Literature DB >> 18184686

Interpool: interpreting smart-pooling results.

Nicolas Thierry-Mieg1, Gilles Bailly.   

Abstract

MOTIVATION: In high-throughput projects aiming to identify rare positives using a binary assay, smart-pooling constitutes an appealing strategy liable of significantly reducing the number of tests while correcting for experimental noise. In order to perform simulations for choosing an appropriate set of pools, and later to interpret the experimental results, the pool outcomes must be 'decoded'. The intuitive aim is clearly to identify the positives that gave rise to an observation, whether real or simulated. However, this goal is not well-formalized and has been the focus of very few studies.
RESULTS: We first provide a clear combinatorial formalization of the 'decoding problem'. We then present interpool, an exact algorithm to solve this problem. An efficient implementation is freely available. Its usefulness is illustrated in the context of yeast-two-hybrid interactome mapping with the Shifted Transversal Design. AVAILABILITY: The implementation, licensed under the GNU GPL, can be downloaded from http://www-timc.imag.fr/Nicolas.Thierry-Mieg/.

Mesh:

Year:  2008        PMID: 18184686     DOI: 10.1093/bioinformatics/btn001

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  4 in total

1.  Shifted Transversal Design smart-pooling for high coverage interactome mapping.

Authors:  Xiaofeng Xin; Jean-François Rual; Tomoko Hirozane-Kishikawa; David E Hill; Marc Vidal; Charles Boone; Nicolas Thierry-Mieg
Journal:  Genome Res       Date:  2009-05-15       Impact factor: 9.043

Review 2.  Pooling in high-throughput drug screening.

Authors:  Raghunandan M Kainkaryam; Peter J Woolf
Journal:  Curr Opin Drug Discov Devel       Date:  2009-05

3.  Precision and recall estimates for two-hybrid screens.

Authors:  Hailiang Huang; Joel S Bader
Journal:  Bioinformatics       Date:  2008-12-17       Impact factor: 6.937

4.  poolHiTS: a shifted transversal design based pooling strategy for high-throughput drug screening.

Authors:  Raghunandan M Kainkaryam; Peter J Woolf
Journal:  BMC Bioinformatics       Date:  2008-05-30       Impact factor: 3.169

  4 in total

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