## 5 Conclusions and Future Work  
In this work, we presented a novel approach and its implementation in a set of automatic procedures able to extract, integrate, extend and standardize genomic and clinical data of The Cancer Genome Atlas as included in the Genomic Data Commons portal. Our approach and software were applied to multiple data types obtained from different types of NGS experiments (i.e., Gene-, miRNA-, Isoform-Expression Quantification, Masked Somatic Mutation, Copy Number Segment, Masked Copy Number Segment, Methylation Beta Value). Additionally, we considered clinical and biospecimen information about the experimental data. To reach our objective, we took advantage of the Genomic Data Model, which allowed us to represent an experimental sample by its genomic regions and its related metadata. The genomic regions are defined by their genomic coordinates (chr, left, right, strand) and genomic features, which are produced by the specific NGS experiment. Conversely, metadata report clinical and biological properties in attribute-value pair format. Based on the GDM representation, we implemented OpenGDC, a software for retrieving TCGA experimental data in the GDC portal, which is then processed with ad-hoc procedures for each data type. Our standardization procedure provides all the data in free-BED format, which contains a set of experiment-specific fields in addition to the genomic coordinates. In order to obtain this standardized format, the software is able to automatically extract additional features from external data sources (e.g., GENCODE, HGNC and miRBase), which are not provided in the original GDC data files. The software also integrates experimental data with clinical and biospecimen information derived from different GDC sources. Our pipeline extracts metadata attributes from the original Clinical and Biospecimen Supplements and from the GDC RESTful APIs. The obtained attributes are merged in a single metadata file for each experiment, using a tab-delimited key-value format. Then, two software components are used in the metadata pipeline: (i) the Data Redundancy Solver, to detect and remove redundant metadata attributes, and (ii) the Data Renaming Module, to redefine attribute names. In particular, data profiling is performed to identify redundant attributes, i.e., with the same values and different names. All these procedures and their input/output data types are thoroughly described in the OpenGDC Format Definition document, available as Additional File 1. We collected the standardized genomic data and metadata in a FTP repository, which we made publicly available at [42]. We also showed usage examples of these data through the application of GMQL queries, to highlight the validity and utility of our approach. They demonstrate that our data representation facilitates data retrieval, integrated processing and analyses, especially thanks to the combination of the filtering on specific clinical/biospecimen attributes and the extraction of genomic features. Future work concerns the application of our data representation and software pipeline to other projects integrated in the GDC portal and to other cancer-related repositories, in order to facilitate knowledge discovery over multiple cancer data. Additionally, we plan to use our approach and software in order to further enhance the data integration among different biomedical public repositories. Finally, we are going to take advantage of the standardized data, which is easily processable by several state of the art bioinformatics tools, in order to perform new knowledge extraction analyses about cancer.