Download Genevec.csv Note: If only a gene was queried, Gene functional similarities are calculated with other 32450 genes. If a gene list was queried, the pair-wise similarities of these genes are calculated and plotted.
It may takes one minute to show. Patience pays. You can download all genes or pathways here gene list     pathway list

Table 1: The GO function based gene similarity calculated by cosine

Table 2: The GO function based gene-pathway similarity calculated by cosine


Table 3: The GO function based pathway-gene similarity calculated by cosine







Download Genevec.csv Note: If only a gene was queried, Gene functional similarities are calculated with other 11056 genes. If a gene list was queried, the pair-wise similarities of these genes are calculated and plotted.
It may takes one minute to show. Patience pays. You can download all genes or pathways here gene list     pathway list

Table 1: The GO function based gene similarity calculated by cosine


Table 2: The GO function based gene-pathway similarity calculated by cosine


Table 3: The GO function based pathway-gene similarity calculated by cosine



About the tool
    Gene Ontology (GO) is a widely used database for gene function annotation. Functions of a gene can be described by multiple GO terms, thus, gene or pathway functional similarities are often described sparsely and qualitatively. How to automatically and quantitatively compare functional similarity between genes and pathways? The tool applies the Latent semantic analysis (LSA) to the gene-GO term count matrix, then the genes are represented by 200-dimentional vectors, which can further be used for gene similarity calculation and pathway vector construction.
    Fig. 1. Schematic diagram for SemanticGO methodological overview. The SVD model is firstly constructed, then the original data is approximated by reducing dimensions from r to k. Rice or other novel data can be projected into the semantic space by folding-in. Gene vectors are then calculated from the reduced semantic space. Pathway vectors can be derived by adding member gene vectors. Those gene or pathway vectors are then used for similarity calculation. WGCNA can also be performed based on gene functional similarity matrix. Finally, a web tool for genes and pathways similarity exploration is provided.
How to use the tool
    The tool mainly consists of two tabs for gene similarity calculation, one for Arabidopsis and one for rice. In the left input panel, users can input a gene (Example:AT1G01010) or a list of genes separated by comma without space (Example:AT1G01010,AT1G33060,AT3G52990,AT4G26520). Or users can input a KEGG metabolism pathway ID (Example:ath00010). A download button is provided for downloading the gene vector data. If a gene is inputted, in the right main panel, three tables will be returned. Table 1 is the gene functional similarity between the input and all other genes. The similarity data can be ordered by ascending or descending by clicking the small triangle at the column header (also for Tables 2 and 3). Table 2 is the gene functional similarity between the input gene and all pathways. Table 3 is the pathway functional similarity between the input pathway and all genes. If multiple genes was inputted, Table 2 will be a symmetric matrix showing the pairwise gene functional similarities between the inputted genes.
    Citation:SemanticGO: a tool for gene functional similarity analysis in Arabidopsis thaliana and rice. Plant Science, 2020, Volume 297, 110527
    Please note that similarity calculation comsumes time.
Contact information
    If you have any questions or ideas about the tool, please contact the admin Wei Liu at Fujian Agriculture & Forestry University by email weilau@fafu.edu.cn






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