Cancer Therapy Response and Resistance
TIDE (Tumor Immune Dysfunction and Exclusion)
TIDE is an infrastructure with several modules to assist cancer immunotherapy applications and research (Jiang et al., Nature Medicine, 2018). The first component is a gene expression biomarker to predict the clinical response to immune checkpoint blockade. The input is a gene expression profile of a cancer sample measured by RNA-Seq on genome-scale or Nano-String on a gene panel. The output is a likelihood score of therapy response or resistance. The second component provides gene query functions for the gene activity associations with T-cell dysfunction and immunotherapy response. The input is a gene name. The output is the associations between gene activity and cancer immune evasion potentials computed from a vast amount of datasets from human clinical studies or pre-clinical models.
CARE (Computational Analysis of REsistance)
CARE is a software developed to identify genome-scale biomarkers of targeted therapy response using compound screen data (Jiang et al., Cell Systems 2018). For each drug, its CARE score vector can serve as a pattern of good responder. Patients will be predicted as responders or non-responders depending on the Pearson correlation between the gene expression profile of cancer samples and CARE score vector. For each gene, the CARE score indicates the association between its molecular alteration and drug efficacy. A positive score indicates a higher expression value (or presence of mutation) to be associated with drug response, while a negative score indicates drug resistance. You can search the results on CCLE, CTRP and CTRP datasets here. Please use the auto-completed name when available.
Biological Network Analysis
NEST (Network Essentiality Scoring Tool)
NEST is designed to predict the gene essentiality based on protein interaction network and gene expression or epigenetic profiles (Jiang et al., Genome Bio 2015). NEST can also be used to enhance the quality of CRISPR or shRNA screen result.
RABIT (Regression Analysis with Background InTegration)
RABIT is a very efficient feature selection algorithm (Jiang et al., PNAS 2015). We applied RABIT to find gene expression regulators in shaping tumor specific gene expression patterns. The gene expression regulator could be transcription factor or RNA binding protein. Besides our application here, you can use RABIT as a general algorithm for feature selection.
SPICi (Speed and Performance In ClusterIng)
SPICi is a fast local network clustering algorithm (Jiang et al., Bioinformatics 2010). SPICi runs in time O(Vlog V +E) and space O(E), where V and E are the number of vertices and edges in the network. It also has state-of the-art performance with respect to the quality of the clusters it uncovers.
CCAT (Combinatorial Code Analysis Tool)
CCAT is a software package for predicting genome-wide co-binding between biological regulators such as Transcription factors (TF) (Jiang et al., Nucleic Acids Res 2014) or RNA binding proteins (RBP) (Jiang et al., PLoS Comput Biol 2013). The CCAT package also includes accompanying tools to cluster similar Position weight matrix (PWM) of different TFs or RBPs into clusters; and search PWMs on multiple genome alignments for conserved motif instances.