Background Genomic-scale sequence alignments are increasingly used to infer phylogenies in order to better understand the processes and patterns of evolution. competing phylogenies from gene partitions found in three mid- to large-size mitochondrial genome alignments. We test the overall performance of these dimensionality reduction methods by applying several goodness-of-fit steps. The intrinsic dimensionality of each data set is also estimated to determine whether projections in 2- and 3-sizes can be expected to reveal meaningful relationships among trees from different data partitions. Several new approaches to aid in the assessment of different phylogenetic landscapes are presented. Results Curvilinear Components Analysis (CCA) and a 309913-83-5 manufacture stochastic gradient decent (SGD) optimization method give the best representation of the original tree-to-tree range matrix 309913-83-5 manufacture for each of the three- mitochondrial genome alignments and greatly outperformed the method currently used to visualize tree landscapes. The CCA?+?SGD method converged at least while fast while previously applied methods for visualizing tree landscapes. We demonstrate for those three mtDNA alignments that 3D projections significantly increase the match 309913-83-5 manufacture between the tree-to-tree distances and may facilitate the interpretation of the relationship among phylogenetic trees. Conclusions We demonstrate that the choice of dimensionality reduction method can significantly influence the spatial relationship among a large set of competing phylogenetic trees. We spotlight the importance of selecting a dimensionality reduction method to visualize large multi-locus phylogenetic landscapes and demonstrate that 3D projections of mitochondrial tree landscapes better capture the relationship among the trees 309913-83-5 manufacture being compared. Electronic supplementary material The online version of this 309913-83-5 manufacture article (doi:10.1186/s12859-017-1479-1) contains supplementary material, which is available to authorized users. Keywords: Mitochondrial DNA, MDS, NLDR, Combining data, Visualization, Tree scenery, Bootstrap Background The quick increase in the availability of genomic-scale multiple sequence alignments covering varied units of taxa gives new and fascinating opportunities for those seeking to understand the processes and patterns of molecular development and brings us a step closer to solving such grand difficulties as assembling a Tree of Existence. In practice however, areas (e.g., genes, codons, and structural features) of large multi-source data units seldom support a single phylogenetic tree. More often than not, we are remaining to sort through hundreds if not thousands of competing phylogenies. Different data partitions may support different phylogenies because reconstruction methods sometimes fail to properly accommodate process heterogeneous underlying data partitions found within an positioning [1C4] or because some data partitions just do not share the same evolutionary history, (observe Maddison  and recommendations cited therein). Furthermore, large data sets are typically more computationally demanding to analyze and frequently call for more intense heuristic shortcuts, which may fail to converge to a global optimum . Consequently, visually representing the similarity or dissimilarity among competing phylogenic trees supported by different genes or by additional a priori defined data partitions in 2 or 3-dimensional space is definitely a potentially powerful way for investigators to gain a better perspective on the problems sometimes associated with the analysis of large multi-source data units . To day, the typical approach used to summarize a set of phylogenetic trees is to create a solitary consensus tree from your set of competing trees, in which the vertices of the consensus tree are only retained if they are shared by a majority of the trees contained within the set of candidate trees. Phylogenetic network  and maximum agreement subtree  methods also result in concise summaries for units of conflicting trees whether the conflicts are caused by reticulate events or by modeling errors. These methods, while easy to interpret, lack info concerning the distribution and relationship among the candidate trees. Refinements to the consensus tree approach have been made by applying clustering methods to determine subsets of related phylogenies contained within the larger set . An appealing facet of this method is definitely that it can be used as an objective means to determine discontinuities in the distribution of candidate phylogenetic trees or the phylogenetic scenery. However, the clustering approach still discards a great deal of info and lacks the fine-grain perspective needed to infer the cause of the discordance among the competing trees. Motivated from the inherent limitations of the consensus tree approach, Amenta and Klinger  applied a dimensionality reduction method that they referred to as iterative Multidimensional Scaling (MDS) to display tree-to-tree distances inside a 2-dimensional space. The practice of visually representing units of competing phylogenetic trees inside a geometric space can be separated into three major and sometimes computationally intensive parts: 1) the selection of a set of phylogenic trees to be compared; 2) the calculation of pairwise distances between all users of Tmem34 the set of selected phylogenetic trees; and 3) the calculation of coordinates in 2 or 3-dimensional space, such that the Euclidean range.