HLA Typing: Methods and Protocols (Methods in Molecular Biology, 2809)

✍ Scribed by Sebastian Boegel (editor)

• Publisher: Humana; Second Edition 2024
• Year: 2024
• Tongue: English
• Leaves: 300
• Edition: 2
• Category: Library

✦ Synopsis

This edition provides a collection of state-of-art methods and tools for human leukocyte antigen (HLA) and major histocompatibility complex (MHC) research. The book explores updated as well as novel in silico tools, resources, and wet lab protocols for HLA typing, including determination of the HLA class I and class II type of an individual in clinical work and research, such as in transplantation medicine and vaccine development in the context of infectious diseases or cancer immunotherapies. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed information and implementation advice that leads to best results.
Up-to-date and practical, HLA Typing: Methods and Protocols, Second Edition serves as a valuable resource for any researcher interested in learning more about this vital field.

✦ Table of Contents

Preface
Contents
Contributors
Chapter 1: HLA Genes: A Hallmark of Functional Genetic Variation and Complex Evolution
1 The Major Histocompatibility Complex
2 The Nature of MHC Variability
3 The Evolution of MHC Variability
3.1 Haplotypic Variation-Evolution of HLA Gene Copies
3.2 Allelic Variation-Pathogen-Mediated Balancing Selection
4 Promises of a Better Functional Understanding of MHC Variability
References
Chapter 2: Allele Frequency Net Database
1 Introduction
2 Materials
2.1 Hardware and System Requirements
2.2 Software Dependencies
2.3 Data Sets
3 Methods
3.1 Website Organization
3.2 Gold, Silver, and Bronze (GSB) Standard HLA Data Sets
3.3 HLA Allele Frequency Search
3.4 HLA Haplotype Frequency Search
3.5 Rare HLA Alleles
3.6 HLA Survey Reports
3.7 HLA-EpiDB
3.8 HLA-ADR Database
4 Notes
References
Chapter 3: AmpliSAS and AmpliHLA: Web Server and Local Tools for MHC Typing of Non-model Species and Human Using NGS Data
1 Introduction
2 Materials
2.1 AmpliSAS
2.2 AmpliHLA
3 Methods
3.1 Local Installation of AmpliSAS and AmpliHLA Perl Scripts and Dependencies
3.2 Installation in a Conda Environment
3.3 Running AmpliSAS and AmpliHLA as a Docker Container
3.4 Web Server AmpliSAS and AmpliHLA
3.5 MHC Class I Genotyping in a Passerine Bird
3.6 Customizing the MHC Class I Genotyping
3.7 Interpreting the Genotyping Results
3.8 Comparing Two Genotyping Result Files
3.9 HLA Typing with Amplicon Sequencing Data
3.10 Interpreting the HLA Typing Results
4 Notes
References
Chapter 4: Comprehensive HLA Typing from a Current Allele Database Using Next-Generation Sequencing Data
1 Introduction
2 Materials
2.1 Hardware
2.2 Software Required
2.3 Installing HLA-HD
2.4 Updating the HLA Dictionary
2.5 Input Data Set
3 Methods
3.1 Running
3.2 Interpretation of the Typing Results
4 Notes
References
Chapter 5: Deep Learning-Based HLA Allele Imputation Applicable to GWAS
1 Introduction
2 Materials
2.1 Hardware
2.2 Software Dependencies
2.3 HLA and SNV Datasets
3 Methods
3.1 Installation
3.2 Creation of Configuration Files for Model Architecture and HLA Genes
3.3 Preparation of Haplotype Phased Target GWAS SNV Data in the MHC Region
3.4 Model Training
3.5 Imputation
3.6 Cross-Validation (Optional)
3.7 Imputation of Amino Acid Alleles (Optional)
3.8 Benchmarking
4 Notes
References
Chapter 6: Benchmarking NGS-Based HLA Typing Algorithms
1 Introduction
2 Materials
2.1 Hardware Requirements
