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Q1: What are the evolutionary forces that have shaped the genomes of contemporary organisms and how can we infer the relevant parameters from multiple sequence alignments?
Q2: Can we develop mathematical, statistical and computational tools that help to analyse big data as generated by high throughput technologies in molecular biology?
Mathematical Models and efficient bioinformatics tools are the cornerstones to work on both questions. We develop such models and turn them into applicable software products for a wide user community. To understand the evolutionary forces, we are developing complex models of sequence evolution that in conjunction with tree reconstruction algorithms provide a comprehensive picture about the historical relationship of organisms and the changes that occur in a gene over time. Our approaches to Q2 are more diverse and are tailored to the special needs of high throughput technologies. We are interested in developing “stand-alone tools”, that can infer all relevant parameters from the input data.
We developed an efficient tree reconstruction algorithm to take the patchy structure of genomic alignments into account and to infer large phylogenetic trees. These methods have been included in the widely used software tool IQ-TREE (http://www.iqtree.org).
We tackled the following problem: For NGS experiments using unique molecular identifiers (UMIs), molecules that are lost entirely during sequencing cause under-estimation of the molecule count, and amplification artifacts like PCR chimeras cause over-estimation. TRUmiCount corrects UMI data for both types of errors, thus improving the accuracy of measured molecule counts considerably (https://cibiv.github.io/trumicount/).
ModelFinder: fast model selection for accurate phylogenetic estimates.
Kalyaanamoorthy, Subha; Minh, Bui Quang; Wong, Thomas K F; von Haeseler, Arndt; Jermiin, Lars S
TRUmiCount: Correctly counting absolute numbers of molecules using unique molecular identifiers.
Pflug, Florian G; von Haeseler, Arndt
Next-generation sequencing diagnostics of bacteremia in septic patients.
Grumaz, Silke; Stevens, Philip; Grumaz, Christian; Decker, Sebastian O; Weigand, Markus A; Hofer, Stefan; Brenner, Thorsten; von Haeseler, Arndt; Sohn, Kai
NextGenMap: fast and accurate read mapping in highly polymorphic genomes.
Sedlazeck, Fritz J; Rescheneder, Philipp; von Haeseler, Arndt
The von Haeseler group participates in the special Doctoral Program "RNA Biology" reviewed and funded by the Austrian Research Fund FWF.
The Group Von Haeseler participates in the Special Research Area (SFB) "RNA-Reg - RNA regulation of the transcriptome" funded by the Austrian Science Fund FWF. SFB's are peer-reviewed, highly interactive research networks, established to foster long-term, interdisciplinary co-operation of local research groups working on the frontiers of their thematic areas.
The FWF project "Parallel computing for phylogenetic interference" funds an international collaborative effort to optimize and improve bioinformatic analysis methods for molecular data. The final aim of the project is to implement those new methods and models to be scalable on all modern multi-core, accelerator and supercomputer architectures.