Plant Bioinformatics: Methods and Protocols

Preface
Contents
Contributors
Chapter 1: Using GenBank and SRA
	1 Introduction
	2 Materials
		2.1 The GenBank Database
			2.1.1 The Composition of GenBank
			2.1.2 GenBank Records and Accessions
			2.1.3 Plant Sequences in GenBank
			2.1.4 Submitting Sequence Records to GenBank
			2.1.5 Integration of GenBank Data with Other Resources
		2.2 Accessing GenBank
			2.2.1 Interactive Access with Entrez
			2.2.2 Scripted Access with the Entrez Programming Utilities
			2.2.3 Bulk Downloads of GenBank via FTP
		2.3 The Sequence Read Archive (SRA)
			2.3.1 The Composition of SRA
			2.3.2 SRA Records and Accessions
			2.3.3 Plant Sequences in SRA
			2.3.4 Submitting Data to SRA
			2.3.5 Integration of SRA Data with Other Resources
		2.4 Accessing SRA Data
			2.4.1 Interactive Access with Entrez
			2.4.2 Scripted Access Through Entrez with the Entrez Programming Utilities
			2.4.3 Downloading SRA Data with the SRA Toolkit
			2.4.4 Accessing SRA Data in the Cloud
	3 Methods
		3.1 Download All Genomes of Green Plants
			3.1.1 Strategy
			3.1.2 Execution
		3.2 Download a Set of GenBank Sequences for a Single Plant Species
			3.2.1 Strategy
			3.2.2 Execution
		3.3 Download SRA Data for a Plant Dataset
			3.3.1 Strategy
			3.3.2 Execution: Download to Local Storage
			3.3.3 Execution: Cloud Platforms
		3.4 Establish and Perform Sequence Similarity Searches on a Local Database of Plant Sequences
			3.4.1 Strategy
			3.4.2 Execution: Using Databases on NCBI Servers
			3.4.3 Execution: Using Local Databases
			3.4.4 Execution: Using Databases in the Cloud
		3.5 Investigating the Taxonomic Distribution of a Metagenomic Dataset
			3.5.1 Strategy
			3.5.2 Execution
	4 Notes
	References
Chapter 2: Scripting Analyses of Genomes in Ensembl Plants
	1 Introduction
	2 Materials
		2.1 Database Structure and Data Access
		2.2 Overview of Data Content
			2.2.1 Genomes and Core  Data
			2.2.2 Variation  Data
			2.2.3 Comparative Genomics  Data
			2.2.4 Baseline Expression  Data
			2.2.5 RNA-seq Tracks
	3 Methods
		3.1 Clone the GitHub Repository and Install Dependencies
		3.2 Perl API Recipes
			3.2.1 Get a BED File with Repeats on Chromosome 4
			3.2.2 Get Markers Mapped on Chromosome 1D of Bread Wheat
		3.3 R Biomart Recipes
		3.4 FTP Recipes
			3.4.1 Download Soft-Masked Genomic Sequences
			3.4.2 Download All Homologies in a Single TSV  File
		3.5 MySQL Recipes
			3.5.1 Count Protein-Coding Genes of a Particular Species
			3.5.2 Get stable_ids of Transcripts Used in Compara Analyses
			3.5.3 Get Variants Significantly Associated with Phenotypes
		3.6 REST Recipes
			3.6.1 Find Features Overlapping a Genomic Region
			3.6.2 Check Consequences of SNPs Within CDS Sequences
		3.7 Annotate the Effect of Variants with the Ensembl Variant Effect Predictor
		3.8 Querying Plant Pangenomes
		3.9 Getting  Help
	4 Notes
	References
Chapter 3: CyVerse for Reproducible Research: RNA-Seq Analysis
	1 Introduction
		1.1 RNA-Seq Example Use Case
	2 Materials
		2.1 Tutorial Dataset
		2.2 CyVerse Account
		2.3 Applications
			2.3.1 SRA-Tools
			2.3.2 FastQC
			2.3.3 Kallisto and Sleuth
			2.3.4 RStudio
			2.3.5 Spreadsheet Software
	3 Methods
		3.1 CyVerse Account Setup
		3.2 Discovery Environment Login and Data Sharing Setup
		3.3 Obtain Accession Numbers and Metadata from the SRA
