Multilevel ONtology Analysis

More and more high-throughput methods allow the investigation of biological functions across multiple "omics" levels. Levels include mRNA and protein expression profiling as well as additional knowledge on e.g. DNA methylation and microRNA regulation. Actual cellular responses to different conditions are best explained when taking all omics levels into account. Beyond the analysis on individual levels, only the integration of multi-level data gives better insights into altered biological functions. To map gene products to their biological functions, public ontologies like Gene Ontology (GO) are being used. Many methods were developed to find those terms in an ontology, which are overrepresented within a set of genes. These methods are not able to appropriately deal with any combination of several data types. Here, we propose a new method to analyze integrated data across multiple omics-levels to simultaneously assess their biological meaning. We developed a model-based Bayesian method for inferring interpretable GO term probabilities in a modular framework, such that any combination of omics-levels can be analysed. Our Multilevel ONtology Analysis (MONA) algorithm performed significantly better than conventional analyses of individual levels and yields best results even for sophisticated models including mRNA fine-tuning by microRNAs. MONA is easy and ready-to-use for applied researchers and will be a useful tool for any cross-omics data analysis.

How to use MONA

For each data type, the user has to provide a list whose length corresponds to the amount of measured genes. This list must consist of 0/1, while 1 indicates the differential expression of the gene at the respective position. The user also has to mark missing values among the second data type in the same way. Additionally, a list of indices is needed that assigns the genes to terms. Each row corresponds to the respictive gene and consists of comma-seperated indices. These indices, in turn, indicate the terms, which are named in a further file. The indices range from zero to the length of the term list minus one. Therefore, the first term in the list has the index zero. See the 'examples' folder for lists that are ready for use with MONA.

System requirements

Free download for non-commercial use only. Unzip the file and start mona.exe for running MONA.

  • Microsoft Windows 7
  • Microsoft .NET Framework 4.0


 By downloading this software I agree to use it for non-commercial purposes only.

Download MONA

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