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

doi:10.1186/s12859-018-2391-z

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^*

^*Corresponding author for this work

Division of Cancer Sciences (L5)

Research output: Contribution to journal › Article › peer-review

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 language	English
Article number	339
Journal	BMC Bioinformatics
Volume	19
Issue number	1
DOIs	https://doi.org/10.1186/s12859-018-2391-z
Publication status	Published - 25 Sept 2018

Keywords

Candidate-selection
DNA sequencing
Validation
Verification

Research Beacons, Institutes and Platforms

Manchester Cancer Research Centre

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10.1186/s12859-018-2391-z

Cite this

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title = "Valection: Design optimization for validation and verification studies",

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.",

keywords = "Candidate-selection, DNA sequencing, Validation, Verification",

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

year = "2018",

month = sep,

day = "25",

doi = "10.1186/s12859-018-2391-z",

language = "English",

volume = "19",

journal = "BMC Bioinformatics",

issn = "1471-2105",

publisher = "Springer Nature",

number = "1",

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TY - JOUR

T1 - Valection

T2 - Design optimization for validation and verification studies

AU - Cooper, Christopher I.

AU - Yao, Delia

AU - Sendorek, Dorota H.

AU - Yamaguchi, Takafumi N.

AU - P'ng, Christine

AU - Houlahan, Kathleen E.

AU - Caloian, Cristian

AU - Fraser, Michael

AU - Ellrott, Kyle

AU - Margolin, Adam A.

AU - Bristow, Robert G.

AU - Stuart, Joshua M.

AU - Boutros, Paul C.

PY - 2018/9/25

Y1 - 2018/9/25

N2 - 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.

AB - 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.

KW - Candidate-selection

KW - DNA sequencing

KW - Validation

KW - Verification

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JO - BMC Bioinformatics

JF - BMC Bioinformatics

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ER -

Valection: Design optimization for validation and verification studies

Abstract

Keywords

Research Beacons, Institutes and Platforms

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