Molekulare Phylogenie, Pflanzenökologie & Systematik, TU Kaiserslautern

Pflanzenökologie & Systematik

Molecular Phylogenetics

J.-Prof. Dr. Frank Kauff

"Nothing in biology makes sense except in the light of evolution" Theodosius Dobzhansky (1973).

Where does the diversity of earth's organisms come from? How did this diversity develop? These are central questions in evolutionary biology. Since the 19th century we know that all living things share a common ancestor, and that species have changed - and still change - over time.

The science of phylogenetics tries to reconstruct the evolutionary history of organisms. Which organisms are closely related? What do we know about their common ancestor. What happened on the way from the ancestor to the specimen we can observe know? These questions are difficult to answer. Fossils that can tell us about the past are extremely rare, and for many groups of smaller and inconspicuous organisms they do not exist at all. This means for reconstructing the development that leads to today's diversity, we have to rely on what characters we can observe on today's organisms. These can be morphological or anatomical features, ecological, behavioral, or even geographical characteristics. But often many species are very small and do not exhibit many features (think of the many fungi that are only known as colorless hyphae), or the organisms of interest are so closely related that they are are hardly distinguishable. It was not until the usefulness of molecular characters for phylogenetic reconstruction was discovered that scientists were able to establish reasonable hypotheses for the evolution of some groups (Fungi again...).

However, although molecular data (usually information from DNA sequence data, ACGTGGAC....) does provide us with lots of information, it does not necessarily make things easier. Many of the problems we have with 'classical' morphological data are still present, but, due to the large amount of data (often thousands and ten thousands of characters for hundreds or sometimes thousands of organisms), on a much larger scale. To overcome these problems, sophisticated mathematical models and computer software together with large amounts of computational power are essential - analyses that run for days on more than a hundred CPUs are not uncommon.

Forschungsprojekte - Research Topics

Bioinformatic infrastructure for AFTOL

The NSF-initiated 'Assembling the Tree of Life' Project aims to "reconstruct the evolutionary origin of all living things". Researchers from all over the world are currently collaborating to generate phylogenies for the various branches of the Tree of Life, each focusing on their area of expertise. One of these sub-projects is AFTOL (Assembling the Fungal Tree of Life), which is a collaboration of five labs in four universities in the US. AFTOL aims at collecting the DNA sequences for seven genetic loci for a total of 1500 taxa. To efficiently coordinate the taxon sampling, DNA extraction and sequencing, and the various steps that are part of a large-scale multi-gene phylogenetic analysis, we developed WASABI. WASABI (Web Accessible Sequence Analysis for Biological Inference) is a software that provides the computational infrastructure for multi-user multi-gene phylogenetic projects. In addition to serving as a web-accessible sequence and voucher database, WASABI provides the functionality for automated sequence analysis, verification, and data set assembly. WASABI is unique as it supports the user by carrying out all steps from processing raw sequence data to a multi-gene sequence analysis within a strong phylogenetic context. Although originally developed as a computational framework for the AFTOL project, WASABI can easily be adapted to the specific needs of most multi-user multi-gene sequencing projects.

Analysis of multi-gene data sets

Analyses based on concatenated data sets from multiple genetic loci are very common. Multi-gene data sets naturally provide larger amounts of data, and - ideally - comprise information from independent loci, which in theory should result in a better supported phylogenetic hypothesis. However, if analyzed separately, data sets from different genes for the same set of taxa can yield different topologies. The question is: should - or can - such data sets be combined for a multi-gene analysis? How can we determine whether two or more data sets are too "different" for a combination? Should we at all care about it (and just always combine)? One way to investigate this is using complex simulations, where artificial (conflicting / non-conflicting) data sets are analyzed, combined and individually, under various models of evolution.

Systematic of lichenized ascomycetes