A Pooling-deconvolution Strategy for Biological Network Elucidation

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2006 Feb 21

PMID 16489335

Citations 8

Authors

Fulai Jin

Tony Hazbun

Gregory A Michaud

Michael Salcius

Paul F Predki

Stanley Fields

Jing Huang

Affiliations

Soon will be listed here.

Abstract

The generation of large-scale data sets is a fundamental requirement of systems biology. But despite recent advances, generation of such high-coverage data remains a major challenge. We developed a pooling-deconvolution strategy that can dramatically decrease the effort required. This strategy, pooling with imaginary tags followed by deconvolution (PI-deconvolution), allows the screening of 2(n) probe proteins (baits) in 2 x n pools, with n replicates for each bait. Deconvolution of baits with their binding partners (preys) can be achieved by reading the prey's profile from the 2 x n experiments. We validated this strategy for protein-protein interaction mapping using both proteome microarrays and a yeast two-hybrid array, demonstrating that PI-deconvolution can be used to identify interactions accurately with fewer experiments and better coverage. We also show that PI-deconvolution can be used to identify protein-small molecule interactions inferred from profiling the yeast deletion collection. PI-deconvolution should be applicable to a wide range of library-against-library approaches and can also be used to optimize array designs.

Citing Articles

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