Hi-C Data Analysis: Methods and Protocols (Methods in Molecular Biology, 2301)

✍ Scribed by Silvio Bicciato (editor), Francesco Ferrari (editor)

• Publisher: Humana
• Year: 2021
• Tongue: English
• Leaves: 355
• Category: Library

✦ Synopsis

This volume details a comprehensive set of methods and tools for Hi-C data processing, analysis, and interpretation. Chapters cover applications of Hi-C to address a variety of biological problems, with a specific focus on state-of-the-art computational procedures adopted for the data analysis. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Hi-C Data Analysis: Methods and Protocols aims to help computational and molecular biologists working in the field of chromatin 3D architecture and transcription regulation.

✦ Table of Contents

Preface
Contents
Contributors
Chapter 1: Normalization of Chromosome Contact Maps: Matrix Balancing and Visualization
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
3 Methods
3.1 Alignment of Read Pairs
3.2 Filtering of Non-informative Events
3.3 Iterative Procedure to Balance the Signal
3.4 Scalogram: Alternative Visualization Tool for Normalized Contact Data
4 Notes
References
Chapter 2: Methods to Assess the Reproducibility and Similarity of Hi-C Data
1 Introduction
1.1 Similarity Measures for Hi-C Data
1.1.1 HiCRep
1.1.2 GenomeDISCO
1.1.3 HiC-Spector
2 Data Sets
2.1 Description of the Example Data
2.2 Download the Data
2.3 Data Formats for Contact Maps
2.4 Converting .bam Files into Matrix Formats
3 Assessing Reproducibility Scores
3.1 HiCRep
3.1.1 System Requirements and Installation
3.1.2 Input Format for Contact Maps
3.1.3 Compute Similarity Score Using HiCRep
3.1.4 Demo Code
3.1.5 Output
3.2 HiC-Spector
3.2.1 System Requirements and Installation
3.2.2 Input Format for Contact Maps
3.2.3 Compute Reproducibility Score Using HiC-Spector
3.2.4 Parameters in HiC-Spector
3.2.5 Demo Code
3.2.6 Output
3.3 GenomeDISCO
3.3.1 System Requirement and Installation
3.3.2 Input Format for Contact Maps
3.3.3 Other Required Input Files
3.3.4 Parameters in GenomeDISCO
3.3.5 Compute the Reproducibility Score Using GenomeDISCO
3.3.6 Output
4 Notes
References
Chapter 3: Methods for the Analysis of Topologically Associating Domains (TADs)
1 Introduction
1.1 The 3D Genome and Topologically Associating Domains (TADs)
1.2 Detecting TADs in Hi-C Maps
1.3 Comparing and Assessing the Quality´´ of Genomic Partitions 2 Materials 2.1 Required Resources 2.1.1 Hardware 2.1.2 Software 2.2 Datasets 3 Methods 3.1 TAD Calling 3.1.1 TopDom Input format Parameters Running TopDom Output format 3.1.2 CaTCH Input format Parameters Running CaTCH Output format 3.1.3 Arrowhead Input format Parameters Running Arrowhead Output format 3.1.4 HiCseg Input format Parameters Running HiCseg Output format 3.2 Comparison of TAD Partitions 3.2.1 The Measure of Concordance 3.2.2 Enrichment of Structural Proteins at TAD Boundaries 3.2.3 Enrichment of either Activating or Repressing Histone Marks within TADs 4 Notes References Chapter 4: Methods for the Differential Analysis of Hi-C Data 1 Introduction 1.1 What We Can C 1.2 From Contacts to Bytes 2 Materials 2.1 Necessary Resources 2.1.1 Hardware 2.1.2 Software 2.2 Data Sets 2.2.1 Description of the Example Data 2.2.2 Data Download 2.2.3 Data Conversion 3 Methods 3.1 Differential Interactions Analysis with no Replicates 3.1.1 HiCCUPS Diff 3.1.2 Selfish 3.2 Differential Interaction Analysis with Replicates 3.2.1 diffHic 3.2.2 multiHiCcompare 3.3 Results Visualization 3.4 Integration with Gene Expression 4 Notes References Chapter 5: Visualizing and Annotating Hi-C Data 1 Introduction 2 Materials 2.1 Required Resources 2.1.1 Hardware Requirements 2.1.2 Software Requirements 2.1.3 Installation Commands Homebrew and Linuxbrew Installing R Installing R with brew Installing Renv and R from Source Installing R Packages 2.2 Datasets 2.2.1 Example Hi-C Dataset 2.2.2 Example Histone Marks ChIP-Seq Data 3 