Developing Mango Graph Studio and its Applications for Bioinformatics and Systems Biology

Modern genomic and post-genomic research produced huge amount of heterogeneous data that must be integrated and analyzed together- Systems biology aims at utilizing these data to help model complex biological systems and further our understanding of living beings, so we may cure more diseases or harvest more renewable bio-energies in the future- However, construction and manipulation of complex graph or network data structures in computer memory to represent the heterogeneous data that are linked to each other require computer science skills- Software engineers who are capable of these in-depth programming efforts may not be able to form biological analysis algorithms or interpret the analysis results, while biologists who can formulate the hypotheses for analyses and understand the analysis results may not be good at programming- Although software engineers and biologists can form interdisciplinary research teams to solve systems biology problems together, it is not always efficient or possible to form such a team- Independent biologists who work on their respective biological research endeavors may prefer a novel software platform that can enable them to perform sophisticated systems biology analyses without the need to learn professional computer science skills- This preferred platform is Mango Graph Studio™- It comes at the right time when biological BIG DATA are no longer just the problems of large genome research centers but have gradually become the problems of every biologist- Mango Graph Studio strikes an optimal balance among its ease of uses (has a modern graphical user interface), flexibility in applications (comes with the Graph Exploration Language), computational power (automatically takes advantage of multi-core CPUs and many-core GPUs) and data scalability (handles million-node graphs easily even on personal computers)- Mango Graph Studio has been published in academic journals, and it will be the focuses of this SBIR Phase I project to advance its applications in bioinformatic research including systems biology, and to promote its uses by scientists in more research fields-