Recent technological advances have revolutionized biological sciences. High-throughput “omics” techniques (such as next generation sequencing and mass spectrometry) allow to obtain comprehensive profiles encompassing genes, transcripts, enzymes and metabolites [1-3]. These techniques are nowadays routinely used for research purposes, and had an enormous impact in nearly all the fields of biological and biomedical sciences. Thanks to sequencing, for example, we now have phylogenetic trees encompassing all known living organisms, and we can glimpse the rich diversity of life on Earth [4-6].The unprecedented magnitude of omics data is a true challenge for the mind of any researcher: no human brain can truly grasp datasets comprising thousands of entities (e.g. genes). Thus, we necessarily rely on computational methods to make sense of the data, and also to summarize it in forms that can be visualized and comprehended.

When the data comes from a multitude of species (which we refer to as “comparative data”), there is an additional complication: the phylogenetic dimension, i.e., the fact that all species are related by a specific tree-like structure called phylogeny. The fundamental architecture of data from multiple species is thus defined by genetics; other entities also possess an underlying tree structure, defined by genetics or epigenetics (e.g. different cell types in the body). In the any analysis of comparative data, the phylogenetic structure must be taken into account at all times, since it dictates the trait (co-)variation expected under a null model of neutral evolution [7-9]. Comparative data provides a perspective which is orthogonal to that of most research in biology. Generally, biological systems are studied by measuring and interpreting the response to specific perturbations (e.g. the gene expression shift triggered by a drug treatment or genetic modification). Comparative biology methods work by interpreting the features of living organisms as resulting from “spontaneous experiments” of the natural world. Here, the perturbation dividing our observations is the effect of time and natural selection, acting on different lineages for millions of years after their split from a common ancestor. Because of the massive timescale involved, comparative research can study very large effects (e.g., perturbations in mammals can lead to a lifespan extension of 50%, whereas a 100-fold difference is observed across the mammalian lineage).

We thus embarked on the development of Treedex, a novel visualization and analysis framework, with two main objectives in mind.

1.  Create a fully interactive environment for the exploration of comparative data of any magnitude, that intuitively conveys the link between measured features and the phylogeny of source species. This will facilitate studying the evolution at large time-scales of fundamental molecular traits, such gene expression and metabolic regulation, that newly generated omics datasets now permit to investigate [10-14].
2. Integrate a wide set of state-of-the-art methodologies from evolutionary/comparative biology in the new environment. This will allow to take full advantage of modern comparative omics data, and make these tools easily accessible even to scientists with very limited experience in bioinformatics. The main focus will be on methods of evolutionary inference, which aim to discover the hidden functional links among measured features (e.g., reconstructing functional pathways of genes, analyzing their distribution, or their sequence, across multiple organisms). This includes tools such as phylogenetic profiling, protein coevolution finding, phylogenetic regression and others [15-23]. Additionally, Treedex will implement the widely used statistics employed in evolutionary sequence analysis.

Treedex has also the purpose to go beyond the vision of the phylogenetic tree just as a “complication” in a statistical analysis, and capitalize all the information that the tree can reveal about the functional network topology. Treedex allows to investigate the full evolutionary trajectories of any quantitative and qualitative trait, namely their ‘feature paths’ (a concept illustrate in the figure below). We plan to exploit this mindset as foundation for the design of novel comparative strategies of functional linkage prediction, implemented in practice as measures of ‘feature path similarity’. Such approach will allow to study functional linkage among features of any nature (e.g., sequences and quantitative traits together). Also, it will make possible to explicitly search for evolutionary trends that support functional linkage, such as convergent co-evolution, in a generic geometric setting.

References

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