		3.4 Upload Files to the Data Store
		3.5 Import Files from SRA with the SRA Toolkit
		3.6 Organize Files, Validate Import, and Extract to FASTQ Format
		3.7 Apply Metadata to FASTQ Files
		3.8 QC Reads with FastQC
		3.9 Quantify Reads with Kallistio
		3.10 Prepare Experimental Design Metadata for Sleuth
		3.11 Evaluate Differential Expression with Sleuth
		3.12 Conclusion
	4 Notes
	References
Chapter 4: Doing Genetic and Genomic Biology Using the Legume Information System and Associated Resources
	1 Introduction
	2 Materials (LIS Technologies and Implementation)
		2.1 Main Site and Database
		2.2 Data Store
		2.3 DSCensor, for Quality Control and Visualization of Datasets in the Data Store
		2.4 Genome Context Viewer
		2.5 Genome Viewers
		2.6 InterMine Instances
		2.7 The Genotype Comparative Visualization Tool (GCViT)
		2.8 Sequence Search Using SequenceServer
		2.9 The ZZBrowse Viewer for GWAS and QTL Experiments
		2.10 Genetic Map Viewers: CMap and CMap-js
		2.11 Geographic Information System and Germplasm Map for Legume Germplasm
		2.12 Expression Resources and Viewers
		2.13 Gene Family Resources and Viewers
	3 Methods
		3.1 Using LIS and Allied Resources to Do Biology: A Use Case Focusing on Control of Flowering Time
		3.2 Do Genetic (GWAS) Features Controlling Days to Flowering (DTF) Correspond in Soybean and Common Bean?
		3.3 Do the Molecular Mechanisms Controlling Days to Flowering (DTF) Correspond in Soybean and Common Bean?
		3.4 What Do Syntenic Relationships Say About the Evolution of This Locus?
		3.5 Do the Identified Genotypes Correspond with Known Phenotypes?
		3.6 Is There a Signature of Selection Evident in Chromosomal-Scale Variant Data?
		3.7 Investigating Regions, Lists, and Analyses Using the Legume ``Mines´´
		3.8 Evaluating Expression Patterns of the Candidate Genes
		3.9 Summary
	4 Notes
	References
Chapter 5: Gramene: A Resource for Comparative Analysis of Plants Genomes and Pathways
	1 Introduction
	2 Materials
		2.1 Hardware and Software Requirements for Users
		2.2 Gramene System Components
		2.3 Local Installation of Gramene
	3 Methods
		3.1 Basic Navigation of the Gramene Website
		3.2 Example Uses of Gramene
	4 Notes
	References
Chapter 6: CerealsDB: A Whistle-Stop Tour of an Open Access SNP Resource
	1 Introduction
	2 Materials
		2.1 Wheat and Wheat Relative SNPs Held in the Database
		2.2 Annotation of Axiom SNPs
		2.3 Genotyping Data Held in the Database
	3 Methods
		3.1 Accessing SNP Data
		3.2 Accessing Genotype Information
		3.3 Accessing Varietal Information
		3.4 Accessing QTL Data
		3.5 Retrieving SNPs that Align to Brachypodium, Rice, and Sorghum
		3.6 Selecting All Axiom Probes Between Two Given Locations
		3.7 Accessing Dendrograms
		3.8 Accessing Functional SNPs
		3.9 Accessing the Introgression Plotter
		3.10 Accessing Walkthrough Tutorials
	4 Notes
	References
Chapter 7: The Barley and Wheat Pan-Genomes
	1 Introduction
		1.1 The 10+ Wheat Genomes Project
		1.2 The International Barley Pan-Genome Project
	2 Methods
		2.1 Gene Prediction/Projection in Plant Pan-Genomes
			2.1.1 De Novo Gene Prediction in Reference Plant Genomes: Example Barley
			2.1.2 Projection of Genes from a Reference to the Pan-Genome
		2.2 Determination of Ortho- and Homeologous Groups
		2.3 Identification and Annotation of Transposable Elements (TEs) and Repeats in Plant Pan-Genomes