Methods 3.1 Importing Hi-C Data into R 3.2 Visualizing Hi-C Contact Maps 3.2.1 Selecting the Region to Visualize 3.2.2 Standard Genomic Loci Based Heatmaps 3.2.3 Rotated Distance-Based Heatmaps 3.2.4 Annotating Hi-C Data with Domain Information Calling Chromatin Domains Filter Domains for Non-informative Regions Annotate the Contact Maps with Domains 3.2.5 Annotate Contact Maps with Histone Marks and TADs Fetch the Bin Positions of the Region of Interest Import Histone Marks Summarize Histone Marks Data at the Same Resolution as Hi-C Data Convert Histone Marks Track to Coordinates Assemble the Complete Plot 3.3 Hi-C Interaction Decay Analysis 3.3.1 Retrieve average interactions for each distance 3.3.2 Plot Interaction Decay Profile 3.4 Aggregated Hi-C Contact Profiles 3.4.1 Average Domain Identify Domains across all Chromosomes Compute Expected Values Rescale Sub-Matrices to a Fixed Dimension Plot the Average Profile 3.4.2 Paired-End Spatial Chromatin Analysis Fetch the Domain Boundary Bins Create and Filter Boundary Pair List Fetch All Sub-Matrices Plot the Average Profile 4 Notes References Chapter 6: Hi-C Data Formats 1 Introduction 2 Data Formats Per Processing Steps 2.1 Formats for Alignments 2.2 Formats for a Contact List 2.3 Formats for a Contact Matrix 2.3.1 The .hic Format 2.3.2 The .cool and .mcool Formats 2.3.3 Other Formats 2.4 Loops, Domains, and Compartments 2.4.1 Calls 2.4.2 Profiles 2.5 Future Direction 3 Notes References Chapter 7: Analysis of Hi-C Data for Discovery of Structural Variations in Cancer 1 Introduction 2 Materials 2.1 Computation Resource Requirement 2.1.1 System Requirement 2.1.2 Software Requirement 2.2 Tool Installation 2.2.1 Install BWA 2.2.2 Install Samtools 2.2.3 Install Conda 2.2.4 Install Pairtools 2.2.5 Install Cooler 2.2.6 Install HiGlass 2.2.7 Install Hi-C Breakfinder 2.3 Example Data Sets 3 Methods 3.1 Overview of the SV Discovery Pipeline Using Hi-C Breakfinder 3.2 Hi-C Read Alignment 3.3 SV Discovery with Hi-C Breakfinder 3.4 Visualization of SVs on Normal Hi-C Maps 3.5 Visualization of SVs on Reconstructed Hi-C Maps 4 Notes Appendix Python script for making reconstructed Hi-C map at SV loci References Chapter 8: Metagenomes Binning Using Proximity-Ligation Data 1 Introduction 2 Materials 2.1 Required Resources 2.1.1 Hardware 2.1.2 Software 3 Methods 3.1 Preliminary Assembly 3.2 Contigs Network of Interactions 3.2.1 Mapping Reads along the Metagenome Assembly 3.2.2 Computation of Contigs Data 3.2.3 Filtering Out of Non-informative Contacts and Construction of the Network 3.3 Partitioning of the Network 3.3.1 Louvain Iterative Procedure 3.3.2 Louvain Data Treatment 3.3.3 Subnetwork Extraction 3.3.4 Louvain Recursive Procedure on Subnetwork 3.4 Downstream Analysis 4 Notes References Chapter 9: Generating High-Resolution Hi-C Contact Maps of Bacteria 1 Introduction 2 Materials 2.1 Equipment 2.2 Consumables for Hi-C Library Preparation 2.3 Consumables for Sequencing Library Preparation 3 Methods 3.1 Generation of a Bacterial Hi-C Library 3.1.1 Cell Fixation 3.1.2 Hi-C Library Construction 3.1.3 Reverse Cross-Linking and DNA Purification 3.2 Preparation of Hi-C Sequencing Libraries 3.2.1 DNA Sonication and Size Selection 3.2.2 Biotin Pull-Down 3.2.3 End-Repair 3.2.4 A-Tailing Adapter Ligation 3.2.5 Library Amplification by PCR 4 Notes References Chapter 10: Computational Tools for the Multiscale Analysis of Hi-C Data in Bacterial Chromosomes 1 Introduction 2 Methods 2.1 Using Our Framework 2.2 Input Data 2.3 Matrix-Based Approach Using numpy Arrays 2.4 Estimating the Contact Law P(s) 2.5 Highlighting the Frontiers of a Hi-C Heat Map 2.6 Frontier Indexes 2.6.1 Estimating a p-Value 3 Notes References Chapter 11: Analysis of HiChIP Data 1 Introduction 2 Materials 2.1 Required Resources 2.1.1 Hardware 2.1.2 Software 2.2 Dataset 2.2.1 Example Dataset: Generalities 2.2.2 Downloading HiChIP Data 2.2.3 Downloading Additional Data 3 Methods 3.1 