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This volume focuses on a variety of in silico protocols of the latest bioinformatics tools and computational pipelines developed for neo-antigen identification and immune cell analysis from high-throughput sequencing data for cancer immunotherapy. The chapters in this book cover topics that discuss the two emerging concepts in recognition of tumor cells using endogenous T cells: cancer vaccines against neo-antigens presented on HLA class I and II alleles, and checkpoint inhibitors. 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.
Cutting-edge and authoritative, Bioinformatics for Cancer Immunotherapy: Methods and Protocols is a valuable research tool for any scientist and researcher interested in learning more about this exciting and developing field.
List of contents
Bioinformatics for Cancer Immunotherapy.- An Individualized Approach for Somatic Variant Discovery.- Ensemble-Based Somatic Mutation Calling in Cancer Genomes.- SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.- HLA Typing from RNA Sequencing and Applications to Cancer.- Rapid High-Resolution Typing of Class I HLA Genes by Nanopore Sequencing.- HLApers: HLA Typing and Quantification of Expression with Personalized Index.- High-Throughput MHC I Ligand Prediction using MHCflurry.- In Silico Prediction of Tumor Neoantigens with TIminer.- OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction.- Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data.- The SysteMHC Atlas: A Computational Pipeline, A Website, and A Data Repository for Immunopeptidomics Analysis.- Identification of Epitope-Specific T Cells in T Cell Receptor Repertoires.- Modeling and Viewing T Cell Receptors using TCRmodel and TCR3d.- In Silico Cell Type Deconvolution Methods in Cancer Immunotherapy.- Immunedeconv - An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA Sequencing Data.- EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.- Computational Deconvolution of Tumor-Infiltrating Immune Components with Bulk Tumor Gene Expression Data.- Cell Type Enrichment Analysis of Bulk Transcriptomes using xCell.- Cap Analysis of Gene Expression (CAGE), A Quantitative and Genome-Wide Assay of Transcription Start Sites.
