Fig. 2
From: PyGMQL: scalable data extraction and analysis for heterogeneous genomic datasets
Relationships between GMQLDataset and GDataframe. Data can be imported into a GMQLDataset from a local GDM dataset with the load_from_path function. Using the load_from_file, it is possible to load generic BED files, while load_from_remote enables the loading of GDM datasets from an external GMQL repository, accessible through TCP connection. The user applies operation on the GMQLDataset and triggers the computation of the result with the materialize function. At the end of computation, the result is stored in-memory in a GDataframe, which can be then manipulated in Python. It is possible to import data directly from Pandas with from_pandas. Finally, it is possible to transform a GDataframe structure back into GMQLDataset using the to_GMQLDataset function