A performance evaluation of NoSQL databases to manage proteomics data

NoSQL databases have recently been introduced as alternatives to traditional relational database management systems because of their capabilities in terms of storing data and query retrieval. Biological datasets can be modelled using various models, for example, graphs (protein-protein interaction) or documents (protein sequence information). Applications that involve these two data models can be combined into a single unique architecture either using the polyglot persistence approach or using a multi-model approach. This paper evaluates the performance of a polyglot persistence approach versus a multi-model store. The polyglot persistence approach combines a graph-oriented database (Neo4j) and a document-oriented database (MongoDB); and the multi-model system is OrientDB. The comparisons are made following these aspects: importation, single operations, and query performance. OrientDB demonstrates a potential to manage large proteomics dataset for query retrieval and graph importation. However, when updating records, OrientDB was found to be slow. There is no single store that performs better in all cases.