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Fig. 2 | BMC Bioinformatics

Fig. 2

From: Reconstructing ancestral gene orders with duplications guided by synteny level genome reconstruction

Fig. 2

a Estimating localizations. Here it shows how MULTIRES defines and infers localizations. Coloured solid wedges represent gene families, with wedges of the same colour belonging to the same family. Synteny blocks are indicated by hollow wedges, with colour indicating homology. The orientation of the wedges represent the orientation of the genes/blocks. Inferring adjacencies between gene families parsimoniously results in the ancestral adjacency graph shown on the top left, with edges representing adjacencies between gene ends. We also have a set of contiguous ancestral regions (CARs) reconstructed at the ancestor, each of which consists of an ordering of the ancestral synteny blocks. On the right of the tree, we display an example of a CAR, and the CAR after all synteny blocks have been doubled into head and tail extremities. We define windows of length 3 as consecutive subsequences of 3 extremities on the CAR. The windows, indicated by coloured line segments in the figure, are used to partition the CAR. In the diagram, we observe after partitioning that one copy of the brown gene (g 1) always occurs in the red window, and one always occurs in the blue window, and never in their intersection. This allows us to partition the brown gene family into two subfamilies, \({g^{1}_{1}}\) and \({g^{1}_{2}}\), called localizations, which are restricted to appear only in the relevant blocks, leading to the localized adjacency graph at the bottom. b Optimization and consensus. Here we show a localized adjacency graph (top) with copy numbers associated to each localization (numbers under the genes). Partitioning the ancestral CARs into segments (black line segments) defines an ordered sequence of induced subgraphs. Using the algorithm given by [24] on each induced subgraph results in a set of adjacencies shown at each layer, with each localization adjacent to at most as many adjacencies as its copy number. For example, the brown localization \({g_{2}^{1}}\) can have at most 1 copy in the gene order, making it adjacent to at most 2 other localizations. The algorithm indicates that these adjacencies are to the red (\({g^{3}_{1}}\)) and orange (\({g^{7}_{1}}\)) localizations. Finally, we combine the subgraphs and find a linear gene order by finding the most frequently conserved adjacencies and using the order of the segments. In the example, since the purple localization \({g_{2}^{2}}\) is only conserved in Segment 3, while the cyan localization \({g_{1}^{5}}\) is conserved in Segment 2 as well, we can resolve the gene order around the duplicated orange localization \({g_{1}^{7}}\)

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