Molecular biology produces and accumulates huge amounts of data that are generally integrated within graphs of molecules linked by various interactions. Exploring potentially interesting substructures (clusters, motifs) within such graphs requires proper abstraction and visualization methods. Most layout techniques (edge and nodes spatial organization) prove insufficient in this case. Royer et al. introduced in 2008 Power graph analysis, a dedicated program using classes of nodes with similar properties and classes of edges linking node classes to achieve a lossless graph compression. The contributions of this paper are twofold. First, we formulate and study this issue in the framework of Formal Concept Analysis. This leads to a generalized view of the initial problem offering new variants and solving approaches. Second, we state the FCA modeling problem in a logical setting, Answer Set programming, which provides a great flexibility for the specification of concept search spaces.

Summary Cutevariant is a GUI based desktop application designed to filter variations from annotated VCF file. The application imports data into a local SQLite database where complex filter queries can be built either from GUI controllers or using a Domain Specific Language called VQL. Cutevariant provides more features than existing applications and is fully customizable thanks to a complete plugins architecture. Availability and implementation Cutevariant is distributed as a multi-platform client-side software under an open source license and is available at https://github.com/labsquare/cutevariant. Supplementary information Supplementary data is available at https://cutevariant.labsquare.org.

Because of the increasing size and complexity of available graph structures in experimental sciences like molecular biology, techniques of graph visualization tend to reach their limit. To assist experimental scientists into the understanding of the underlying phenomena, most visualization methods are based on the organization of edges and nodes in clusters. Among recent ones, Power Graph Analysis is a lossless compression of the graph based on the search of cliques and bicliques, improving the readability of the overall structure. Royer et al. introduced a heuristic approach providing approximate solutions to this NP-complete problem. Later, Bourneuf et al. formalized the heuristic using Formal Concept Analysis. This paper proposes to extend this work by a formalization of the graph compression search space. It shows that (1) the heuristic cannot always achieve an optimal compression, and (2) the concept lattice associated to a graph enables a more complete exploration of the search space. Our conclusion is that the search for graph compression can be usefully associated with the search for patterns in the concept lattice and that, conversely, confusing sets of objects and attributes brings new interesting problems for FCA.