Selective Phenotyping, Entropy Reduction, and the Mastermind game

BMC Bioinformatics. 2011;12(1):406 DOI 10.1186/1471-2105-12-406

 

Journal Homepage

Journal Title: BMC Bioinformatics

ISSN: 1471-2105 (Online)

Publisher: BMC

LCC Subject Category: Medicine: Medicine (General): Computer applications to medicine. Medical informatics | Science: Biology (General)

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Gagneur Julien
Elze Markus C
Tresch Achim

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 19 weeks

 

Abstract | Full Text

<p>Abstract</p> <p>Background</p> <p>With the advance of genome sequencing technologies, phenotyping, rather than genotyping, is becoming the most expensive task when mapping genetic traits. The need for efficient selective phenotyping strategies, <it>i</it>.<it>e</it>. methods to select a subset of genotyped individuals for phenotyping, therefore increases. Current methods have focused either on improving the detection of causative genetic variants or their precise genomic location separately.</p> <p>Results</p> <p>Here we recognize selective phenotyping as a Bayesian model discrimination problem and introduce SPARE (Selective Phenotyping Approach by Reduction of Entropy). Unlike previous methods, SPARE can integrate the information of previously phenotyped individuals, thereby enabling an efficient incremental strategy. The effective performance of SPARE is demonstrated on simulated data as well as on an experimental yeast dataset.</p> <p>Conclusions</p> <p>Using entropy reduction as an objective criterion gives a natural way to tackle both issues of detection and localization simultaneously and to integrate intermediate phenotypic data. We foresee entropy-based strategies as a fruitful research direction for selective phenotyping.</p>