A linked data graph approach to integration of immunological data Systems biology involves the integration of multiple data types (across different data sources) to offer a more complete picture of the biological system being studied. While many existing biological databases are implemented using the traditional SQL (Structured Query Language) database technology, NoSQL database technologies have been explored as a more relationshipbased, flexible and scalable method of data integration. In this paper, we describe how to use the Neo4J graph database to integrate a variety of types of data sets in the context of systems vaccinology. Specifically, we have converted into a common graph model diverse types of vaccine response measurement data from the NIHNIAID ImmPort data repository, pathway data from Reactome, influenza virus strains from WHO, and taxonomic data from NCBI Taxon. While Neo4J provides a graphbased query language (Cypher) for data retrieval, we develop a webbased dashboard for users to easily browse and visualize data without the need to learn Cypher. In addition, we have prototyped a natural language query interface for users to interact with our system. In conclusion, we demonstrate the feasibility of using a graphbased database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to reveal novel relationships among heterogeneous biological data.

optimizing the Research of DNA Sequences in a NoSQL Document Database: A Preliminary Study The study of DNA sequences has become indispensable for basic biological research, and in numerous applied fields such as comparative genomics, evolutionary biology, pan genomics, genetics of disease, regulation of gene expression, oncology and many others, all supported by bioinformatics. In the era of Cloud computing, federating the Cloud systems of different genetics research organisations paves the way towards a new era of data sharing and new mashup services and applications. However, due to the huge amount of genomics data (genomics Big Data) that have to be managed, a parallel distributed NoSQL DataBase Management System (DBMS) approach becomes fundamental. Specifically, due to the textual nature of genomics data, a NoSQL DBMS appears to be the most suitable solution. In this paper, by considering the whole human genome, we present a preliminary study comparing this latter using MongoDB with a SQLlike database solution, i.e., MySQL in order to look for DNA sequences. Moreover, in order to optimize the research of genomics codes, we adopt hash functions that allow mapping nucleotides sequences of arbitrary size onto data of a fixed smaller size. Experiments, shows that MongoDB apart simplifying the management of genomics data provides better performances.