		2.4 Visualization and Data Presentation of the Barley and Wheat Pan-Genome Data
		2.5 The ENSEMBL Plants Database Platform for 10+ Wheat Genome Data
		2.6 The Barley Pan-Genome Resources at IPK Gatersleben
	References
Chapter 8: Basics of Bash
	1 Introduction
	2 Materials
	3 Methods
		3.1 Introduction to the Shell
			3.1.1 Shell
			3.1.2 Misspelling
			3.1.3 Tab Completion
			3.1.4 Cancelling a Command
			3.1.5 Flags and Program Options
			3.1.6 Your Previous Commands
		3.2 Navigation in Your Shell
			3.2.1 Your Current Working Directory
			3.2.2 Listing Files and Folders
			3.2.3 Moving Through Your Folders
		3.3 Creating and Modifying Files and Directories
			3.3.1 Making Directories
			3.3.2 Making Files
			3.3.3 Copying Files
			3.3.4 Renaming Files and Folders
			3.3.5 Deleting Files and Folders
			3.3.6 Wildcards
		3.4 Examining Files and Directories
			3.4.1 Printing the Entire  File
			3.4.2 Printing the Top and Bottom of a  File
			3.4.3 Counting Characters and Lines Within Files
			3.4.4 Redirecting and Appending Output
		3.5 Sorting and Filtering
			3.5.1 Sorting Files
			3.5.2 Searching Files
		3.6 Combining Commands
			3.6.1 Pipes
		3.7 Automating Commands
			3.7.1 Loops
			3.7.2 Combining Loops with Pipes
Chapter 9: Pipeline Automation via Snakemake
	1 Introduction
	2 Materials
		2.1 Input Files
		2.2 Hardware
		2.3 Software
	3 Methods
		3.1 Installation
		3.2 Running Pipelines with Snakemake
			3.2.1 Basic snakefile Example
			3.2.2 Add rule all to the snakefile
		3.3 Wildcards in Snakemake
			3.3.1 Add the glob_wildcards Function to the snakefile
			3.3.2 Wildcard snakefile Example
		3.4 Logs and Benchmarks
			3.4.1 Log and Benchmark Example snakefile
		3.5 Version Control in Snakemake
			3.5.1 Anaconda snakefile Example
		3.6 Snakemake in HPC Environments
			3.6.1 config.yaml File for this Example
			3.6.2 Example sbatch Flags
			3.6.3 HPC Resources snakefile Example
		3.7 Building a Snakemake Pipeline
			3.7.1 Snakemake Pipeline Example
		3.8 Additional Functionalities
	4 Notes
	References
Chapter 10: SciApps: An Automated Platform for Processing and Distribution of Plant Genomics Data
	1 Introduction
	2 Materials
		2.1 Hardware and Software Requirements for Users
		2.2 SciApps System Components
		2.3 Local Installation of SciApps
	3 Methods
		3.1 Basic Navigation of the SciApps Website
		3.2 Launching Analysis Jobs
		3.3 Constructing a Workflow
		3.4 The BSAseq Workflow
		3.5 Plant ENCODE Data Coordination Center
			3.5.1 Processing the MaizeCODE Data
			3.5.2 Accessing the MaizeCODE Data as Reproducible Workflows
			3.5.3 Accessing the MaizeCODE Data as Genome Browser Tracks
			3.5.4 Accessing the Raw MaizeCODE Data from CyVerse Data Store
			3.5.5 Summary
	4 Notes
	References
Chapter 11: Trimming and Validation of Illumina Short Reads Using Trimmomatic, Trinity Assembly, and Assessment of RNA-Seq Data
	1 Introduction
	2 Materials
		2.1 Hardware Requirements
		2.2 Software Tools
		2.3 Setting Up Your Computer Using Conda
		2.4 Input Data
	3 Methods
		3.1 Quality Control Using FastQC
		3.2 Trimming and Filtering Using Trimmomatic
		3.3 Assessing the Impact of Trimming on Downstream Analysis
		3.4 De Novo Assembly Using Trinity
		3.5 Assembly Evaluation Using Quast
		3.6 Assessment of Assembly Completeness/Correctness Using BUSCO
	4 Notes
	References
