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Figure 1. Unified models predicting gene regulation based on landscapes of gene-regulating factors. For each gene, position specific combinatorial patterns.

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Presentation on theme: "Figure 1. Unified models predicting gene regulation based on landscapes of gene-regulating factors. For each gene, position specific combinatorial patterns."— Presentation transcript:

1 Figure 1. Unified models predicting gene regulation based on landscapes of gene-regulating factors. For each gene, position specific combinatorial patterns of 52 gene regulators in promoter regions in ESCs are used to predict cell type specific gene regulation using Gaussian process models. From: Gene-regulatory interactions in embryonic stem cells represent cell-type specific gene regulatory programs Nucleic Acids Res. 2017;45(18): doi: /nar/gkx752 Nucleic Acids Res | © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact

2 Figure 2. The integrative patterns of transcription factors and chromatin modifications significantly explain gene expression levels in mouse embryonic stem cells. (A) Integrative patterns of 52 gene regulators in ESCs efficiently predict mRNA levels in ESCs measured in mRNA-sequencing experiments. The models are trained by using genes and tested by using the remaining 10,000 genes. The correlation coefficients between measured and predicted mRNA levels are around 0.8, P-value ≈ 0. (B) Predicting gene expression levels in ESC from the patterns of a single gene regulator and the classes of gene regulators: six chromatin domain associated factors, 10 chromatin modifiers, 15 transcription factors, 21 chromatin modifications, and all 52 factors. Boxplots show distributions of the correlation coefficients between the predicted and measured values of 1000 performance tests of model training by using randomly selected 5000 genes for the respective functional classes of gene regulators. From: Gene-regulatory interactions in embryonic stem cells represent cell-type specific gene regulatory programs Nucleic Acids Res. 2017;45(18): doi: /nar/gkx752 Nucleic Acids Res | © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact

3 Figure 3. The integrated patterns of gene regulators in ESCs fully explain differential gene expression between NPCs and ESCs. (A) Inferring differential gene expression in NPCs from patterns of 52 gene regulating factors in ESCs. (B) Inferring differential gene regulation in NPCs from patterns of six chromatin domain associated factors, 10 chromatin modifiers, 15 transcription factors, 21 chromatin modifications, and all 52 factors in ESCs. Negative control shows correlation coefficients between the predicted NPC-specific gene regulations based on whole 52 factors and measured adult liver-specific gene regulation. (C) Inferring differential gene expression levels in NPCs from patterns of a single gene regulator in ESCs. The top 10 highly predictive factors are marked. From: Gene-regulatory interactions in embryonic stem cells represent cell-type specific gene regulatory programs Nucleic Acids Res. 2017;45(18): doi: /nar/gkx752 Nucleic Acids Res | © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact

4 Figure 4. The differential gene regulation in adult liver cells and B cell progenitors are effectively predicted by the integrative pattern of the gene regulators in ESCs. (A) The integrative patterns of gene regulators in ESCs efficiently predict differential gene expression in adult liver cells and B cell progenitors from ESCs measured by using cDNA microarray experiments. The correlation coefficients between measured and predicted differential levels are 0.71 and 0.72 for adult liver cells and B cell progenitors, respectively. The models are trained by using genes and tested by using the remaining genes. (B) The differential gene regulation levels in both adult liver cells and B cell progenitors are not sufficiently explained by using a subset of gene regulators in functional classes, i.e. chromatin domain factors, chromatin modifiers, transcription factors, and chromatin modifications including DNA methylations. Boxplots show distributions of the correlation coefficients between predicted and measured values of 1000 performance tests of model training by using randomly selected 5000 genes for the respective functional classes of gene regulators. From: Gene-regulatory interactions in embryonic stem cells represent cell-type specific gene regulatory programs Nucleic Acids Res. 2017;45(18): doi: /nar/gkx752 Nucleic Acids Res | © The Author(s) Published by Oxford University Press on behalf of Nucleic Acids Research.This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact

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