| Cells function through complex mechanisms, involving a number of biological events of different types. Thus, to characterize how cells function, measuring biological events at the genetic and epigenetic level is very important. Genetic level events include genome sequences and expression of genes. Epigenetic events are those that control genetic events. Fortunately, all of these genetic and epigenetic events can be measured by using sequencing technologies. However, each of these events generates a large volume of data, known as comics data, and integration of these big molecular data sets is challenging even for bioinformatics experts.;In this thesis, we propose a novel cloud-based bioinformatics platform, BioVLAB, for processing omits data and performing the integrated analysis of genetic and epigenetic omits data in the context of gene regulation. On top of the BioVLAB infrastructure, we developed two cloud-based systems: BioVLAB-MMIA-NGS and BioVLAB-mCpG-SNPEXPRESS. The BioVLAB-MMIA-NGS is designed for the integrated analysis of miRNA and mRNA data sets to investigate the regulatory effect of miRNA on gene regulation. The BioVLAB-mCpG-SNP-EXPRESS analyzes a three-way relationship between DNA methylation, sequence variations, and gene expression, and it can be used to examine the influence of promoter methylations and mutations to downstream gene regulation. Both information systems provide a user-friendly web-based interface to monitor the analysis process and integrate cloud resources on demand to handle big multi omits data set. The utility of the system is demonstrated by performing analysis of a phenotypically distinct 30 breast cancer cell line data set. Our experimental results show that the information systems successfully capture the characteristics of complex relationships and produce biologically meaningful result on the multi-omits data set. In conclusion, our approach provides pragmatic solutions to analyzing complex relationship between multi-omits data set. |