Chapter 12: De Novo Assembly of Linked Reads Using Supernova 2.0
	1 Introduction
		1.1 Linked Reads
		1.2 The Supernova Algorithm
	2 Materials
		2.1 Datasets
		2.2 Software
	3 Methods
		3.1 Installing and Running Supernova
		3.2 Interpreting Supernova´s Output
	4 Notes
	References
Chapter 13: Applications of Optical Mapping for Plant Genome Assembly and Structural Variation Detection
	1 Introduction
	2 Materials
		2.1 Types of Datasets
		2.2 Bioinformatics Tools and Scripts
	3 Methods
		3.1 Draft Genome In Silico Digestion
		3.2 Molecule Quality Report
		3.3 De Novo Optical-Map Assembly
		3.4 Scaffolding
		3.5 Structural Variation Detection
		3.6 Visualization
	4 Notes
	References
Chapter 14: Making a Pangenome Using the Iterative Mapping Approach
	1 Introduction
	2 Materials
		2.1 Sequencing Reads
		2.2 Hardware
		2.3 Software
	3 Methods
		3.1 Quality Control
			3.1.1 Quality Control of Raw Reads Using FastQC
		3.2 Alignment of Reads to Reference Using Bowtie2 and Samtools
		3.3 Iterative Assembly Using MaSuRCA
			3.3.1 MaSuRCA
			3.3.2 Iterative Assembly
		3.4 Contamination Examination Using Blastn and Samtools
	4 Notes
	References
Chapter 15: Construction of Practical Haplotype Graph (PHG) with the Whole-Genome Sequence Data
	1 Introduction
	2 Materials
	3 Methods
		3.1 Identify the Conserved Reference Ranges
			3.1.1 Make Blast Database
			3.1.2 Merge Blast Database
			3.1.3 Compare Target Species Genes with Merged Blast Database
			3.1.4 Create a List of Unique Conserved Gene Ids
			3.1.5 Create a List of Genes with Chr, Start, End, Geneid from Sorghum Genes gff3 File
			3.1.6 Extract Conserved Gene (from Point 4) Coordinates from the List of the Overall Genes (Point 5) (See Note 2)
			3.1.7 Extend the Conserved Regions with 1 kb on Either Side and Merge the Neighbor Regions which Are 500 bp Apart
		3.2 Haplotype Calling from Diverse Individuals of Species
			3.2.1 Trimming Reads
			3.2.2 Mapping Reads
			3.2.3 Mark Duplicates
			3.2.4 Group Mapped Read in Bam
			3.2.5 Haplotype Calling
			3.2.6 Filter Variants
			3.2.7 Recalibration
			3.2.8 Calling Haplotypes
			3.2.9 Merge Gvcf Files
			3.2.10 Call Genotype from Gvcf
		3.3 Build a PHG Graph
			3.3.1 Preparing a Docker Environment
			3.3.2 Loading Reference Ranges
			3.3.3 Populate the PHG Database with Haplotypes from Diverse Taxa
			3.3.4 Creating Consensus Haplotypes
			3.3.5 Pan-Genome Fasta File Extraction from PHG
			3.3.6 Impute Variants from PHG
	4 Notes
	References
Chapter 16: Visualization Tools for Genomic Conservation
	1 Introduction
		1.1 Background on Genomic Conservation
		1.2 Synteny Analysis Pipeline
	2 Materials
		2.1 Hardware
		2.2 Required Files and Formats
	3 Methods
		3.1 SynVisio
			3.1.1 Dashboard
			3.1.2 Genome Level View
			3.1.3 Chromosome View
			3.1.4 Gene-Block View
			3.1.5 Multi-genome Views
				Stacked Parallel Plot
				Hive Plot
			3.1.6 Additional Features
				Track Annotations
				Gene Search Panel
				Support for Unplaced Scaffolds
				Image Export
				Revisitation Support
			3.1.7 SynVisio Example Usage
		3.2 Accusyn
			3.2.1 Decluttering
			3.2.2 Additional Features
				Annotation Tracks
				Revisitation Snapshots
	4 Notes
	References
Chapter 17: Annotation of Protein-Coding Genes in Plant Genomes
	1 Introduction
	2 Materials
		2.1 Input Data
			2.1.1 Genome Assembly
			2.1.2 External Evidence
