Expression data
#Genome-build: GRCh38.p12
#Datasource: OCISG
#IFN system: Type I IFN in fibroblast
Fold change:
Expression data
#Genome-build: GRCh38.p12
#Datasource: Interferome
#IFN system:
Average fold change of up-regulations: Average fold change of down-regulations:

Defining the characteristics of interferon-alpha-stimulated genes

A virus-infected cell triggers a signalling cascade resulting in the secretion of interferons (IFNs) It in turn induces the up-regulation of IFN-stimulated genes (ISGs) that play an important role in the inhibition of the viral infection. We conducted detailed analyses on 7443 features relating to evolutionary conservation, nucleotide composition, gene expression, amino acid composition, and network properties to elucidate factors associated with the stimulation of human genes in response to the typical IFN-α. We propose that ISGs are less evolutionary conserved than genes that are not significantly stimulated in IFN experiments (non-ISGs). ISGs show significant depletion of GC-content in the coding region of their canonical transcripts, which leads to differential representation in their nucleotide and amino acid compositions. ISG products tend to be implicated in key pathways of the human protein-protein interaction (PPI) network but are away from the hubs or bottlenecks. Interferon-repressed human genes (IRGs), which are down-regulated in the presence of IFNs, can have similar properties to ISGs. Meanwhile, we also propose a machine learning framework integrating the support vector machine (SVM) and feature selection algorithms. It achieves an area under the receiver operating characteristic curve (AUC) of 0.7455 for ISG prediction and demonstrates the similarity between ISGs triggered by type I and III IFNs. The model predicts several genes as potential ISGs that so far have shown no significant differential expression when stimulated with IFN in the cell/tissue types in the available databases. This webserver is developed based on our project, allowing researchers to obtain prediction results for different human genes and their expression performance in IFN experiments.

Please cite the following article if using any of the results from this webserver:

Haiting Chai, Quan Gu, Joseph Hughes and David L. Robertson (202X), Defining the Characteristics of Interferon-alpha-stimulated Genes: Insight from Data Expression and Machine Learning, XXXX, XX(X): XXX-XXX, PMID: XXXXXXXX. link

Datasets & Supplementary Files

Background dataset of this project: Dataset S1
Refined ISGs and non-ISGs with high confidence: Dataset S2
IRGs in our background dataset: Dataset S4
Refined type I ISGs and non-ISGs from the Interferome database: Dataset S5
Refined type II ISGs and non-ISGs from the Interferome database: Dataset S6
Refined type III ISGs and non-ISGs from the Interferome database: Dataset S7
Human gene with limited expression from the OCISG database: Dataset S8
Human protein-protein interaction network with high confidence: Human_PPIs
Enriched short linear motifs in ISGs and their protein products: Enriched_SLim_List
Features used to build our prediction model: Feature_List
Features positively associated with higher up-regulation after IFN treatments: Positive_factors
Features negatively associated with higher up-regulation after IFN treatments: Negative_factors
The result of Mann-Whitney U tests for parametric features: U_tests
Association between feature representations and IFN-α stimulations: Feature_Association
The result of Pearson's chi-squared tests for sequence motifs: ChiSquared_tests
Decision trees generated during five-cross validation on the training dataset S2': Decision_trees