14 research outputs found

    A Model-Based Method for Gene Dependency Measurement

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    <div><p>Many computational methods have been widely used to identify transcription regulatory interactions based on gene expression profiles. The selection of dependency measure is very important for successful regulatory network inference. In this paper, we develop a new method–DBoMM (Difference in BIC of Mixture Models)–for estimating dependency of gene by fitting the gene expression profiles into mixture Gaussian models. We show that DBoMM out-performs 4 other existing methods, including Kendall’s tau correlation (TAU), Pearson Correlation (COR), Euclidean distance (EUC) and Mutual information (MI) using <em>Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster</em>, <em>Arabidopsis thaliana</em> data and synthetic data. DBoMM can also identify condition-dependent regulatory interactions and is robust to noisy data. Of the 741 <em>Escherichia coli</em> regulatory interactions inferred by DBoMM at a 60% true positive rate, 65 are previously known interactions and 676 are novel predictions. To validate the new prediction, the promoter sequences of target genes regulated by the same transcription factors were analyzed and significant motifs were identified.</p> </div

    DBoMM can catch the conditional dependent interactions and distinguish the real gene interactions from the background.

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    <p>The expression profiles of two interacting genes (a) and non-interacting genes (c) are fitted into a bivariate mixture Gaussian distribution (joint distribution with different colors). The expression profiles of two interacting genes (b) and non-interacting genes (d) are separately fitted into two univariate mixture Gaussian distribution (marginal distribution). The blue and green lines represent the distribution of the two genes respectively. The contours correspond to the joint densities implied by DBoMM.</p

    Motifs detected for TF and .

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    <p>(a). The regulatory motif detected in the promoters of the 19 inferred target operons(upper) compared to the motif identified in PRODORIC. (b). The regulatory motif detected in the promoters of 8 inferred target operons(upper) compared to the motif identified in PRODORIC(lower).</p

    A comparison of different methods using PR-curve.

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    <p>(a). <i>E.coli</i> dataset and the reference network from RegulonDB; (b). <i>Yeast</i> datset and the reference network from YEASTRACT; (c). <i>Drosophila</i> dataset and the reference network from DroID; (d). <i>Arabidopsis</i> datset and the reference network from AGRIS. X axis: recall; Y axis: precision. In general, DBoMM out-performs other 4 methods using various datasets.</p

    DBoMM is robust to noise.

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    <p>Different levels of noise are introduced to the datasets. The numbers in the legend correspond to the noise levels, e.g. “noisy2” means 20% of noise introduced. DBoMM remains stable with up to 60% of noise. X axis: recall; y axis: precision.</p

    DBoMM can identify the conditional dependent regulatory interactions between two genes.

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    <p>The experimental conditions are classified into 6 different clusters based on the expression profiles of two genes (<i>lexA</i> and <i>recA</i>). Cn represents the index of the cluster.</p

    Inhibitory effect of different concentration of rice-produced rhIGFBP-3 on HT-29 human colon cancer cells.

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    <p>Different concentration of seed proteins (ranged from 1.042 to 15.625 mg) from WT and transgenic SBK-66 and SB-57 lines were used to treat HT-29 cells. Data are shown as means ± SD. *<i>p</i> < 0.05 and ** <i>p</i> < 0.01 denote statistically significant and very significant differences, respectively, between transgenic lines and WT.</p

    Inhibitory effect of commercial rhIGFBP-3 on human MCF-7 breast cancer cells and HT-29 colon cancer cells.

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    <p>Different concentrations of commercial rhIGFBP-3 ranged from 9.375 ng/ml to 300 ng/ml were used to treat MCF-7 and HT-29 cells. Data are shown as means ± SD.</p

    Glycoprotein staining of transgenic lines and WT line in rice seeds.

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    <p>Total protein was extracted from mature dehulled seeds of SB-57 and SBK-66 transgenic lines and WT. RNase B was used as the glycoprotein control. To evaluate endoglycosidase digestion efficiency, RNase B was digested by PNGase F and Endo H. Lane 1: marker; lane 2: WT; lane 3: SBK-66; lane 4: SK-57; lane 5: RNase B control without digestion; lane 6: RNase B digested by PNGase F; and lane 7: RNase B digested by Endo H. Band 1: PNGase F; band 2: RNase B; band 3: RNase B with Endo H digestion, and band 4: RNase B with PNGase F digestion.</p
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