Titlebook: Bioinformatics for Cancer Immunotherapy; Methods and Protocol Sebastian Boegel Book 2020 Springer Science+Business Media, LLC, part of Spri

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SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations,sue and its matched normal (commonly blood or adjacent normal tissue) for side-by-side comparison. However, when interrogating entire genomes (or even just the coding regions), the number of sequencing errors easily outnumbers the number of real somatic mutations by orders of magnitudes. Here, we de

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HLA Typing from RNA Sequencing and Applications to Cancer,t of cancer and treatment therapies, the HLA locus plays a critical role in tumor recognition and tolerance mechanisms. In silico HLA class I and class II typing, as well as expression quantification from next-generation RNA sequencing, can therefore have great potential clinical applications. Howev

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High-Throughput MHC I Ligand Prediction Using MHCflurry,r integration into high-throughput bioinformatic pipelines. Users can download models fit to publicly available data or train predictors on their own affinity measurements or mass spec datasets. This chapter gives a tutorial on essential MHCflurry functionality, including generating predictions, tra

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Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires,epitope-specific T cells has been instrumental in our understanding of cancer immunology and the development of personalized immunotherapies. To facilitate the study of T-cell epitope specificity, we developed a prediction tool, TCRex, that can identify epitope-specific T-cell receptors (TCRs) direc

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Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fesults of several methods. Given numerous dependencies and differences in input and output format of the various computational methods, comparative analyses can become quite complex. This motivated us to develop ., an R package providing uniform and user-friendly access to seven state-of-the-art com