		2.2 Software
	3 Methods
		3.1 Computation Phase
			3.1.1 Repeat Identification
			3.1.2 Repeat Masking
			3.1.3 Alignment of Evidences
			3.1.4 Gene Prediction
		3.2 Annotation Phase
		3.3 Quality Control
	4 Notes
	References
Chapter 18: Finding and Characterizing Repeats in Plant Genomes
	1 Introduction
	2 Detecting Transposable Elements in Plant Genomes
		2.1 Large-Scale Search for Transposable Elements
			2.1.1 All TEs; Ab Initio Methods
			2.1.2 All TEs; Homology-Based Methods
			2.1.3 All TEs; Structured Methods
			2.1.4 All TEs; Pipeline Methods
		2.2 LTR Retrotransposons
			2.2.1 Homology-Based Methods
			2.2.2 Structure-Based Methods
		2.3 Non-LTR Retrotransposons
		2.4 DNA Transposons
			2.4.1 Structure-Based Methods
		2.5 Helitrons
	3 Analysis of Transposable Elements Directly from Sequencing Reads
		3.1 TE Analysis Using Raw Sequencing Data Without Reference Sequence
			3.1.1 Unassembled Raw Reads Can Already Tell a Lot
		3.2 Detection of TE Variants Using Short-Reads (with Reference Sequence)
			3.2.1 Detection of Non-reference TE Insertions
			3.2.2 Presence/Absence of Reference TE Insertions
		3.3 Contribution of Long-Read Sequencing Technologies for TE Detection
	4 Efficient Search for Repeats in Genomic Sequences
		4.1 The Art of Indexing
			4.1.1 Suffix Trees
			4.1.2 Suffix Arrays
			4.1.3 FM-Index and Burrows-Wheeler Transform
			4.1.4 Hash Tables Versus Burrows-Wheeler Transform
		4.2 Finding Approximated Words, a Matter of Seeds
		4.3 Using Reads Instead of Contigs: From Seeds to Minimizers
	5 Modeling Repeated Structures: Black Box and Explicit Approaches
		5.1 The Need for Models of the Repeated Structures
		5.2 Machine Learning for TE Detection
			5.2.1 How Do ML-Based Methods Work on TEs?
		5.3 Building Models from Scratch: A Gentle Introduction to the Theory of Languages
		5.4 Linguistic Analysis of Genomic Sequences
			5.4.1 Dedicated Pattern Matching
			5.4.2 General Purpose Pattern Matching
	References
Chapter 19: Gene Co-expression Network Analysis
	1 Introduction
	2 Materials
	3 Methods
		3.1 Experimental Design
		3.2 Data Acquisition and Preprocessing
		Box 1 Recommended Commands and Parameters for Alignment of RNA-Seq Reads and Obtaining Raw Gene Counts
		3.3 Data Normalization
		Box 2 R Code to Load the Data to Obtain Table 2 and Filter and Normalize It Using the TPM Approach and Produce the Values Show...
		3.4 Distance Calculation
		Box 3 R Code to Generate an n x n Similarity Matrix (Table 4) Using the Pearson Correlation Coefficient Starting from Table 3
		3.5 Adjacency Function and Adjacency Matrix
		3.6 Use of Free Scale Topology to Determine Adjacency Function Parameters
		Box 4 R Code to Calculate the R2 Fit to the Power Function
		Box 5 R Code to Compute the Unsigned Weighted Adjacency Matrix
		3.7 Identification of Gene Modules Using the Topological Overlap Measure (TOM)
		Box 6 R Code to Compute TOM Scores for the Weighted and Unweighted Adjacency Matrices Obtained in the Previous Step and then G...
		3.8 Eigengene and Merging Modules
		Box 7 R Code to Calculate Eigengene Modules and Merge Close Modules into a Single Cluster. Then Plot the Dendrogram with the U...
		3.9 Final Remarks
	4 Notes
	References
Chapter 20: Skim-Based Genotyping by Sequencing Using a Double Haploid Population to Call SNPs, Infer Gene Conversions, and Im...