Preprocessing of Raw Data with HiC-Pro 3.1.1 Input and Configuration Files 3.1.2 Alignment 3.1.3 Filtering 3.2 Identification of Loops 3.2.1 ChIP-Seq Peak Calling 3.2.2 Identification of HiChIP Loops 3.2.3 Calling Differential Loops 3.3 Loop Visualization 3.3.1 Plotting Loops with Diffloop 3.3.2 Exploring Interactions with WashU Epigenome Browser 4 Notes References Chapter 12: The Physical Behavior of Interphase Chromosomes: Polymer Theory and Coarse-Grain Computer Simulations 1 On a Few Fundamental Facts Concerning Chromosome Folding 2 Polymer Physicsin a Nutshell´´: Theory and Simulation
2.1 Theory
2.2 Simulation
2.2.1 Monte Carlo Methods: Metropolis Algorithm
2.2.2 Molecular Dynamic Methods
3 Untangled Ring Polymers in Melt and the Physical Modeling of Interphase Chromosomes
3.1 Ring Polymers in Melt as a Model for Interphase Chromosomes: A Biological Basis
3.2 Physics of Ring Polymers in Melt: Models and Methods
3.3 Constructing Double-Folded, Randomly Branched Ring Polymers in Melt: An Efficient Monte Carlo/Molecular Dynamic Multi-scal...
3.4 Model Predictions and Comparison to Experimental Results
4 Conclusions and Outlook
References
Chapter 13: Polymer Folding Simulations from Hi-C Data
1 Introduction
2 Methods
2.1 The Polymeric Model
2.2 Determination of the Effective Potential
2.3 Analysis of the Results
3 Notes
References
Chapter 14: Predictive Polymer Models for 3D Chromosome Organization
1 Introduction
2 Mechanisms Driving Chromosome Organization
2.1 Diffusing Transcription Factor Model: Bridging-Induced Phase Separation
2.2 The Loop Extrusion Model: Cohesin-Mediated Domains
2.3 Block Copolymer Models: Chromatin Self-Interactions and A/B Compartments
3 The HiP-HoP Model
3.1 Simulation Methods
3.2 Simulation Input Data
4 HiP-HoP Simulation Results
4.1 HiP-HoP at Specific Gene Loci
4.2 HiP-HoP at the Chromosome Scale
5 Discussion and Future Outlook
6 Notes
References
Chapter 15: Polymer Modeling of 3D Epigenome Folding: Application to Drosophila
1 Introduction
2 Methods
2.1 Coarse-Grained Polymer Model for Chromosome
2.2 Block Copolymer Model for Epigenome Folding
2.3 Lattice Model and Numerical Simulations
2.4 Comparison with Experiments
3 Notes
References
Chapter 16: A Polymer Physics Model to Dissect Genome Organization in Healthy and Pathological Phenotypes
1 Introduction
2 SBS Polymer Model and PRISMR Inference Method
3 Predicting the Impact of SVs on the EPHA4 Locus Architecture
4 Pitx1 Regulation Is Explained by Tissue-Specific 3D Conformations
5 Discussion
References
Chapter 17: The 3D Organization of Chromatin Colors in Mammalian Nuclei
1 Introduction
2 Materials
2.1 Required Resources
2.1.1 Datasets
2.1.2 File Formats
2.1.3 Software
3 Methods
3.1 Overview of the Results
3.2 3D Reconstruction in MATLAB
3.3 3D Annotation in MATLAB
3.4 3D Reconstruction and Epigenetic Annotation in Python
3.4.1 Main.py
3.4.2 HiCtoolbox.py
4 Notes
References
Chapter 18: Modeling the 3D Genome Using Hi-C and Nuclear Lamin-Genome Contacts
1 Introduction
2 Materials
2.1 Required Software Needed to Run the Pipeline
2.2 Preparations Prior to Running the Pipeline
2.2.1 Setting up HiC-Pro
2.2.2 Downloading and Organizing the Required Hi-C Data
2.2.3 Downloading and Organizing the Required LAD Data
2.2.4 Downloading and Installing the Required Processing Scripts
3 Methods
3.1 Run HiC-Pro to Process Hi-C Data
3.2 Run Armatus to Call TADs
3.3 Convert Called TADs into a Segmented Genome to Define Chrom3D Beads
3.4 Computing Inter-Bead Contact/Interaction Frequencies
3.5 Identifying Statistically Significant Inter-Bead Interactions within Chromosomes
3.6 Identifying Statistically Significant Inter-Bead Interactions between Chromosomes
3.7 Creating a Model Setup File in GTrack Format
3.8 Adding LAD Information to the Model Setup File (GTrack
3.9 Making the Model Setup File (GTrack) Diploid
3.10 Running Chrom3D
3.11 Visualizing the Resulting Chrom3D Model in ChimeraX
4 Notes
References
Index