Main description:

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.

Contents:

Preface...
Table of Contents...
Contributing Authors...

1 Bioinformatics for Cancer Immunotherapy
Christoph Holtstrater, Barbara Schroers, Thomas Bukur, and Martin Loewer

2 An Individualized Approach for Somatic Variant Discovery
Minghao Li, Ting He, Chen Cao, and Quan Long

3 Ensemble-Based Somatic Mutation Calling in Cancer Genomes
Weitai Huang, Yu Amanda Guo, Mei Mei Chang, and Anders Jacobsen Skanderup

4 SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations
Li Tai Fang

5 HLA Typing from RNA Sequencing and Applications to Cancer
Rose Orenbuch, Ioan Filip, and Raul Rabadan

6 Rapid High-Resolution Typing of Class I HLA Genes by Nanopore Sequencing
Chang Liu and Rick Berry

7 HLApers: HLA Typing and Quantification of Expression with Personalized Index
Vitor R. C. Aguiar, Cibele Masotti, Anamaria A. Camargo, and Diogo Meyer

8 High-Throughput MHC I Ligand Prediction using MHCflurry
Timothy O'Donnell and Alex Rubinsteyn

9 In Silico Prediction of Tumor Neoantigens with TIminer
Alexander Kirchmair and Francesca Finotello

10 OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction
Julia Kodysh and Alex Rubinsteyn

11 Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data
Prathyusha Konda, J. Patrick Murphy, and Shashi Gujar

12 The SysteMHC Atlas: A Computational Pipeline, A Website, and A Data Repository for Immunopeptidomics Analysis
Wenguang Shao, Etienne Caron, Patrick Pedrioli, and Ruedi Aebersold

13 Identification of Epitope-Specific T Cells in T Cell Receptor Repertoires
Sofie Gielis, Pieter Moris, Wout Bittremieux, Nicolas De Neuter, Benson Ogunjimi, Kris Laukens, and Pieter Meysman

14 Modeling and Viewing T Cell Receptors using TCRmodel and TCR3d
Ragul Gowthaman and Brian G. Pierce

15 In Silico Cell Type Deconvolution Methods in Cancer Immunotherapy
Gregor Sturm, Francesca Finotello, and Markus List

16 Immunedeconv - An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA Sequencing Data
Gregor Sturm, Francesca Finotello, and Markus List

17 EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data
Julien Racle and David Gfeller

18 Computational Deconvolution of Tumor-Infiltrating Immune Components with Bulk Tumor Gene Expression Data
Bo Li, Taiwen Li, Jun S. Liu, and X. Shirley Liu

19 Cell Type Enrichment Analysis of Bulk Transcriptomes using xCell
Dvir Aran

20 Cap Analysis of Gene Expression (CAGE), A Quantitative and Genome-Wide Assay of Transcription Start Sites
Masaki Suimye Morioka, Hideya Kawaji, Hiromi Nishiyori-Sueki, Mitsuyoshi Murata, Miki Kojima-Ishiyama, Piero Carninci, and Masayoshi Itoh