Skip to main content



Bioinformatics is a subdiscipline of biology and computer science that focuses on discovering, storing, analyzing, and disseminating biological data such as DNA and amino acid sequences. This kind of data often comes in the form of highly complex and interconnected networks. While most of the data is currently stored in relational databases, the problem of complicated domain models and evergrowing data diversity presents a challenge to data scientists.

Memgraph as a graph database can efficiently map the relationships in such highly interconnected networks in the field of biology. From molecule to protein interactions, almost everything can be modeled using graphs and explored with graph algorithms.


Applying graphs

Protein-protein interaction networks

Proteins rarely act individually, they are often in interaction with other proteins to perform some function. Protein-protein interaction networks (PPIN) are used to simplify and model these complex interactions. In PPIN, proteins are represented as nodes. Interactions between proteins are described by edges connecting the corresponding nodes.

Researches have discovered an important property of PPINs and consequentially enabled us to develop methods for identifying essential proteins. Most proteins in PPINs interact with only several other proteins, meaning that they do not have many connections. However, there is a small number of proteins that interact with almost every other protein. These proteins, included in many interactions, can be detected in the network as highly connected nodes, called hubs and they are very likely essential proteins.

Sequence similarity networks

A sequence similarity network (SNN) enables the visualization of relationships among protein sequences. The proteins which are related in some way are most often grouped in clusters. The graph is made up of nodes that represent proteins, while the edges indicate similarity in amino acid sequence.

SNNs are used to explore relationships in large and diverse sets of sequences because the computational resources needed for traditional methods of analysis (for example, phylogenetic trees) would be unfeasible, due to the difficulty in generating accurate multiple alignments.

Disease networks

Systems biology experiments create an enormous amount of data of multiple modalities. Because of its complexity and rich semantics, this kind of data presents a hurdle for standard storage and analysis solutions.

Graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.

Where to next?

This text is a summary of one area that fits perfectly with the application of graphs. Therefore, we would like to have you with us when implementing some of these solutions. Share opinions, experiences and problems you encounter when working with Memgraph on our Discord server. We are here for you and we will help you along the way.