The project is carried out with the professional participation of UD-GenoMed Kft. (spin-off company of the Institute of Biochemistry and Molecular Biology of the University of Debrecen) and the Psychiatric and Psychotherapy Clinic of Semmelweis University.

During the project, our goal is to implement an IT system which can perform the automated analysis of the composition of the intestinal flora and the connections between various psychological diseases. In this examination, the system should be able to record clinical data related to the studied patient, follow through full microbiome analyses and explore the links between clinical and next-generation sequencing data.

An important function of the system is the development of an expert system module based on BigData procedures for analyzing the relationships between microbiome and clinical data. By analyzing the identified correlations, we can get closer to understanding the complex interactions between intestinal flora and psychological diseases, as well as develop prognosis procedures to increase the effectiveness of medical treatment.

In addition, the application should be able to perform taxon annotation of groups of bacteria identified as a result, of comparisons and screenings (Strain, Class, Order, etc.) based, on existing metagenom databases.

By involving patients diagnosed with major depression in the research – as part of a scientific sub-project - it is possible, to test the planned service and IT system, and on the other hand, to identify the microbiome patterns responsible for the development of the two diseases and/or differences in therapeutic responsiveness. Thus, by analyzing microbiome data, disease-specific and overlapping markers can be identified with the involvement of healthy, control patients. With the implementation of the sub-project, the large database ensures the examination of the symptoms of the two diseases and the relationship between microbiome patterns by, using data mining and machine learning procedures.

The system can be characterized by the following main cases of use :

• Clinical data management can be dynamically expandable for disease types (disease diagnostics, therapies, tests, treatment of findings).
• Microbiome workflow tracking: IT provision of the microbiome analysis process of test samples (sample arrival, sample validation, sample storage tracking, ...).
• Dynamic bioinformatics processing of sequencing genomic data: processing sequencing data according to DNA samples using bioinformatics transformations supported by a flexible bioinformatics workflow engine.
• Analysis of the correlation between microbiome and clinical data: An expert system based on Big Data and machine learning procedures provides the background for the analysis of the relationship between metagenome and clinical data - apart from the example disease model – provides, an opportunity to examine other disease models and their relationships.