Bioinformatics for Cancer Immunotherapy

Chapter 1 Bioinformatics for Cancer Immunotherapy

Chapter 2 An Individualized Approach for Somatic Variant Discovery

Chapter 3 Ensemble-Based Somatic Mutation Calling in Cancer Genomes

Chapter 4 SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations

Chapter 5 HLA Typing from RNA Sequencing and Applications to Cancer

Chapter 6 Rapid High-Resolution Typing of Class I HLA Genes by Nanopore Sequencing

Chapter 7 HLApers: HLA Typing and Quantification of Expression with Personalized Index

Chapter 8 High-Throughput MHC I Ligand Prediction Using MHCflurry

Chapter 9 In Silico Prediction of Tumor Neoantigens with TIminer

Chapter 10 OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction

Chapter 11 Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data

Chapter 12 The SysteMHC Atlas: a Computational Pipeline, a Website, and a Data Repository for Immunopeptidomic Analyses

Chapter 13 Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires

Chapter 14 Modeling and Viewing T Cell Receptors Using TCRmodel and TCR3d

Chapter 16 Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA-Sequencing Data

Chapter 17 EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data

Chapter 18 Computational Deconvolution of Tumor-Infiltrating Immune Components with Bulk Tumor Gene Expression Data

Chapter 19 Cell-Type Enrichment Analysis of Bulk Transcriptomes Using xCell

Chapter 20 Cap Analysis of Gene Expression (CAGE): A Quantitative and Genome-Wide Assay of Transcription Start Sites