Modeling Transcriptional Regulation: Methods and Protocols

Preface
Contents
Contributors
Chapter 1: Co-expression Networks in Predicting Transcriptional Gene Regulation
1 Introduction
2 Arabidopsis Transcriptional Data for Multiple Stress Conditions
3 Transcriptome Analysis Console (TAC)
4 Arabidopsis Protein-Protein Interaction (PPI) Network
5 MDraw: Network Motif Analysis Tool
6 Cytoscape: Network Visualization and Analysis Software
7 Methods
7.1 Normalizing Arabidopsis Microarray Data
7.2 Principle Component Analysis (PCA)
7.3 Identification of Differentially Expressed Genes (DEGs)
7.4 Identification of Co-expressing Genes and Network Motifs Across the Datasets
7.5 Visualization and Analysis of Co-expression Network
8 Notes
References
Chapter 2: Inference of Gene Coexpression Networks from Bulk-Based RNA-Sequencing Data
1 Introduction
2 Materials
3 Methods
3.1 Sequencing Depth
3.2 Identify Highly Expressed Genes
3.3 Negative Binomial Model
3.3.1 iCC
3.4 Normalization-Based Models
3.4.1 Variance Stabilizing Transformation
3.4.2 Other Normalization Methods
3.4.3 Weighted Gene Coexpression Network Analysis
4 Notes
References
Chapter 3: Genomic Footprinting Analyses from DNase-seq Data to Construct Gene Regulatory Networks
1 Introduction
2 Materials
3 Methods
3.1 Identification of DHSs
3.1.1 Preparing the Genome-Specific Files
3.1.2 Aligning DNAse-Seq Reads to the Genome
3.1.3 Filtering the Alignment Files
3.1.4 Identification of Open Chromatin Regions
3.2 Scanning for TF Binding Motifs within DHSs
3.2.1 Collecting and Formatting TF Binding Sites
3.2.2 Extracting DNA Sequence from Open Chromatin Regions
3.2.3 Scanning for TF Binding Motifs Within Open Chromatin Regions
3.3 Genomic Footprinting
3.3.1 Installation and Execution of CENTIPEDE
3.3.2 TF/Motif Assignation
3.3.3 Assigning Footprints to Genes
3.4 Network Visualization
3.4.1 Selection of Genes and Network Preparation
3.4.2 Network Visualization in Cytoscape
4 Notes
References
Chapter 4: Spatiotemporal Gene Expression Profiling and Network Inference: A Roadmap for Analysis, Visualization, and Key Gene...
1 Introduction
2 Materials
2.1 TuxNet Architecture
2.2 Software Versions
2.3 TuxNet Website
3 Methods
3.1 Designing Experiments to Address Specific Hypotheses
3.2 Analyzing Raw Data to Prepare for Network Inference
3.3 Selecting and Assessing DEGs
3.4 Selecting Clustering Datasets and Methods
3.5 Selecting a Network Inference Technique
3.6 Visualizing and Assessing Inferred Networks to Draw Conclusions
3.7 Using Network Motifs to Select Candidate Genes for Further Research
4 Notes
References
Chapter 5: Dynamic Modeling of Transcriptional Gene Regulatory Networks
1 Introduction
2 Gene Circuits
2.1 Gene Circuit Models of GRNs
2.2 The Gap Gene GRN of Drosophila
2.3 Inference of GRN Connectivity and Parameters from Quantitative Gene Expression Data
2.3.1 Training Data
2.4 Fast Inference of Gene Regulation (FIGR)
2.4.1 Determining the ON/OFF State of the Genes
Spline Fits
Velocities
Assigning ON/OFF Gene States
2.4.2 Inference of Gene Circuit Parameters
Binary Classification to Infer the Regulatory Parameters
Inference of the Kinetic Parameters
2.4.3 Refinement
3 Materials
4 Methods
4.1 Choosing the GRN and Experimental Design
4.2 Obtaining FIGR
4.3 Defining FIGR Options
4.4 Supplying Input Data
4.4.1 Time Point Data File Format
4.4.2 Gene Expression Data File Format
4.4.3 Reading the Files into MATLAB Workspace
4.5 Inferring the GRN Using Infer( )
4.6 Optional Refinement of the GRN Using refineFIGRParams( )
4.7 Simulating and Analyzing the GRN
4.7.1 Evaluating How Well the Model Fits the Data
4.7.2 Inferring Genetic Interactions in the GRN
4.7.3 Perturbations and Predictions
4.8 Example Script for Inferring and Simulating the Gap Gene GRN
Bibliography
Chapter 6: Mathematical Programming for Modeling Expression of a Gene Using Gurobi Optimizer to Identify Its Transcriptional R...
