Computational Epigenomics and Epitranscriptomics

Preface
References
Contents
Contributors
Chapter 1: DNA Methylation Data Analysis Using Msuite
1 Introduction
2 Materials
3 Methods
3.1 Running Environment and Dependencies
3.2 Building Indices
3.3 Run Msuite
3.4 The Msuite Output
4 Notes
References
Chapter 2: Interactive DNA Methylation Array Analysis with ShinyÉPICo
1 Introduction
2 Materials
3 Methods
3.1 Data Upload
3.2 Quality Control
3.3 Normalization
3.4 Differentially Methylated Position Calculation
3.5 Differentially Methylated Region Calculation
3.6 Results Export
4 Notes
References
Chapter 3: Predicting Chromatin Interactions from DNA Sequence Using DeepC
1 Introduction
2 Materials
2.1 Data
2.1.1 Hi-C Data
2.1.2 Pre-trained Convolutional Filter Weights for Transfer Learning
2.1.3 Trained Models
2.1.4 Additional Data
2.2 Software
2.3 Hardware
3 Methods
3.1 Data Pre-processing
3.1.1 Pre-processing Using the Wrapper Script
3.1.2 Pre-processing Manually
3.1.3 Collate Data for Training
3.2 Training a Model
3.3 Predicting Chromatin Interactions
3.3.1 Run Predictions
3.3.2 Visualize Predictions Using the Wrapper Script
3.3.3 Visualize Predictions Manually
4 Notes
References
Chapter 4: Integrating Single-Cell Methylome and Transcriptome Data with MAPLE
1 Introduction
2 Materials
3 Methods
3.1 Overview of MAPLE
3.2 Methylome Matrix Construction
3.3 Downstream Analysis
3.4 Integration
4 Notes
References
Chapter 5: Quantitative Comparison of Multiple Chromatin Immunoprecipitation-Sequencing (ChIP-seq) Experiments with spikChIP
1 Introduction
2 Materials
2.1 Cell Culture
2.2 Equipment
2.3 Disposables
2.4 Software Requirements
3 Methods
3.1 Preparation of Spike-in Chromatin for ChIP-seq Experiments
3.1.1 Preparation of Drosophila SL2 Cells
3.1.2 Preparation of Drosophila SL2 Chromatin
3.1.3 Quality Control and DNA Quantification from Fragmented Chromatin
3.1.4 Incorporation of Drosophila SL2 Chromatin with Experimental Chromatin Samples
3.2 Computational Analysis of ChIP-seq Data Using Spike-in Chromatin
3.2.1 Generation of the Genome Index for ChIP-seq Mapping
3.2.2 Genome Mapping of each Individual ChIP-seq Sample
3.2.3 Extraction of Aligned Reads into Distinct Genome and Spike-in Files
3.2.4 Identification of ChIP-Enriched Regions on Each Experiment
3.2.5 Preparation of the Configuration File of spikChIP
3.2.6 Preparation of the Genome Definition File of spikChIP
3.2.7 Running spikChIP to Normalize a Collection of ChIP-seq Samples
3.2.8 Interpretation and Usage of spikChIP Output Files
3.2.9 Generation of Genome-Wide Profiles for Graphical Browsers
4 Notes
References
6: A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes i...
1 Introduction
2 Materials
2.1 Summary
2.1.1 Github Clone
2.1.2 Docker
2.1.3 St. Jude Cloud
3 Methods
3.1 Step 1: Response Variable (Only for Transfer Learning)
3.2 Input
3.3 Example Command
3.3.1 Optional Arguments
3.3.2 Optional Example Command
3.4 Output
3.5 Step 2: Feature Extraction
3.6 Input
3.7 Example Command
3.7.1 Optional Arguments
3.7.2 Optional Example Command
3.8 Output
3.9 Step 3: Format
3.10 Input
3.11 Example Command
3.11.1 Optional Arguments
3.11.2 Optional Example Command
3.12 Output
3.13 Step 4: Run Model
3.14 Input
3.15 Example Command
3.15.1 Optional Arguments
3.15.2 Optional Example Command
3.16 Output
3.17 Example Output
3.18 Step 5: Transfer Learning (Optional)
3.19 Input
3.20 Example Command
3.20.1 Optional Arguments
3.20.2 Optional Example Command
3.21 Output
References
Chapter 7: DNA Modification Patterns Filtering and Analysis Using DNAModAnnot
1 Introduction
2 Materials
2.1 Data Sources
2.2 Software and Installation
3 Methods
3.1 Loading Mandatory Files
3.1.1 Import Genome Sequence Information
3.1.2 Import Modification Input Files
3.2 Sequencing Quality Assessment and Filtering
3.3 Analysis of Global Distribution and Motif of DNA Modification Data
3.4 False Discovery Rate Estimations and Filtering (PacBio Only)
3.5 Analysis of DNA Modification Patterns with Genomic Annotations and Other Sequencing Data
3.5.1 Computing Counts by Genomic Feature
3.5.2 Quantitative Parameter by Feature and by Mod Count Categories
3.5.3 Computing Count Within Genomic Features
