SegMine is a powerful methodology for semantic analysis of transcriptomic data. It offers improved hypothesis generation and data interpretation for life scientists. SegMine employs semantic subgroup discovery to construct elaborate rules which identify enriched gene sets, and link discovery for the creation and visualization of new biological hypotheses.
Standard microarray analysis would included functional analysis of differentially expressed (DE) genes through geneset enrichment methods (GSEA, Fisher’s exact test, GOStats...) using different ontologies (GO, Kegg, Reactome,…). Visualisation of such results is often lacking. The advanage of SegMine is that experimental results are interpreted in prior knowledge in the form of semantic rules, e.g. combined onotology terms which assists with biological interpretation of results. Visualisation of genes encompassed in a rule in prior knowledge using BioMine engine is also really helpful. In addition, BioMine can assist in generation of novel hypothesis through link discovery between different sets of DE genes from the same experimental dataset. This altogether brings biological interpretation of transcriptomics datasets to another level.
SegMine is currently available for analysis of human, mouse and rat experimental datasets (version for plant experimental datasets is still under development and will be available soon). You should start your analysis using Gene Ids linked to the measured data (many different IDs are now recognized by SegMine). Thus you are not technology limited – datasets can be produced using microarrays, RNASeq or high-throughput real-time PCR.
SegMine for ClowdFlows is under development! When completed, we will stop developing the current implementation in Orange4WS, and only bug fixes will be provided.