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SINBA

Background

Synergy inference by data-driven network-based Bayesian analysis(SINBA) is used to bridge the novel discoveries from network-based systems biology to reverse pharmacology. SINBA will tremendously reduce the time and resource cost of conventional combination screening. SINBA also integrated a blood-brain barrier penetrated drug-target database, which will expedite the drug discovery for CNS tumors.

Package structure

SINBA package
SINBA package

Installation

Require R >= 3.5.0.

  1. install from github
devtools::install_github("jyyulab/SINBA",auth_token = "your auth_token", lib="your lib path")
  1. install from local file
pkg.dir <- "/Volumes/project_space/yu3grp/software_JY/yu3grp/git_repo/SINBA" #"/research_jude/rgs01_jude/groups/yu3grp/projects/software_JY/yu3grp/git_repo/SINBA"
devtools::install_local(sprintf("%s/SINBA_1.1.tar.gz",pkg.dir),lib="your lib path")

Features

  • Comprehensive Drug-Gene Interaction Resources: Includes six curated drug-gene interaction databases, featuring the blood-brain barrier penetrant drug database (BPdb), which supports the selection of CNS-specific drugs.
  • Integrated Gene-Gene Interaction Network: Facilitates the inference of disease or subgroup-specific driver genes. These drivers may not have identifiable genetic alterations and can be “hidden” regulators affected by epigenetic, post-transcriptional, or post-translational mechanisms.
  • Synergy Discovery Platform: Harnesses the strengths of in silico prediction and high-throughput screening to identify novel synergistic drug pairs.

Credits and historical remarks

The companion packages and data portals

References

A. Khatamian, E. O. Paull, A. Califano and J. Yu Bioinformatics 2019: SJARACNe: a scalable software tool for gene network reverse engineering from big data. PMCID: PMC6581437
X. Dong, L. Ding, A. Thrasher, X. Wang, J. Liu, Q. Pan, et al.Nat Commun 2023: NetBID2 provides comprehensive hidden driver analysis. PMCID: PMC10160099