Figure 1. Conceptual framework to unify the known and unknown sequence space and integration of the framework in the current analytical workflows.

(A) Link between the conceptual framework and the computational workflow to partition the sequence space in the four conceptual categories. AGNOSTOS infers, validates and refines the GCs and combines them in gene cluster communities (GCCs). Then, it classifies them in one of the four conceptual categories based on their level of ‘darkness’. Finally, we add context to each GC based on several sources of information, providing a robust framework for generating hypotheses that can be used to augment experimental data. (B) The computational workflow provides two mechanisms to structure sequence space using GCs, de novo creation of the GCs (DB creation), or integrating the dataset in an existing GC database (DB update). The structured sequence space can then be plugged into traditional analytical workflows to annotate the genes within each GC of the known fraction. With AGNOSTOS, we provide the opportunity to integrate the unknown fraction into microbiome analyses easily. (C) The versatility of the GCs enables analyses at different scales depending on the scope of our experiments. We can group GCs in gene cluster communities based on their shared homologies to perform coarse-grained analyses. On the other hand, we can design fine-grained analyses using the relationships between the genes in a GC, that is detecting network modules in the GC inner sequence similarity network. Additionally, given that GCs are conserved across environments, organisms and experimental conditions give us access to an unprecedented amount of information to design and interpret experimental data.