Lifemap: Exploring the Entire Tree of Life

International audienceThe Tree of Life (ToL) is meant to be a unique representation of the evolutionary relationships between all species on earth. Huge efforts are made to assemble such a large tree, helped by the decrease of sequencing costs and improved methods to reconstruct and combine phylogenies, but no tool exists today to explore the ToL in its entirety in a satisfying manner. By combining methods used in modern cartography, such as OpenStreetMap, with a new way of representing tree-like structures, I created Lifemap, a tool allowing the exploration of a complete representation of the ToL (between 800,000 and 2.2 million species depending on the data source) in a zoomable interface. A server version of Lifemap also allows users to visualize their own trees. This should help researchers in ecology and evolutionary biology in their everyday work, but may also permit the diffusion to a broader audience of our current knowledge of the evolutionary relationships linking all organisms

Tanglegrams are misleading for visual evaluation of tree congruence

Evolutionary Biologists are often faced with the need to compare phylogenetic trees. One popular method consists in visualizing the trees face to face with links connecting matching taxa. These tanglegrams are optimized beforehand so that the number of lines crossing (the entanglement) is minimal. This representation is implicitly justified by the expectation that the level of entanglement is correlated with the level of similarity (or congruence) between the trees compared. Using simulations, we show that this correlation is actually very weak, which should preclude the use of such technique for getting insight into the level of congruence between trees

Zombi: A phylogenetic simulator of trees, genomes and sequences that accounts for dead lineages

International audienceHere we present Zombi, a tool to simulate the evolution of species, genomes and sequences in silico, that considers for the first time the evolution of genomes in extinct lineages. It also incorporates various features that have not to date been combined in a single simulator, such as the possibility of generating species trees with a pre-defined variation of speciation and extinction rates through time, simulating explicitly intergenic sequences of variable length and outputting gene tree - species tree reconciliations

Tanglegrams Are Misleading for Visual Evaluation of Tree Congruence

International audienc

Principle of the tiling system used in Lifemap.

<p>Like in OSM, the image displayed at a given zoom level is composed of paving (or tiling) of small square images. To each of these square images at a given zoom level corresponds four images of the same size at the next level.</p

Screenshots of the Lifemap server.

<p>Visualizing a tree with the Lifemap server is a three-step process that is performed from the dedicated web page (top screen). Once the tree file has been uploaded and the computations have been performed, the tree can be explored (bottom screen). This last screenshot shows the appearance of Lifemap for a fully bifurcating tree with nodes automatically named (see text).</p

Three successive zoom levels in Lifemap illustrating the half-circle representation employed.

<p>Each clade is represented by a half-circle whose size depends on the relative number of species in the clade as compared to its sister clades at a given level. Note that these proportions are not respected at the tree root where the three superkingdoms are arbitrarily given the same size. Computation of the size of each half-circle is based on the angle they are associated with (α and β on the first panel): if <i>n</i>_<i>A</i> and <i>n</i>_<i>B</i> are the number of species in clades A and B, respectively, the angles in degrees are computed as follows: and . The square root reduces the difference in half-circle sizes between very small and very large groups. At every level, the half-circles (clades) are randomly distributed within their parental half-circle.</p

Screenshots of the Lifemap tool.

<p>(<b>A</b>) Example of the appearance of Lifemap when zooming to the primates order. (<b>B</b>) Example of information displayed in the general public version when clicking on a node. (<b>C</b>) Visualization of the path between two taxa. Lemur image credit: Mathias Appel, Flickr (<a href="https://flic.kr/p/FKtBbU" target="_blank">https://flic.kr/p/FKtBbU</a>).</p

Efficient prediction of co-complexed proteins based on coevolution.

The prediction of the network of protein-protein interactions (PPI) of an organism is crucial for the understanding of biological processes and for the development of new drugs. Machine learning methods have been successfully applied to the prediction of PPI in yeast by the integration of multiple direct and indirect biological data sources. However, experimental data are not available for most organisms. We propose here an ensemble machine learning approach for the prediction of PPI that depends solely on features independent from experimental data. We developed new estimators of the coevolution between proteins and combined them in an ensemble learning procedure.We applied this method to a dataset of known co-complexed proteins in Escherichia coli and compared it to previously published methods. We show that our method allows prediction of PPI with an unprecedented precision of 95.5% for the first 200 sorted pairs of proteins compared to 28.5% on the same dataset with the previous best method.A close inspection of the best predicted pairs allowed us to detect new or recently discovered interactions between chemotactic components, the flagellar apparatus and RNA polymerase complexes in E. coli

Phylo-MCOA: a fast and efficient method to detect outlier genes and species in phylogenomics using multiple co-inertia analysis

International audienc