2.2 Software Requirements
2.3 Setting Up the Pipeline
2.4 Overview of Containers
3 Methods
3.1 Selecting WES Samples for the Reference Dataset
3.2 Running the Pipeline
3.3 Locating the Results
3.4 Overview of the Rules in the Pipeline
3.4.1 Preprocessing
3.4.2 Kourami
3.4.3 HLALA
3.4.4 OptiType
3.4.5 HISAT-Genotype
3.5 Modifying the Pipeline
4 Notes
References
Chapter 7: HLA Typing and Mutation Calling from Normal and Tumor Whole Genome Sequencing Data with ALPHLARD-NT
1 Introduction
2 Materials
2.1 Input Data
2.2 Installation of ALPHLARD-NT
2.3 Installation of Preprocessors
3 Methods
3.1 Preparation of Input BAM Files
3.2 Execution of ALPHLARD-NT
3.3 Interpretation of Results
4 Notes
References
Chapter 8: NanoHLA: A Method for Human Leukocyte Antigen Class I Genes Typing Without Error Correction Based on Nanopore Seque...
1 Introduction
2 Materials
2.1 Principles of NanoHLA
2.2 Hardware
2.3 Software
2.4 Download and Install NanoHLA
3 Methods
3.1 Dry Running NanoHLA
3.2 Inputs and Parameters
3.3 Outputs and Description
3.4 Running NanoHLA and Performance Validation
3.4.1 Get Sequencing Data
3.4.2 NGS-Based Pipeline for HLA Typing and Parameters
3.4.3 Running NanoHLA and HLA-LA
3.4.4 Results and Performance
4 Notes
References
Chapter 9: Imputation-Based HLA Typing with GWAS SNPs
1 Introduction
2 Materials
2.1 Hardware
2.2 Software Dependencies
2.3 HLA and SNP Datasets
3 Methods
3.1 Imputation with Pre-built Models
3.2 Model Training
3.2.1 Execution Time for Model Building
3.2.2 Prepare SNP Data for a Specific Genotyping Platform
3.2.3 Build HIBAG Models with Default Settings
3.2.4 GPU Computing
3.2.5 Compute Clusters
4 Discussion
References
Chapter 10: Full-Length Characterization of Novel HLA-DRB1 Alleles for Reference Database Submission
1 Introduction
2 Materials
2.1 PCR Primers
2.2 PCR
2.3 Purification
2.4 Library Preparation and Sequencing: Illumina Shotgun
2.5 Library Preparation and Sequencing: PacBio
2.6 Library Preparation and Sequencing: ONT
2.7 Genotyping and Sequence Submission to IPD-IMGT/HLA
3 Methods
3.1 PCR
3.2 Illumina Library Preparation and Sequencing
3.3 PacBio Library Preparation and Sequencing
3.4 ONT Library Preparation and Sequencing
3.5 Data Analysis: DR2S
3.6 Submission to IPD-IMGT/HLA: TypeLoader2
4 Notes
References
Chapter 11: Submitting Novel Full-Length HLA, MIC, and KIR Alleles with TypeLoader2
1 Introduction
2 Materials
2.1 Hardware Requirements
2.2 Software
2.3 Reference Files
2.4 Sequence Files
2.5 Accounts for ENA and IPD
3 Methods
3.1 Setup of TypeLoader2
3.2 Initial Run
3.2.1 Create a Test User Account
3.2.2 Login and Reference Update
3.2.3 Connect to ENA and IPD
Register with ENA
Register with IPD
Configure the TypeLoader2 Settings
3.2.4 Download Example Files
3.2.5 Create a Project
3.2.6 Add an Allele
3.2.7 Submit to EMBL-ENA
3.3 Productive Use
3.3.1 Create IPD Files
3.3.2 Submitting to IPD
3.3.3 Bulk Upload of Alleles to TypeLoader2
4 Notes
References
Chapter 12: PIRCHE-II Risk and Acceptable Mismatch Profile Analysis in Solid Organ Transplantation
1 Introduction
1.1 Transplantation and HLA Matching
1.2 Recognition of Mismatched HLA
1.3 PIRCHE-II Method
1.4 PIRCHE-II and Associations with Clinical Outcome
2 Materials
2.1 PIRCHE Web Portal
2.2 HLA Typing Data
2.3 Access, Analysis, and Extraction Scripts
2.4 Risk and Acceptable Mismatch Profile (RAMP)
3 Methods
3.1 Prepare Genotype Data
3.1.1 Extract Patient or Donor HLA Typing Data Manually
3.1.2 Extract Patient or Donor HLA Typing Data as a GL-String
