Univ.-Prof. Dr. Arndt von HAESELER
Center for Integrative Bioinformatics Vienna (CIBIV)
(CIBIV is a joint institute of Vienna University, Medical University, and University of Veterinary Medicine, Vienna, Austria)
Alignment, Sequence evolution, Gene trees, Population genetics, Complex patterns of evolution
Our research interest is the integration of different areas of expertise to answer important biological questions. A special focus lies on the reconstruction of evolutionary history, especially, the development of phylogenetic methods and complex models and their application to large and complex datasets.
Currently we are working (in collaboration with various colleagues) on the following aspects of molecular evolution:
Statistics of sequence alignment (i.e. mcmcalgn). Recently we have extended this approach to reconstruct an alignment and a phylogenetic tree simultaneously.
We are working on models sequence that allow dependencies among sequence sites (i.e. Markov fields). We are developing test statistics to select the most suitable model, to detect groups of sequence that evolve differently from the rest of a gene family, and have developed a test to detect change points in a phylogenetic tree. Currently we are working on methods to detect the dependency structure among sequence positions in an alignment.
We develop efficient heuristic algorithms to reconstruct trees based on sequence data (i.e. TREE-PUZZLE). To this end we have developed parallel TREE-PUZZLE program. Moreover, we are currently developing a variant of TREE-PUZZLE, which computes (maximum) likelihood trees for up to 1,000 sequences in reasonable time. We are also working on super tree methods to merge different gene trees to form one species tree. Quartet based tree reconstruction method appear as a versatile tool to study super trees from a new perspective.
We are interested in the development and application of coalescence based methods to infer the demographic history of populations. In the future we plan to work on coalescence processes with complex interactions patterns.
Complex pattern of evolution
Recently, we have developed a maximum likelihood based method to estimate the amount of gene flow among prokaryotes by analyzing the COG database. This full genome analysis poses a collection of new computational problems as well as modeling problems.
The topics outlined above will eventually be employed to reconstruct one gigantic species tree utilizing all the sequence data available for the different species. Models of sequence evolution are necessary to detect differently evolving regions in complete genomes. Tree reconstruction methods for a large number of sequences allow the reconstruction of gene trees with several hundred sequences, and finally the patchiness of the available sequence data for different species makes it necessary to apply super tree methods. A better understanding of complex evolutionary patterns will also reveal instances where the gene trees are different from the species tree. Once this is well understood it seems reasonable to construct a sequenced based tree of life.
To understand the processes that have shaped the genomes of contemporary species, we apply methods from statistics, computer sciences, mathematics and computational statistics to develop models that mimic the process of evolution. These methods are further investigated in close collaboration with "wet" biologists to address real biological questions.
H.Q. Dinh, M. Dubine, F. Sedlazecke, N. Lettner, O. Mittelsten Scheid, and A. von Haeseler (2012) Advanced Methylome Analysis after Bisulfite Deep Sequencing: an Example in Arabidopsis. PLoS ONE, 7, e41528. (DOI: 10.1371/journal.pone.0041528, PMCID: PMC3401099)
I. Ebersberger, R. de Matos Simoes, A. Kupczok, M. Gube, E. Kothe, K. Voigt, and A. von Haeseler (2012) A Consistent Backbone for the Fungi. Mol. Biol. Evol., 29, 1319-1334. (DOI: 10.1093/molbev/msr285, PMID: 22114356)
A. von Haeseler (2012) Do we still need supertrees? BMC Biol., 10, 13. (DOI: 10.1186/1741-7007-10-13, PMID: 22369571)
T. Köstler, A. von Haeseler, and I. Ebersberger (2012) REvolver: Modeling sequence evolution under domain constraints. Mol. Biol. Evol., 29, 2133-2145. (DOI: 10.1093/molbev/mss078, PMID: 2383532)
T. Laubach, A. von Haeseler, and M.J. Lercher (2012) TreeSnatcher plus: capturing phylogenetic trees from images. BMC Bioinform., 13, 110. (DOI: 10.1186/1471-2105-13-110, PMID: 22624611)
M.A.T. Nguyen, T. Gesell, and A. von Haeseler (2012) ImOSM: Intermittent Evolution and Robustness of Phylogenetic Methods. Mol. Biol. Evol., 29, 663-673. (free reprint, DOI: 10.1093/molbev/msr220, PMID: 21940641)
S. Reitter-Pfoertner, A. von Haeseler B. Horvath, R. Sunder-Plassmann, V. Tiedje, I. Pabinger, and C. Mannhalter (2012) Identification of an ancient haemophilia A splice site mutation. Thromb. Res., 130, 445-450. (DOI: 10.1016/j.thromres.2012.02.008, PMID: 22401796)
P. Rescheneder, A. von Haeseler, and F.J. Sedlazeck (2012) MASon: Million Alignments In Seconds - A Platform Independent Pairwise Sequence Alignment Library for Next Generation Sequencing Data. Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2012), 195-201, SciTePress, Setubal, Portugal. (DOI: 10.5220/0003775701950201)
C. Veselye, S. Taubere, F.J. Sedlazeck, A. von Haeseler, and M.F. Jantsch (2012) Adenosine deaminases that act on RNA induce reproducible changes in abundance and sequence of embryonic miRNAs. Genome Res., 22, 1468-1476. (DOI: 10.1101/gr.133025.111, PMID: 22310477)
M. A. T. Nguyen, S. Klaere, and A. von Haeseler (2011) MISFITS: Evaluating the goodness of fit between a phylogenetic model and an alignment. Mol. Biol. Evol., 28, 143-152. (free copy, DOI: 10.1093/molbev/msq180, PMID: 20643866)
e contributed equally