	1 Introduction
	2 Materials
		2.1 Sequencing Reads
		2.2 Reference Genome
		2.3 Software
	3 Methods
		3.1 Alignment of Reads to Reference Using SOAPaligner
		3.2 Sorting and Merging Using Picard and Samtools
		3.3 Removal of Clonal Reads and PCR Clones Using Picard
		3.4 Calling SNPs Using SGSautoSNP
		3.5 Calling Genotypes Using snp_genotyping_all.Pl
		3.6 Visualizing Genotypes
		3.7 Cleaning  SNPs
		3.8 Imputing Genotypes
	4 Notes
	References
Chapter 21: Managing High-Density Genotyping Data with Gigwa
	1 Introduction
	2 Materials
		2.1 Loading Data
		2.2 Basic Filtering
		2.3 Advanced Filtering
		2.4 Discrimination Based on Groups of Individuals
		2.5 Query Bookmarking
	3 Methods
		3.1 Visualizing Marker List and Details
		3.2 Visualizing Marker Distribution
		3.3 Visualizing Gene Information
		3.4 Exporting Data
		3.5 Working with REST APIs
	4 Implementation and Availability
	References
Chapter 22: Machine Learning for Image Analysis: Leaf Disease Segmentation
	1 Introduction
		1.1 What Is Deep Learning?
	2 Materials
		2.1 Dataset Utilized in the Methodology
		2.2 Computational Requirements
		2.3 Installation Requirements
			2.3.1 Install Anaconda
			2.3.2 Install Required Libraries
	3 Methods
		3.1 Proposed Methodology Pathways
		3.2 Launch Jupyter Notebook
		3.3 Loading Libraries
		3.4 Define Variables Used for Data Processing
		3.5 Functions for Dataset Processing
		3.6 Define Variables Used During Training
		3.7 Inspect the Dataset
		3.8 Create the Dataset and Apply Transformations
		3.9 Visualize the Data
		3.10 Define the Model
		3.11 Compile the Model
		3.12 Define Runtime Functions
		3.13 Training the Model
		3.14 Evaluate Training
	4 Notes
	References
Chapter 23: Analysis of Bisulfite Sequencing Data Using Bismark and DMRcaller to Identify Differentially Methylated Regions
	1 Introduction
	2 Materials
	3 Methods
		3.1 Read Filtering
		3.2 Genome Preparation
		3.3 Read Alignment
		3.4 Deduplication
		3.5 Methylation Rate Calculation
		3.6 Identification of DMRs
	4 Notes
	References
Chapter 24: Long Intergenic Noncoding RNA (lincRNA) Discovery from Non-Strand-Specific RNA-Seq Data
	1 Introduction
	2 Materials
		2.1 Reference Sequence, Annotation, and RNA-Seq Sequencing  Data
		2.2 Bioinformatic Tools and Scripts
	3 Methods
		3.1 Obtaining RNA-Seq Sequencing Reads
		3.2 Preparation of the Reference Sequence for Mapping
		3.3 Building Transcriptome Assemblies Using StringTie
		3.4 Comparing Annotations Using Gffcompare
		3.5 Identification of Noncoding Genes Using CPC2 and DIAMOND
		3.6 Comparison of the Noncoding RNAs Against Rfam Database
		3.7 Application of the Minimum Length Filter to the Noncoding  Loci
		3.8 Compare Locations of Coding and Noncoding Loci to Identify lincRNAs
		3.9 Estimate Expression Abundance of lincRNAs
		3.10 Calculation of Correlation Between Coding and Noncoding Gene Expression
		3.11 Comparing Location of lincRNAs Against SNPs Associated with Resistance
	4 Notes
	References
Chapter 25: Linkage Disequilibrium Statistics and Block Visualization
	1 Introduction
	2 Materials
		2.1 Input Data
		2.2 Essential Software for This Chapter
		2.3 Additional LD Analysis Software
	3 Methods
		3.1 PLINK Format
		3.2 Data Filtering
		3.3 Raw Linkage Disequilibrium Statistics
		3.4 Generating Linkage Blocks Using PLINK
		3.5 Visualizing LD Blocks from PLINK
		3.6 Visualizing LD by D′ with LDBlockShow
		3.7 Visualizing LD by R2 and Interpreting Differences with D′
	4 Notes
	References
Chapter 26: Analysis of Small RNA Sequencing Data in Plants
	1 Introduction
	2 Materials
		2.1 Workstation
		2.2 Small RNA Sequencing Data
		2.3 Reference Genome
		2.4 Software and Tools
	3 Methods
		3.1 Data Preprocessing
		3.2 Identification of Known miRNAs
		3.3 Identification of Novel miRNAs
		3.4 Prediction of miRNA Targets
		3.5 Differential Expression of miRNAs
	4 Notes
	References
Chapter 27: Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases
	1 Introduction
	2 Materials
		2.1 Plant Reactome
		2.2 PubChem
	3 Methods
		3.1 Plant Reactome Data Model and Collaborations
			3.1.1 New Content and Features: Developmental Pathways and Interactome Data Overlays
			3.1.2 Data Integration
		3.2 PubChem Data Collection
		3.3 Biocuration of Complex Biological Processes
	4 Notes
	References
Chapter 28: AgroLD: A Knowledge Graph Database for Plant Functional Genomics
	1 Introduction
	2 Materials
		2.1 Data Sources
		2.2 Data Integration and Annotation
			2.2.1 Toward Automation of RDF Transformations
			2.2.2 Semantic Annotation of Data with Bio-ontologies
			2.2.3 Methods for Linking Entities from Distinct Graphs
	3 Methods
	4 Notes
	References
Index