Frontiers in ImmunologyBioinformaticsPeer-reviewed journal article
IOBR: Multi-omics Immuno-Oncology Biological Research to decode tumor microenvironment and signatures
Dongqiang ZengiD, Zilan YeiD, Rongfang SheniD, Guangchuang YuiD, Jiani WuiD, Yi XiongiD, Rui ZhouiD, Wenjun Qiu, Na HuangiD, Li SuniD, Xuejun LiiD, Jianping BiniD, Yulin LiaoiD, Min ShiiD, Wangjun LiaoiD
Abstract
MotivationRecent advance in next generation sequencing has triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to exploring robust signatures for clinical translation is increasingly highlighted, attributed to the clinical success of immune checkpoint blockade in diverse malignancies. However, effective tools to comprehensively interpret multi-omics data is still warranted to provide increased granularity into intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. ResultsWe developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing comprehensive investigation of estimation of reported or user-built signatures, TME deconvolution and signature construction base on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, lncRNA profiling, genomic characteristics and signatures generated from single-cell RNA sequencing data in different cancer settings. Additionally, IOBR also integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool, to leverage multi-omics data to facilitate immuno-oncology exploration and unveiling of tumor-immune interactions and accelerating precision immunotherapy.
- assay: bulk RNA-seq
- assay: multiome
- research focus: benchmarking
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1,264
- FWCI
- 71.21
- Field percentile
- top 1%
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