NDLI: A data integration method for exploring gene regulatory mechanisms

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A data integration method for exploring gene regulatory mechanisms

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A data integration method for exploring gene regulatory mechanisms

Content Provider	ACM Digital Library
Author	Gamalielsson, Jonas Synnergren, Jane Olsson, Björn
Abstract	Systems biology aims to understand the behavior of and interaction between various components of the living cell, such as genes, proteins, and metabolites. A large number of components are involved in these complex systems and the diversity of relationships between the components can be overwhelming, and there is therefore a need for analysis methods incorporating data integration. We here present a method for exploring gene regulatory mechanisms which integrates various types of data to assist the identification of important components in gene regulation mechanisms. By first analyzing gene expression data, a set of differentially expressed genes is selected. These genes are then further investigated by combining various types of biological information, such as clustering results, promoter sequences, binding sites, transcription factors and other previously published information regarding the selected genes. Inspired by Information Fusion research, we also mapped functions of the proposed method to the well-known OODA-model to facilitate application of this data integration method in other research communities. We have successfully applied the method to genes identified as differentially expressed in human embryonic stem cells at different stages of differentiation towards cardiac cells. We identified 15 novel motifs that may represent important binding sites in the cardiac cell linage.
Starting Page	81
Ending Page	84
Page Count	4
File Format	PDF
ISBN	9781605582511
DOI	10.1145/1458449.1458468
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Motifs Data integration Gene regulation Data fusion Gene expression
Content Type	Text
Resource Type	Article

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