1 Introduction
1.1 Linear Model Function
1.2 Linear Model for Multiple Independent Variables
1.3 Gurobi Optimizer for Linear Modeling
2 Material
3 Methods
3.1 Gurobi Optimizer Installation
3.2 Gene Expression Data
3.3 Preparing the Gurobi Input Model File
3.4 Running Gurobi with Input Model File
3.5 Interpreting the Gurobi Output
3.6 Predicting Gene Expression
3.7 Manipulating Linear Model with Prior Information
4 Notes
References
Chapter 7: Multiscale Modeling of Cross-Regulatory Transcript and Protein Influences
1 Introduction
2 Materials
3 Methods
3.1 Model Development
3.2 Model Prediction
3.3 Network Topology Analysis
3.4 Example Implementation
3.4.1 Model Development
3.4.2 Model Prediction
3.4.3 Network Topology Analysis
4 Notes
References
Chapter 8: Biological Network Mining
1 Introduction
2 Materials
2.1 BEERE Webserver
2.2 WIPER API Service
2.3 PAGER Webserver
3 Method
3.1 Ranking Biomedical Entities and Visualizing Networks
3.2 Ranking Biomedical Entity-to-Entity Associations
3.3 Performing Gene Set Enrichment Analysis Using PAGER
4 Notes
References
Chapter 9: Identification of Gene Regulatory Networks from Single-Cell Expression Data
1 Introduction
2 Materials
2.1 System Requirement and Pipeline Download
2.2 Folder Structure
2.3 Data Download and Software Installation
2.3.1 Single-Cell Expression Data Download
2.3.2 Installation of R and Required R Packages
2.3.3 Installation of Python and Required Python Packages
2.4 Download DAP-seq and ATAC-seq Data
3 Methods
3.1 Data Normalization and Integration
3.1.1 R Command to Import Drop-seq Data
3.1.2 R Command to Import 10x Genomics Data
3.1.3 R Command for Cross-Study Integration (Optional)
3.2 Identify Cell Types Using ICI and Generate Gene Lists
3.3 Network Analysis Using ConSReg
4 Notes
References
Chapter 10: Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC
1 Introduction
1.1 GENIE3
1.2 PPCOR
1.3 GRNBoost2
1.4 MICRAT
1.5 PIDC
2 Materials
3 Methods
3.1 Preprocessing and Cleaning of the Data (Python Shell)
3.1.1 Import Python Libraries
3.1.2 Setup the File and Folder Variables (Python Shell)
3.1.3 Cleaning the Data (Python Shell)
3.1.4 Quality Control of TPM Data (Python Shell)
3.2 Gene Regulatory Network (GRN) Analysis Using grnboost2 (See Note 3)
3.3 Create Regulons
3.4 Identify Cells with Active Gene Sets
3.5 Save Loom File on Disk (Python Shell).
4 Notes
References
Chapter 11: Modeling Immune Dynamics in Plants Using JIMENA-Package
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 12: Dynamic Regulatory Event Mining by iDREM in Large-Scale Multi-omics Datasets During Biotic and Abiotic Stress in P...
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 13: A Semi-In Vivo Transcriptional Assay to Dissect Plant Defense Regulatory Modules
1 Introduction
2 Materials
3 Methods
3.1 Cloning
3.2 Infiltration by Agrobacterium and Confirmation of Transformation
3.3 Noninvasive Luciferase Measurement
3.4 Luciferase Measurement Using DLR System
3.5 Sample Collection
3.6 Luciferase Measurement
4 Notes
References
Chapter 14: Assessing Global Circadian Rhythm Through Single-Time-Point Transcriptomic Analysis
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 15: High-Throughput Targeted Transcriptional Profiling of Defense Genes Using RNA-Mediated Oligonucleotide Annealing, ...
1 Introduction
2 Materials
2.1 Plant Material and RNA
2.2 Equipment
2.3 Reagents
3 Methods
3.1 Designing RASL Probes
3.2 Preparation of Equilibrated Biotinylated Oligo-dT Streptavidin-Coated Beads
3.3 Preparing RASL-seq Library
3.4 Quantitation from Sequencing Data
4 Notes
References
Chapter 16: Rapid Validation of Transcriptional Enhancers Using a Transient Reporter Assay
1 Introduction
2 Materials
2.1 Vector Construction
2.2 Agroinfiltration Preparation
3 Methods
3.1 Enhancer DNA Fragments Preparation
3.2 Vector Construction and Transformation
3.3 Plant Growth
3.4 Agrobacterium-Mediated Leaf Transformation
3.5 Photographing Photon-Counting Experiments and Data Normalization
3.5.1 For Enhancer Characterization
3.5.2 For Time-Lapse Tracking
4 Notes
References
Chapter 17: Computational Identification of ceRNA and Reconstruction of ceRNA Regulatory Network Based on RNA-seq and Small RN...
1 Introduction
2 Materials
2.1 Software and Programs
2.2 Databases and Online Services
2.3 Input Files
3 Methods
3.1 Identification of Transcribed Loci (See Note 1)
3.2 Annotation of Transcribed Loci (See Note 3)
3.3 Investigation of Annotated miRNA Loci
3.4 Identification of Novel miRNA Loci (See Note 4)
3.5 Identification of miRNA Target Gene
3.6 Identification of ceRNA
3.7 Expression Correlation Among ceRNA, miRNA, and Target Genes
3.8 Reconstruction and Display ceRNA Regulatory Network
4 Notes
References
Chapter 18: In Silico Prediction for ncRNAs in Prokaryotes
1 Introduction
2 Materials
2.1 Reference Prokaryotic Genomes
2.2 RNA-Seq Data
2.3 Annotation of ncRNA Sequences
2.4 Bioinformatics Tools
2.5 Artemis
2.6 BLAST
2.7 Infernal 1.1.2 (INFERence of RNA Alignment)
2.8 TargetRNA2
2.9 SAMTools
2.10 Transcriptome Analysis
3 Methods
References
Chapter 19: Mathematical Linear Programming to Model MicroRNAs-Mediated Gene Regulation Using Gurobi Optimizer
1 Introduction
1.1 A Linear Model Function
1.2 A Linear Model for Multiple Independent Variables
1.3 Gurobi Optimizer for Linear Modeling
2 Material
3 Methods
3.1 Gurobi Optimizer Installation
3.2 Gene Expression Data
3.3 Preparing the Gurobi Input Model File
3.4 Running Gurobi with Input Model File
3.5 Interpreting Gurobi Output
3.6 Predicting Gene Expression
4 Notes
References
Index