Universität zu Köln

Failmezger, Henrik, Froehlich, Holger and Tresch, Achim (2013). Unsupervised automated high throughput phenotyping of RNAi time-lapse movies. BMC Bioinformatics, 14. LONDON: BMC. ISSN 1471-2105

Full text not available from this repository.

Abstract

Background: Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens. Results: We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene's function. Conclusion: Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Failmezger, Henrik UNSPECIFIED UNSPECIFIED UNSPECIFIED Froehlich, Holger UNSPECIFIED UNSPECIFIED UNSPECIFIED Tresch, Achim UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-474214
DOI:	10.1186/1471-2105-14-292
Journal or Publication Title:	BMC Bioinformatics
Volume:	14
Date:	2013
Publisher:	BMC
Place of Publication:	LONDON
ISSN:	1471-2105
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language HUMAN-CELLS; GENE; CLASSIFICATION; SEGMENTATION; MECHANISM; P21(CIP1); LESSONS; GENOME; KEGG; TOOL Multiple languages Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/47421