BMC Bioinformatics (Sep 2018)

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,
  • SMC-DNA Challenge Participants,
  • Kyle Ellrott,
  • Adam A Margolin,
  • Robert G Bristow,
  • Joshua M Stuart,
  • Paul C Boutros

DOI
https://doi.org/10.1186/s12859-018-2391-z
Journal volume & issue
Vol. 19, no. 1
pp. 1 – 11

Abstract

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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

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