3.5.4 Computing Distance from Genomic Features
3.5.5 Local Visualization with Gviz
4 Notes
References
Chapter 8: Methylome Imputation by Methylation Patterns
1 Introduction
2 Materials
3 Methods
3.1 Implementation
3.2 Parameters
4 Notes
References
Chapter 9: Sequoia: A Framework for Visual Analysis of RNA Modifications from Direct RNA Sequencing Data
1 Introduction
2 Materials
3 Methods
3.1 Backend Computation
3.2 Visualization Interface
3.2.1 Data Selection
3.2.2 5-mer List
3.2.3 t-SNE Plot
3.2.4 Signal Plot
4 Notes
4.1 Input Files
4.2 Execution (Signal Extraction)
5 Summary and Discussion
References
Chapter 10: Predicting Pseudouridine Sites with Porpoise
1 Introduction
2 Materials
2.1 Software
2.2 Python Environment and Required Packages
2.3 Data Sources
3 Methods
3.1 Local Stand-Alone Version of Porpoise
3.1.1 Sequence Windows
3.1.2 Step-by-Step Usage Guide
3.1.3 Output Format
3.2 Webserver
3.2.1 Online Webserver Layout
3.2.2 Running Predictions Through the Online Webserver
3.3 Auto-pipeline for Model Training
3.3.1 Step-by-Step Details
3.3.2 Outputs
4 Notes
References
Chapter 11: Pseudouridine Identification and Functional Annotation with PIANO
1 Introduction
2 Materials
2.1 Software
2.2 Data Sources
3 Methods
3.1 A High-Accuracy Predictor of Human Ψ Sites Using a Machine Learning Approach
3.1.1 Dataset Preparation for the Machine Learning Approach
3.1.2 Feature Encoding Methods
3.1.3 Model Training and Evaluation
3.1.4 Functional Annotation of Putative Ψ Sites and Probability Estimation
3.2 Using PIANO Website to Obtain the Desired Ψ site
3.2.1 Input File of PIANO
3.2.2 Encoding Types Used to Start the Prediction Job
3.2.3 Result Explanation for Genomic Feature-Based Prediction
3.2.4 Result Explanation for Sequence-Based Prediction
3.2.5 Ψ Site Collection in PIANO Database
4 Notes
References
Chapter 12: Analyzing mRNA Epigenetic Sequencing Data with TRESS
1 Introduction
2 Materials
2.1 Data
2.2 Software
3 Methods
3.1 Download and Preprocess Data
3.2 Prepare R Environment
3.3 Conduct Peak Calling Analysis
3.4 Visualization of Individual Peaks
3.5 Conduct Differential Peak Calling Analysis
4 Notes
References
Chapter 13: Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores
1 Introduction
2 Materials
2.1 Nanopore Sequencing Test Datasets: Total RNA from Wild Type and snoRNA-depleted S. cerevisiae Strains
2.2 Required Infrastructure to Run MoP2 on the Yeast Dataset
2.3 Software Installation
3 Methods
3.1 Tuning the Pipeline Parameters
3.2 Running MoP2 Pipelines
3.3 Mop_preprocess: Pre-processing of FAST5 or FASTQ Files
3.3.1 Mop_preprocess Steps
3.3.2 Mop_preprocess Configuration and Running
3.3.3 Mop_preprocess Output
3.3.4 Mop_preprocess Runtime
3.4 Mop_tail: Estimation of poly(A) Tail Lengths
3.5 Mop_mod: Prediction of RNA Modifications Using Four Different Approaches
3.6 Mop_consensus: Identification of Robust Changes in RNA Modifications Across Two Conditions
3.7 MoP2 Execution Monitoring and Reporting
4 Notes
References
Chapter 14: Data Analysis Pipeline for Detection and Quantification of Pseudouridine (ψ) in RNA by HydraPsiSeq
1 Introduction
2 Materials
2.1 Library Sequencing
2.2 Analysis of Raw Reads´ Quality by FastQC
2.3 Trimming
2.4 Alignment
2.5 Data Processing
2.6 Data Treatment
2.7 Data Analysis
3 Methods
3.1 Library Sequencing
3.2 Analysis of Raw Reads´ Quality by FastQC
3.3 Trimming
3.4 Alignment
3.5 Data Processing
3.6 Data Treatment
3.7 Data Analysis
4 Notes
References
Chapter 15: Analysis of RNA Sequences and Modifications Using NASE
1 Introduction
2 Materials
3 Methods
3.1 Building a Workflow
3.2 The Minimal NASE Workflow
3.3 Adding Decoys
3.4 Adding Label-Free Quantitation
4 Notes
References
Chapter 16: Mapping of RNA Modifications by Direct Nanopore Sequencing and JACUSA2
1 Introduction
2 Materials
2.1 ONT Direct RNA Sequencing
2.2 Preparation of an In Vitro Transcriptome Sample
2.3 Hardware Requirements
2.4 Software Dependencies and Installation
3 Methods
3.1 Nanopore Direct RNA Sequencing
3.2 Preparation of an In Vitro Transcriptome Sample
3.3 Nanopore Read Processing
3.4 Use Case 1: Comparison of Wild-Type and Knockout Samples
3.5 Use Case 2: Comparison of Wild-Type and IVT Samples
4 Notes
References
Index