Abstract
SummaryRecently, a number of powerful computational tools for dissecting tumor-immune cell interactions from next-generation sequencing data have been developed. However, the assembly of analytical pipelines and execution of multi-step workflows are laborious and involve a large number of intermediate steps with many dependencies and parameter settings. Here we present TIminer, an easy-to-use computational pipeline for mining tumor-immune cell interactions from next-generation sequencing data. TIminer enables integrative immunogenomic analyses, including: human leukocyte antigens typing, neoantigen prediction, characterization of immune infiltrates and quantification of tumor immunogenicity.Availability and implementationTIminer is freely available at http://icbi.i-med.ac.at/software/timiner/timiner.shtml.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Recent breakthroughs in cancer immunotherapy and decreasing costs of next-generation sequencing (NGS) technologies sparked intensive research into tumor-immune cell interactions using genomic tools
Several tools and analytical pipelines have been developed and used to effectively mine tumor immunologic and genomic data and extract information relevant for cancer immunology and immunotherapy that includes: human leukocyte antigens (HLAs) typing, prediction of neoantigens, tumorinfiltrating immune cells estimated from RNA-sequencing (RNA-seq) data, and expression levels of key molecules like immune checkpoints (e.g. cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) and programmed cell death protein 1 (PD1) and major histocompatibility complex molecules (see our recent review (Hackl et al, 2016))
In order to simplify the access to the individual tools, we provide a unified Python application programming interface (API)
Summary
Recent breakthroughs in cancer immunotherapy and decreasing costs of next-generation sequencing (NGS) technologies sparked intensive research into tumor-immune cell interactions using genomic tools. Several tools and analytical pipelines have been developed and used to effectively mine tumor immunologic and genomic data and extract information relevant for cancer immunology and immunotherapy that includes: human leukocyte antigens (HLAs) typing, prediction of neoantigens (non-self tumor antigens predicted from somatic mutations binding specific HLA types), tumorinfiltrating immune cells estimated from RNA-sequencing (RNA-seq) data, and expression levels of key molecules like immune checkpoints (e.g. cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) and programmed cell death protein 1 (PD1) and major histocompatibility complex molecules (see our recent review (Hackl et al, 2016)). Several tools have been recently published but provide only limited functionality like the identification of tumor neoantigens (Supplementary Table S1). TIminer integrates stateof-the-art bioinformatics tools to analyze single-sample RNA-seq data and somatic DNA mutations to characterize the tumor-immune interface including: (i) genotyping of HLAs from NGS data, (ii) prediction of tumor neoantigens using mutation data and HLA types, (iii) characterization of tumor-infiltrating immune cells from bulk RNA-seq data; and (iv) quantification of tumor immunogenicity from expression data
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