Substantial data comprising thousands of genes and transcripts often challenges efficient data analysis. Classical GO enrichment analyses based on a database search, though helpful for functional categorization of given gene sets, is less useful if detailed analysis is required at the level of studying genes involved in specific pathways and/or physiological functions. For this purpose, different tools have been developed and used with variable success, e.g., the TM4 suit by Saeed et al. (2003 (link)), and GoMinerTM by Zeeberg et al. (2003 (link)).
The MapMan-omics data analyses software (Thimm et al., 2004 (link); Usadel et al., 2005 (link); Urbanczyk-Wochniak et al., 2006 (link);http://mapman.gabipd.org ) allows visualization of -omics data at the process or pathway level. The software is designed and optimized to map transcriptomic data on currently available databases for many plant species, including A. lyrata, A. thaliana (Affymetrix, Agilent, TAIR 6, TAIR 7, TAIR 8, TAIR 9, and TAIR 10), Brassica napus, B. rapa, Carica papaya, Citrus, Eucalyptus grandis, Glycine max, Gossypium raimondii, Oryza sativa, Populus trichocarpa, Zea mays, and many other plant species. MapMan utilizes a hierarchical “BIN”-based ontology system. Specific bins are allocated to biological functions and sub-bins are allocated to individual steps or nodes in that particular biological function in a hierarchical order. For example, BIN number 20 is for stress, BIN number 20.1 is for biotic stress, and 20.2 is for abiotic stress. Similarly, sub-bins for abiotic stress include 20.2.1 (heat stress), 20.2.2 (cold stress), 20.2.3 (drought stress), 20.2.4 (wounding), and 20.2.5 (light). The bin and sub-bin approach minimizes the redundancy usually found in GO enrichment analyses. In addition, the software also utilizes gene expression values and displays the analyzed data as a diagram which enhances comprehension and is of a quality appropriate for presentation. Genes with increased or decreased expression levels are shown as color-coded squares in blocks. This tool has been widely used and constantly evolves to accommodate more plant species and data sets.
In order to get more meaningful information, we analyzed 6436 DEGs through MapMan version 3.6.0RC1 to visualize various genes involved in various pathways and biological functions and their expression patterns. For this purpose, all the data for the genes with significantly differential expression (P ≤ 0.05) were arranged in Microsoft Excel with their standard unique locus identifiers and their final expression value [Log2 (FPKM treated/FPKM untreated)] and saved in a tab-delimited format. These files were then mapped against the Arabidopsis “Ath_AGI_LOCUS_TAIR10_Aug2012.m02” database in MapMan. After analyzing the data, selected pathways were combined by adding their respective bins and sub-bins to custom-made images uploaded to MapMan.
The MapMan-omics data analyses software (Thimm et al., 2004 (link); Usadel et al., 2005 (link); Urbanczyk-Wochniak et al., 2006 (link);
In order to get more meaningful information, we analyzed 6436 DEGs through MapMan version 3.6.0RC1 to visualize various genes involved in various pathways and biological functions and their expression patterns. For this purpose, all the data for the genes with significantly differential expression (P ≤ 0.05) were arranged in Microsoft Excel with their standard unique locus identifiers and their final expression value [Log2 (FPKM treated/FPKM untreated)] and saved in a tab-delimited format. These files were then mapped against the Arabidopsis “Ath_AGI_LOCUS_TAIR10_Aug2012.m02” database in MapMan. After analyzing the data, selected pathways were combined by adding their respective bins and sub-bins to custom-made images uploaded to MapMan.
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