GeneCloudOmics

GeneCloudOmics performs high-throughput analysis of microarray and RNA-Seq gene expression data, providing reads normalization, scatter plots, linear and non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression, pathway enrichment, evolutionary and pathological analyses, and protein–protein interaction identification for transcriptomic and proteomic datasets.

Key Features:

• Supported data types: Microarray and RNA-Seq gene expression data are explicitly supported.
• Reads normalization: Implements reads normalization for transcriptomic data.
• Scatter plots and correlations: Generates scatter plots and computes linear and non-linear correlations.
• Dimensionality reduction: Performs principal component analysis (PCA).
• Clustering methods: Includes hierarchical clustering, k-means, t-SNE, and self-organizing maps (SOM).
• Differential expression analyses: Identifies differentially expressed genes (DEGs) across conditions.
• Pathway enrichment: Conducts pathway enrichment analyses.
• Evolutionary analyses: Provides analyses focused on evolutionary aspects of genes.
• Pathological analyses: Includes analyses related to pathological gene expression changes.
• Protein–protein interaction identification: Identifies protein–protein interactions (PPI).
• Proteomics support: Incorporates tools for analyzing protein datasets.
• NCBI GEO import: Allows direct importation of gene expression data from the NCBI Gene Expression Omnibus (GEO) database.
• Comprehensive task suite: Aggregates a suite of 23 bioinformatics tasks covering the above analyses.

Scientific Applications:

• Developmental biology: Analysis of gene expression changes across developmental stages.
• Host–parasite relationships: Comparative expression analyses to investigate host–parasite interactions.
• Disease progression and drug effects: Identification of genes involved in disease progression and evaluation of drug-induced expression changes.

Methodology:

Cloud-based transcriptome analysis that quantifies transcriptome-wide expression across conditions (e.g., development stages, mutants, diseases, drug treatments) to identify differentially expressed genes (DEGs) and support downstream pathway, evolutionary, pathological, and PPI analyses.