The transcription factor encyclopedia.
Ontology highlight
ABSTRACT:
SUBMITTER: Yusuf D
PROVIDER: S-EPMC3439975 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The purpose of this study is to survey gene expression of potato (solanum tuberosum gilroy) in a variety of tissues and life cycle timepoints. Potato plants were grown from true seeds, planted in either soil or MS media, and grown in growth chambers. Four biological replicates each of twenty different tissue samples were collected at the appropriate timepoints. The samples were immediately frozen at -80. The tissues were then ground into a powder in liquid nitrogen, and RNA was extracted using Qiagen's QiaShredder columns and Qiagen's Rneasy MinElute Kit. The RNA was amplified using Arcturus' RiboAmp RNA Amplification Kit. 1ug of amplified RNA was used in TIGR's indirect labelleling procedure. All hybridaztions were applied to TIGR's 10kV4 potato array. Keywords: Loop design
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Project description:Protein-protein interactions of human transcrption factors
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Project description:BackgroundChromatin immunoprecipitation (ChIP) experiments are now the most comprehensive experimental approaches for mapping the binding of transcription factors (TFs) to their target genes. However, ChIP data alone is insufficient for identifying functional binding target genes of TFs for two reasons. First, there is an inherent high false positive/negative rate in ChIP-chip or ChIP-seq experiments. Second, binding signals in the ChIP data do not necessarily imply functionality.MethodsIt is known that ChIP-chip data and TF knockout (TFKO) data reveal complementary information on gene regulation. While ChIP-chip data can provide TF-gene binding pairs, TFKO data can provide TF-gene regulation pairs. Therefore, we propose a novel network approach for identifying functional TF-gene binding pairs by integrating the ChIP-chip data with the TFKO data. In our method, a TF-gene binding pair from the ChIP-chip data is regarded to be functional if it also has high confident curated TFKO TF-gene regulatory relation or deduced hypostatic TF-gene regulatory relation.Results and conclusionsWe first validated our method on a gathered ground truth set. Then we applied our method to the ChIP-chip data to identify functional TF-gene binding pairs. The biological significance of our identified functional TF-gene binding pairs was shown by assessing their functional enrichment, the prevalence of protein-protein interaction, and expression coherence. Our results outperformed the results of three existing methods across all measures. And our identified functional targets of TFs also showed statistical significance over the randomly assigned TF-gene pairs. We also showed that our method is dataset independent and can apply to ChIP-seq data and the E. coli genome. Finally, we provided an example showing the biological applicability of our notion.
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