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Valection: design optimization for validation and verification studies

Authors :
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
Source :
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-11 (2018)
Publication Year :
2018
Publisher :
BMC, 2018.

Abstract

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

Details

Language :
English
ISSN :
14712105
Volume :
19
Issue :
1
Database :
Directory of Open Access Journals
Journal :
BMC Bioinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.523eeedb54fb4c0eaf90704f90a6d119
Document Type :
article
Full Text :
https://doi.org/10.1186/s12859-018-2391-z