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| * Zhana Duren, Xi Chen, Rui Jiang, Yong Wang, and Wing Hung Wong. __[Modeling gene regulation from paired expression and chromatin accessibility data|http://bioinformatics.oxfordjournals.org/cgi/content/abstract/22/19/2413]__. ''PNAS'', Vol. 22, No. 19, pp. 2413-2420, 2006. |
| * Zhana Duren, Xi Chen, Rui Jiang, Yong Wang, and Wing Hung Wong. __[Modeling gene regulation from paired expression and chromatin accessibility data|http://www.pnas.org/content/114/25/E4914]__. ''PNAS'', vol. 114 no. 25 E4914-E4923 (2017). |
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| [[file:Picture1.png|right|thumb]] |
| Chromatin plays a critical role in the regulation of gene expression. Interactions among chromatin regulators, sequence-specific transcription factors and cis-regulatory sequence elements are the main driving forces shaping context-specific chromatin structure and gene expression. However, because of the large number of such interactions, direct data on them are often missing in most cellular contexts. The purpose of the present work is to show that, by modeling matched expression and accessibility data across diverse cellular contexts, it is possible to recover a significant portion of the information in the missing data on binding locations and chromatin states and simultaneously, to achieve accurate inference of gene regulatory relations. |
| The rapid increase of genome-wide data sets on gene expression, chromatin states and transcription factor (TF) binding locations offers an exciting opportunity to interpret the information encoded in genomes and epigenomes. This task can be challenging as it requires joint modeling of context specific activation of cis-regulatory elements (RE) and the effects on transcription of associated regulatory factors. To meet this challenge, we propose a statistical approach based on paired expression and chromatin accessibility (PECA) data across diverse cellular contexts. In our approach, we model 1) the localization to REs of chromatin regulators (CR) based on their interaction with sequence-specific TF, 2) the activation of REs due to CRs that are localized to them, 3) the effect of TFs bound to activated REs on the transcription of target genes (TG). The transcriptional regulatory network inferred by PECA provides a detailed view of how trans- and cis-regulatory elements work together to affect gene expression in a context specific manner. |
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| Our analytical approach for learning from this data is to model the distribution of the expression of target genes (TG) conditional on the accessibility of regulatory elements and the expression of transcription factors (TF) and chromatin regulators (CR). |
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| * __Step 1__: Format the Multiple Microarray Datasets into desired data file (See the attached example file as a reference). |
| * __Step 2__: Open the data file by click the 'open' button and browse the data file location. |
| * __Step 3__: Choose proper parameters. There are two parameters you can choose by altering the default value. Specifically, |
| ** __Lambda__: This parameter is used in the inferring algorithm to adjust the sparsity of the structure. The default value is 0.0. |
| ** __Threshold__: This parameter is used in the control the output file GRN.dot, which can be visualized by the neato tool of the software package Graphviz. The threshold parameter controls the number of the edge whose strength of link is smaller than Threshold not shown in the network graph. The smaller this parameter, the more edges in the network graph. The default value is set to 1e-6. |
| * __Step 4__: Computing by click the 'Infer' button when the data file and parameters are ready. |
| * __Step 5__: Checking the results. There are two files are output in the same directory with the software. |
| ** __GRN.dat__: This file records the matrix assessing the strengths of edges of the network, i.e. the element located on the i-th row and the j-th column means the regulatory strength from the j-th gene to the i-th gene, the sign + means the activation and the sign - means repression. |
| ** __GRN.dot__: This file records the information to output the network structure which can be illustrated by Graphviz. Note that only the edges whose regulatory strength in GRN.dat is larger than Threshold parameter are shown. The software Graphviz can be downloaded from [http://www.graphviz.org] freely. |
| !!Useful Resource: |
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| Download 25 bio-sample matched RNA-seq and DNase-seq data from mouse ENCODE project. |
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| Download regulatoryElement-targetGene association. |
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| Download PECA predicted whole network and context specific network for 25 tissues or cell type. |
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| !!Using PECA Tools: |
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| PECA-TNet: Matlab tool for context specific network |
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| Input: paired expression of genes (Exp) and chromatin accessibility of regulatory elements (Opn) on your interested cellular context. |
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| Output: context specific network (TissueSpecificNet). |
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| Usage: TissueSpecificNet=PECATool1(Exp,Opn,GeneName,ElementName); |
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| Where GeneName and ElementName are name of gene and regulatory elements. |
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| PECA-Anno: Matlab tool for genomic region annotation by network |
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| Input: Bed file of the genomic region you interested (region.bed’). |
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| Output: annotation network of your region (RegionNet). |
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| Usage: RegionNet=PECATool2(‘region.bed’); |
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| * [GRNInfer.zip|GRNInfer/GRNInfer.zip] |
| * [PECA|http://web.stanford.edu/~zduren/PECA/] |
