ANALYZE MULTI-OMICS DATA

Each button represents an algorithm and hides the complexity of building a pipeline by providing a visual interface with informative pop-ups. Some algorithms include optional inputs while most have a default preset that will be sufficient for a typical user. The pipeline options are dynamic, responding to the user’s data input and previous selection.

The T-BioInfo platform provides data processing, analysis and visualization options suited for the data type you are working with. The UCSC Genome Browser can be used to visualize Gene Expression or ChIP-seq data. Other types of data are more complex to visualize. Below is an example of a custom visualization module that allows the user to visualize longitudonal passage data from a CirSeq experiment using Dengue variation between two species. These custom visualizations can help navigate data between various formats in an integrated way and export areas of interest in CSV format for further analysis.

Multi-omics data-driven approaches are already playing a major role in in oncology and other chronic diseases such as asthma, mental disorders, chronic infectious diseases and diabetes. A great example of the need for multi-omics integration is the complex biology of cancer where cell sub-populations and several levels of regulation contribute to the challenges in precise diagnosis and treatment selection for patients. Often times, studies that start with public-domain databases like TCGA can help focus on a specific area of interest or a mechism that is later studied in cell lines and animal models. Integrating data from these various studies and public-domain resources into an actionable analysis output requires trying various approaches before the final result is obtained.