3.1.3 Extract One or More Donors´ HLA Typing Data and Prepare a CSV Input
3.1.4 HML
3.2 PIRCHE-II in Living Donor Scenarios
3.3 Risk and Acceptable Mismatch Profile (RAMP)
3.4 PIRCHE-II in Research and Donor Cohorts
4 Notes
References
Chapter 13: Graph-Based Imputation Methods and Their Applications to Single Donors and Families
1 Introduction
1.1 Family Imputation: GRAMM
1.2 Haplotype Frequency Estimation Using GRIMM-Based EM: MR-GRIMME
2 Related Work
2.1 Imputation
2.2 Family Imputation
2.3 Haplotype Frequency Estimates
3 Results
3.1 Prediction of Unphased Multilocus Unambiguous Genotypes (UMUG)
3.2 Prediction of Phase in Families
3.3 Estimate of Recombination Rate
3.4 GRIMM Can Be Extended to Estimate Haplotype Frequencies
3.5 Multi-region EM Has Slightly Higher Log-Likelihood Than Single-Region EM
4 Discussion
References
Chapter 14: How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.2.1 MoDec
2.3 Getting MixMHC2pred
2.3.1 Web Application
2.3.2 Executable
2.4 MHC-II Sequences
3 Methods
3.1 Predicting MHC-II Binding Specificities from MHC-II Amino Acid Sequences
3.2 Predictions of Ligands and Epitopes
3.2.1 Web Application
3.2.2 Executable
3.2.3 Analysis of the Results
4 Notes
5 Discussion
References
Chapter 15: DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.3 Webserver
2.4 Download and Installation
3 Methods
3.1 Running DeepHLApan
3.2 Interpreting the Output
4 Notes
References
Chapter 16: In Silico: Predicting Intrinsic Features of HLA Class-I Restricted Neoantigens
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.3 Download
3 Methods
3.1 Visualizing Neoantigens in GNIFdb
3.1.1 The Usage Guide for Visualization
3.1.2 Example Application of Visualization in GNIFdb
3.2 Searching GNIFdb
3.2.1 The Usage Guide for Search
3.2.2 Search Output
3.3 Tools for Calculating Intrinsic Features
3.3.1 Running ProtFP
3.3.2 Running Blosum Indice
3.3.3 Running Cruciani Properties
3.3.4 Running FASGAI
3.3.5 Running MSWHIM
3.3.6 Running Kidera Factor
3.3.7 Running ST-Scales
3.3.8 Running T-Scales
3.3.9 Running Z-Scales
3.3.10 Running VHSE-Scales
3.3.11 Running AA Distribution
3.3.12 Running Physical Chemical Properties
4 neoDL: Neoantigen Intrinsic Feature-Based Deep Learning Model
4.1 Training neoDL
4.2 Running neoDL
4.3 Example Application of neoDL in GNIFdb
5 NES: Normalized Enrichment Score for Gene Sets
References
Chapter 17: Designing High Binding Affinity Peptides for MHC Class I Using MAM: An In Silico Approach
1 Introduction
2 Methods
2.1 Overview of MAM
2.2 Embedding
2.3 MAM-CNN Predictor
2.4 Generation
2.5 Transfer Learning
3 User Guidance
3.1 Input to MAM
3.2 MAM Output
3.3 MAM Installation (Timing: ~5-10 Min)
3.4 MAM Configuration
3.5 Embed Peptides with Word2Vec (Timing: ~2 Min)
3.6 Predict the MHC-I-Peptide Binding Affinity via MAM-CNN (Timing: ~3 min)
3.7 Generate New Peptides with Potential High Affinity (Timing: ~1 min)
3.8 Apply MAM for Mamu-A1001:01 via Fine-Tuning (Timing: ~4 min)
4 Discussion
References
Chapter 18: MHCtools 1.5: Analysis of MHC Sequencing Data in R
1 Introduction
1.1 MHC Genotyping
1.2 MHC Diversity
1.3 MHC Supertypes
1.4 MHC Haplotypes
2 Materials
2.1 Hardware
2.2 Software
2.3 Data Formats
2.3.1 MHC Genotyping
2.3.2 MHC Diversity
2.3.3 MHC Supertypes
2.3.4 MHC Haplotypes
3 Methods
3.1 MHC Genotyping
3.2 MHC Diversity
3.3 MHC Supertypes
3.4 MHC Haplotypes
4 Notes
4.1 MHC Genotyping
4.2 MHC Diversity
4.3 MHC Supertypes
4.4 MHC Haplotypes
References
Index