Address: Dr. Inke Herms AG Genominformatik Technische Fakultät Universität Bielefeld Postfach 100131 33501 Bielefeld Office: U10-145 Phone: +49 (0)521-106-4915 Fax: +49 (0)521-106-6495 Email: Inke Herms

Introduction:

I've studied biomathematics at the University of Greifswald. Then I worked in the Biomathematics and Theoretical Bioinformatics group at Bielefeld University, where I finished my diploma thesis entitled Deterministic and stochastic recombination dynamics in July 2005. From August 2005 to October 2009 I was a PhD student of the Graduate School for Bioinformatics and Genome Research. In February 2006 I joined the Genome Informatics group (junior group: Computational Methods for Emerging Technologies (COMET)). I defended my PhD-thesis entitled Probabilistic Arithmetic Automata - Applications of a Stochastic Computational Framework in Biological Sequence Analysis on the 10/30/2009. Now, I work as a post-doctoral researcher in the genome informatics group. I am currently concentrating on the development, implementation and evaluation of algorithms concerned with the statistical analysis of metagenomic samples and comparisons thereof.

Research interests:

• in general, working in the interdisciplinary area of mathematics, computer science and biology: probability theory, sequence analysis, proteomics, high-throughput sequencing
• stochastic models like Markov chains and Hidden Markov Models (and in particular PAAs), their simulation and application (e.g. in proteomics or population genetics)
• combinatorial and probabilistical analysis of protein identification via mass spectrometry and of short sequence reads

Research projects:

• Metagenomics
  We investigate questions concerning the reliability of taxonomic and functional classification of metagenomic samples and the comparison between different samples.
• Probabilistic Arithmetic Automata (PAA) and their applications in biological sequence analysis
  My PhD-project concerns the investigation of a new class of probabilistic automata, called Probabilistic Arithmetic Automata (PAAs). These represent an extension to the widely used Hidden Markov Models (HMMs). In fact, the automata produce sequences of characters which are associated with a weight distribution. By means of corresponding recurrences, we can compute the probability distribution of the value resulting from a sequence of arithmetic operations performed on the emitted weights. Usually, the characters are represented by the states of the automaton, and the emissions as well as the operations depend on the states. PAAs have first been used to model proteolytic cleavage of proteins or peptides and ``measure'' the resulting fragments; in this context we understand the weights as the molecular masses of amino acids and compute e.g. the length and mass distributions of ``typical'' peptide fragments. This allows us to calculate the significance of the occurrence of a certain mass in the mass spectrum of a sample protein, which provides a database-independent scoring of mass peaks. In other applications, the emitted weights may be indicators of the occurrence of a certain object. This relates to classical probabilistic pattern matching. The application from computational biology considered in the thesis is the computation of alignment seed sensitivity. Moreover, also deterministic information can be incorporated into the PAA framework. An according automaton has been constructed in order to compute the read length distribution of 454 sequencing reads.

  My publications:

  • Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling [PDF]
    Stefan Wolfsheimer, Inke Herms, Sven Rahmann and Alexander K Hartmann
    BMC Bioinformatics 2011, 12:47
  • Probabilistic Arithmetic Automata: Applications of a Stochastic Computational Framework in Biological Sequence Analysis [PDF]
    Inke Herms, 2009
    
  • Computing Alignment Seed Sensitivity with Probabilistic Arithmetic Automata [Link]
    Inke Herms and Sven Rahmann
    In Proceedings of the 8th Workshop on Algorithms in Bioinformatics, LNBI, volume 5251, pages 318-329, 2008
  • Single-Crossover Dynamics: Finite versus Infinite Populations [PDF]
    Ellen Baake and Inke Herms
    Bulletin of Mathematical Biology 2008 (70), pp. 603-624

  Recent Talks:

  • thesis defense [PDF]
  • Computing Alignment Seed Sensitivity with Probabilistic Arithmetic Automata, WABI 2008 [PDF]
  • AG-seminar SS 2006 [PDF]

  Teaching:

  • SS2010:
    • Sequenzanalyse II
    • GI seminar
    • Journal Club in Bioinformatics
  • WS 2005/2006: Tutorial for Algorithmic Stochastics in Bioinformatics
  • SS 2005: Tutorium zu Mathematische Biologie II: Wahrscheinlichkeitstheorie und Statistik