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Fig. 2. ChIP-seq with valley-finding identifies regions for transcription-factor binding. Genome viewer tracks for Trim27 (chr13:21,267,345-21,277,316), E2f3 (chr13:30,071,171-30,083,320), and Hist1h1b (chr13:21,868,763-21,874,488), showing ChIP-seq reads (top), MACs peaks (middle), valley regions (lower, orange), and IgG control (grey, lower) for H3K27Ac (top 4 rows) and H3K4me3 (bottom 4 rows). The valleys highlight regions where we searched for transcription factor binding motifs.. From: Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ib00040a Click here for additional data file. Click here for additional data file. .
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Fig. 5. Validation shows in vitro and in vivo effects for Hgs and Wwp1. In the competition assays, we measure the relative abundances of pre-B-cells with and without an shRNA against our gene of interest either in culture or transplanted into mice. We measure relative proportions at the time of morbidity using FACS. All plots are mean ± S.D. For all samples, n = 3, except for Hgs, and Wwp1 tissue samples where n = 4.. From: Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ib00040a Click here for additional data file. Click here for additional data file. .
Fig. 4. SAMNet selects transcription factors that explain genes with greatest differential expression. The transcription factors and differentially expressed genes are represented as triangles and octagons respectively. Grey shading on the transcription factors represents the extent of depletion in the original screen. Shading on the differentially expressed genes reflects either down-regulation (green) or up-regulation (purple) in the in vivo screen relative to the in vitro screen. The thickness of the interaction line represents the amount of flow captured by that interaction; qualitatively this reflects an edge with higher interaction confidence in the underlying interactome. Node border color represents fractional representation in a family of 100 random networks. Those without pink/orange border coloring are non-specific. A high-resolution image is available: ; http://fraenkel-nsf.csbi.mit.edu/psiquic/.. From: Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ib00040a Click here for additional data file. Click here for additional data file. .
Fig. 3. SAMNet identifies integrated network for ALL progression. The purple/green edges represent interactions from the in vivo/in vitro screens. RNAi hits are represented by a shaded square; the shading refers to the extent of depletion in the original screen. A diamond is a transcription factor selected by SAMNet; those that are shaded are also hits from the shRNA screen. All white-face nodes are hidden targets selected by the algorithm. Node border color represents fractional representation in a family of 100 random networks. Those without pink/orange border coloring are non-specific. The thickness of the interaction line represents the amount of flow captured by that interaction; qualitatively this reflects an edge with higher interaction confidence in the underlying interactome. Downstream mRNA pictured in . Red arrows indicate where Wwp1, Hgs, Lmo2, and Pogz exist within the network. A high-resolution image is available: ; http://fraenkel-nsf.csbi.mit.edu/psiquic/.. From: Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ib00040a Click here for additional data file. Click here for additional data file. .
Fig. 1. Constructing a network model from multiple ‘omic measurements. (A) We start with a probabilistic interactome that includes protein–protein interactions scored by the confidence of their interaction. This confidence score reflects the strength of evidence across multiple interaction databases and this score constrains the edge's capacity within our flow-based model. Higher confidence leads to higher capacity. Some of these proteins are transcription factors (triangles). We complement these edges with transcription-factor (triangles) to DNA (octagons) binding interactions. We predict these interactions and their edge probabilities by measuring active and open chromatin via ChIP-seq and looking for enrichment of transcription factor binding motifs. Conceptually, this is our available road map for creating pathways where the capacities are akin to speed limits. (B) We connect an artificial source node to all proteins that have corresponding shRNAs that were considered hits in the screen. These edge capacities reflect the strength of the shRNA effect. In our model, these edges reflect how strongly an shRNA depletes from input to morbidity. We connect an artificial sink node to differentially expressed mRNAs. These edges reflect the fold-change in expression. The algorithm introduces flow into the network and looks for an optimal route from the source to the sink, selecting edges based on available capacity. (C) The final path through the interactome becomes the de novo pathway. This pathway may or may not include all of the original inputs (e.g. differentially expressed mRNA or depleted shRNAs). Further, SAMNet allows the simultaneous construction of pathways for multiple conditions. In our investigation we treated the parallel in vitro and in vivo screens as separate conditions. (D) Screening design interrogates in vivo specific regulators of ALL progression. A genome-scale library was introduced to ALL cells in vitro. Representative samples were either maintained in culture or transplanted into mouse models. At time of morbidity, blood and culture samples were re-sequenced to measure shRNA representation.. From: Pathway-based network modeling finds hidden genes in shRNA screen for regulators of acute lymphoblastic leukemia †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ib00040a Click here for additional data file. Click here for additional data file. .
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