HLA Typing Methods and Protocols

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. 

HLA Genes: A Hallmark of Functional Genetic Variation and Complex Evolution.- Allele Frequency Net Database.- AmpliSAS and AmpliHLA: Web Server and Local Tools for MHC Typing of Non-Model Species and Human Using NGS Data.- Comprehensive HLA Typing from a Current Allele Database Using Next-Generation Sequencing Data.- Deep Learning-Based HLA Allele Imputation Applicable to GWAS.- Benchmarking NGS-Based HLA Typing Algorithms.- HLA Typing and Mutation Calling from Normal and Tumor Whole Genome Sequencing Data with ALPHLARD-NT.- NanoHLA: A Method for Human Leukocyte Antigen Class I Genes Typing Without Error Correction Based on Nanopore Sequencing Data.- Imputation-Based HLA Typing with GWAS SNPs.- Full-Length Characterization of Novel HLA-DRB1 Alleles for Reference Database Submission.- Submitting Novel Full-Length HLA, MIC, and KIR Alleles with TypeLoader2.- PIRCHE II Risk and Acceptable Mismatch Profile Analysis in Solid Organ Transplantation.- Graph-Based Imputation Methods and Their Applications to Single Donors and Families.- How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred.- DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity.- In Silico Predicting Intrinsic Features of HLA Class-I Restricted Neoantigens.- Designing High Binding Affinity Peptides for MHC Class I Using MAM: An In Silico Approach.- MHCtools 1.5: Analysis of MHC Sequencing Data in R.