018-2391-z
Cooper, Christopher I. ; Yao, Delia ; Sendorek, Dorota H. ; Yamaguchi, Takafumi N. ; P'ng, Christine ; Houlahan, Kathleen E. ; Caloian, Cristian ; Fraser, Michael ; Ellrott, Kyle ; Margolin, Adam ; Bristow, Robert G. ; Stuart, Joshua M. ; Boutros, Paul C. / Valection : Design optimization for validation and verification studies. In: BMC Bioinformatics. 2018 ; Vol. 19, No. 1.
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