Universität zu Köln

Simeone, Angela, Marsico, Giovanni, Collinet, Claudio ORCID: 0000-0002-8532-2601, Galvez, Thierry, Kalaidzidis, Yannis ORCID: 0000-0002-6137-1193, Zerial, Marino and Beyer, Andreas ORCID: 0000-0002-3891-2123 (2014). Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data. PLoS Comput. Biol., 10 (9). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1553-7358

Full text not available from this repository.

Abstract

Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is the lack of computational methods for the systematic integration of multi-parametric phenotypic datasets with molecular interactions. Here, we present Integrative Multi Profile Analysis of Cellular Traits (IMPACT). The main goal of IMPACT is to identify the most consistent phenotypic profile among interacting genes. This approach utilizes two types of external information: sets of related genes (IMPACT-sets) and network information (IMPACT-modules). Based on the notion that interacting genes are more likely to be involved in similar functions than non-interacting genes, this data is used as a prior to inform the filtering of phenotypic profiles that are similar among interacting genes. IMPACT-sets selects the most frequent profile among a set of related genes. IMPACT-modules identifies sub-networks containing genes with similar phenotype profiles. The statistical significance of these selections is subsequently quantified via permutations of the data. IMPACT (1) handles multiple profiles per gene, (2) rescues genes with weak phenotypes and (3) accounts for multiple biases e. g. caused by the network topology. Application to a genome-wide RNAi screen on endocytosis showed that IMPACT improved the recovery of known endocytosis-related genes, decreased off-target effects, and detected consistent phenotypes. Those findings were confirmed by rescreening 468 genes. Additionally we validated an unexpected influence of the IGF-receptor on EGF-endocytosis. IMPACT facilitates the selection of high-quality phenotypic profiles using different types of independent information, thereby supporting the molecular interpretation of functional screens.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Simeone, Angela UNSPECIFIED UNSPECIFIED UNSPECIFIED Marsico, Giovanni UNSPECIFIED UNSPECIFIED UNSPECIFIED Collinet, Claudio UNSPECIFIED orcid.org/0000-0002-8532-2601 UNSPECIFIED Galvez, Thierry UNSPECIFIED UNSPECIFIED UNSPECIFIED Kalaidzidis, Yannis UNSPECIFIED orcid.org/0000-0002-6137-1193 UNSPECIFIED Zerial, Marino UNSPECIFIED UNSPECIFIED UNSPECIFIED Beyer, Andreas UNSPECIFIED orcid.org/0000-0002-3891-2123 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-431401
DOI:	10.1371/journal.pcbi.1003801
Journal or Publication Title:	PLoS Comput. Biol.
Volume:	10
Number:	9
Date:	2014
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1553-7358
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language GROWTH-FACTOR RECEPTOR; BREAST-CANCER CELLS; ENDOCYTOSIS; SIRNA; IDENTIFICATION; HETERODIMERIZATION; PHOSPHORYLATION; TRANSACTIVATION; ACTIVATION; RESISTANCE Multiple languages Biochemical Research Methods; Mathematical & Computational Biology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/43140