Bioinformatics for Cancer Immunotherapy

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.