Mayday - integrative analytics for expression data

Center for Bioinformatics Tübingen, University of Tübingen, Sand 14, 72076 Tübingen, Germany.
BMC Bioinformatics (Impact Factor: 2.58). 03/2010; 11(1):121. DOI: 10.1186/1471-2105-11-121
Source: PubMed


DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.
We have rewritten large parts of Mayday's core to make it more efficient and ready for future developments. Among the large number of new plugins are an automated processing framework, dynamic filtering, new and efficient clustering methods, a machine learning module and database connectivity. Extensive manual data analysis can be done using an inbuilt R terminal and an integrated SQL querying interface. Our visualization framework has become more powerful, new plot types have been added and existing plots improved.
We present a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians. Most everyday tasks are already covered. The large number of available plugins as well as the extension possibilities using compiled plugins and ad-hoc scripting allow for the rapid adaption of Mayday also to very specialized data exploration. Mayday is available at

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Available from: Florian Battke
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    • "The standard technique for oneand two-way clustering is the clustered heatmap, where rows and/or columns are reordered to reflect the similarities. Examples for visual analysis tools that provide interactive heatmaps are Mayday [14], Caleydo [15,16] and the Dual Analysis framework [17]. For hierarchical clustering results, the clustered heatmap is commonly extended with a dendrogram that represents the similarities between the rows or columns [18]. "
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    ABSTRACT: Cluster analysis is widely used to discover patterns in multi-dimensional data. Clustered heatmaps are the standard technique for visualizing one-way and two-way clustering results. In clustered heatmaps, rows and/or columns are reordered, resulting in a representation that shows the clusters as contiguous blocks. However, for biclustering results, where clusters can overlap, it is not possible to reorder the matrix in this way without duplicating rows and/or columns. We present Furby, an interactive visualization technique for analyzing biclustering results. Our contribution is twofold. First, the technique provides an overview of a biclustering result, showing the actual data that forms the individual clusters together with the information which rows and columns they share. Second, for fuzzy clustering results, the proposed technique additionally enables analysts to interactively set the thresholds that transform the fuzzy (soft) clustering into hard clusters that can then be investigated using heatmaps or bar charts. Changes in the membership value thresholds are immediately reflected in the visualization. We demonstrate the value of Furby by loading biclustering results applied to a multi-tissue dataset into the visualization. The proposed tool allows analysts to assess the overall quality of a biclustering result. Based on this high-level overview, analysts can then interactively explore the individual biclusters in detail. This novel way of handling fuzzy clustering results also supports analysts in finding the optimal thresholds that lead to the best clusters.
    Full-text · Article · May 2014 · BMC Bioinformatics
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    • "All of the CAZyme genes (CEs, PLs, and GHs) identified in F. graminearum were expressed in these experiments. The expression profiles of these genes could be categorized into nine models by k-means clustering algorithm implemented in program Mayday [51]. The expression profiles of spike infection of barley and wheat head were similar to each other but different from those of conidium germination. "
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    ABSTRACT: The version of this article published in BMC Genomics 2013, 14: 274, contains 9 unpublished genomes (Botryobasidium botryosum, Gymnopus luxurians, Hypholoma sublateritium, Jaapia argillacea, Hebeloma cylindrosporum, Conidiobolus coronatus, Laccaria amethystina, Paxillus involutus, and P. rubicundulus) downloaded from JGI website. In this correction, we removed these genomes after discussion with editors and data producers whom we should have contacted before downloading these genomes. Removing these data did not alter the principle results and conclusions of our original work. The relevant Figures 1, 2, 3, 4 and 6; and Table 1 have been revised. Additional files 1, 3, 4, and 5 were also revised. We would like to apologize for any confusion or inconvenience this may have caused. Background Fungi produce a variety of carbohydrate activity enzymes (CAZymes) for the degradation of plant polysaccharide materials to facilitate infection and/or gain nutrition. Identifying and comparing CAZymes from fungi with different nutritional modes or infection mechanisms may provide information for better understanding of their life styles and infection models. To date, over hundreds of fungal genomes are publicly available. However, a systematic comparative analysis of fungal CAZymes across the entire fungal kingdom has not been reported. Results In this study, we systemically identified glycoside hydrolases (GHs), polysaccharide lyases (PLs), carbohydrate esterases (CEs), and glycosyltransferases (GTs) as well as carbohydrate-binding modules (CBMs) in the predicted proteomes of 94 representative fungi from Ascomycota, Basidiomycota, Chytridiomycota, and Zygomycota. Comparative analysis of these CAZymes that play major roles in plant polysaccharide degradation revealed that fungi exhibit tremendous diversity in the number and variety of CAZymes. Among them, some families of GHs and CEs are the most prevalent CAZymes that are distributed in all of the fungi analyzed. Importantly, cellulases of some GH families are present in fungi that are not known to have cellulose-degrading ability. In addition, our results also showed that in general, plant pathogenic fungi have the highest number of CAZymes. Biotrophic fungi tend to have fewer CAZymes than necrotrophic and hemibiotrophic fungi. Pathogens of dicots often contain more pectinases than fungi infecting monocots. Interestingly, besides yeasts, many saprophytic fungi that are highly active in degrading plant biomass contain fewer CAZymes than plant pathogenic fungi. Furthermore, analysis of the gene expression profile of the wheat scab fungus Fusarium graminearum revealed that most of the CAZyme genes related to cell wall degradation were up-regulated during plant infection. Phylogenetic analysis also revealed a complex history of lineage-specific expansions and attritions for the PL1 family. Conclusions Our study provides insights into the variety and expansion of fungal CAZyme classes and revealed the relationship of CAZyme size and diversity with their nutritional strategy and host specificity.
    Full-text · Article · Apr 2013 · BMC Genomics
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    • "Enrichment of pathways was tested using the Reactome (v3.0) and the KEGG (v3.0) database from the Molecular Signature Database (MSigDB). Heatmaps were carried out with Mayday 2.8 [15]. For presentation of treatment effects in the heatmap, the pathway normalized enrichment score (NES) was adjusted with the appropriate FDR as follows: adjustedNES = (1−FDR)×NES. "
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    ABSTRACT: The liver X receptor α (LXRα) is a ligand-dependent nuclear receptor and the major regulator of reverse cholesterol transport in macrophages. This makes it an interesting target for mechanistic study and treatment of atherosclerosis. We optimized a promising stilbenoid structure (STX4) in order to reach nanomolar effective concentrations in LXRα reporter-gene assays. STX4 displayed the unique property to activate LXRα effectively but not its subtype LXRβ. The potential of STX4 to increase transcriptional activity as an LXRα ligand was tested with gene expression analyses in THP1-derived human macrophages and oxLDL-loaded human foam cells. Only in foam cells but not in macrophage cells STX4 treatment showed athero-protective effects with similar potency as the synthetic LXR ligand T0901317 (T09). Surprisingly, combinatorial treatment with STX4 and T09 resulted in an additive effect on reporter-gene activation and target gene expression. In physiological tests the cellular content of total and esterified cholesterol was significantly reduced by STX4 without the undesirable increase in triglyceride levels as observed for T09. STX4 is a new LXRα-ligand to study transcriptional regulation of anti-atherogenic processes in cell or models, and provides a promising lead structure for pharmaceutical development.
    Full-text · Article · Feb 2013 · PLoS ONE
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