Global prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans.

TitleGlobal prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans.
Publication TypeJournal Article
Year of Publication2009
AuthorsChikina MD, Huttenhower C, Murphy CT, Troyanskaya OG
JournalPLoS Comput Biol
Volume5
Issue6
Paginatione1000417
Date Published2009 Jun
ISSN1553-7358
KeywordsAlgorithms, Animals, Artificial Intelligence, Base Sequence, Caenorhabditis elegans, Computer Simulation, Data Interpretation, Statistical, GATA Transcription Factors, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Green Fluorescent Proteins, MicroRNAs, Models, Genetic, Oligonucleotide Array Sequence Analysis, Promoter Regions, Genetic, Reproducibility of Results, Sequence Homology, Nucleic Acid
Abstract

Tissue-specific gene expression plays a fundamental role in metazoan biology and is an important aspect of many complex diseases. Nevertheless, an organism-wide map of tissue-specific expression remains elusive due to difficulty in obtaining these data experimentally. Here, we leveraged existing whole-animal Caenorhabditis elegans microarray data representing diverse conditions and developmental stages to generate accurate predictions of tissue-specific gene expression and experimentally validated these predictions. These patterns of tissue-specific expression are more accurate than existing high-throughput experimental studies for nearly all tissues; they also complement existing experiments by addressing tissue-specific expression present at particular developmental stages and in small tissues. We used these predictions to address several experimentally challenging questions, including the identification of tissue-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues. We also investigate the role of tissue context in gene function through tissue-specific functional interaction networks. To our knowledge, this is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data.

DOI10.1371/journal.pcbi.1000417
Alternate JournalPLoS Comput. Biol.
PubMed ID19543383
PubMed Central IDPMC2692103
Grant ListP50 GM071508 / GM / NIGMS NIH HHS / United States
R01 GM071966 / GM / NIGMS NIH HHS / United States
T32 HG003284 / HG / NHGRI NIH HHS / United States
R01 GM071966! / GM / NIGMS NIH HHS / United States