PhD Student

Monica Ticlla


Computational PathoGenOmics / Bioinformatics

The growing application of high-throughput methods in life sciences opens novel opportunities to investigate pathogen-related mechanisms at system-wide scales. These newly emerging methods rely heavily on computational tools for the management and the analysis of data. Tools applied by the Computational PathoGenOmics group are predominantly based on state-of-the-art open source programs developed by the scientific community for the UNIX/linux computer operating system. Established approaches are supplemented by customized scripts and programs, and by data analysis in the R environment.

Typical research questions include the identification of genomic variants of pathogens or hosts associated with disease, of pathogen-induced alterations in gene expression levels, or also the assessment of epigenetic modifications of DNA.

The Computational PathoGenOmics group assists according to competencies all research groups at SwissTPH in the design of -omics experiments and the data analysis. As a research group affiliated to the Swiss Institute of Bioinformatics, the group furthermore coordinates bioinformatic activities in the -omics workgroup at SwissTPH, and participates in teaching of bioinformatic courses at the University of Basel.


DNA methylation in prokaryotes
The Gram-negative bacterium Neisseria meningitidis is a major causative agent of meningitis and septicaemia worldwide. Yet the colonization of the human nasopharyngeal mucosa by Neisseria meningitidis is not necessarily associated with disease, and to present there is no pathogenic genotype known which would allow to strictly distinguish disease-causing strains from inoffensive carrier strains.
The Computational PathoGenOmics group investigates in collaboration with the Molecular Immunology unit at Swiss TPH the epigenetic DNA methylation profiles of Neisseria meningitidis isolates. In this context we investigate also DNA sequence variants altering the activity of genes (phase variation) and develop methods to genotype repetitive sequences.

Components and consequences of transcription factor binding
Transcription factors bind to specific DNA sequences. In order to encode the sophisticated gene regulation in higher organisms, transcription factors assumingly act via interactions of several factors. Chromatin immunoprecipitation in combination with high-throughput sequencing assays (ChIP-seq) enables to determine binding profiles on a whole-genome scale.
The family of STAT transcription factors represents a central component in the signaling pathways activated subsequent to infections. The Computational PathoGenOmics group is involved in multiple collaborations aiming at an improvement of methods to interpret ChIP-seq data, and to elucidate mechanisms of interactions of transcription factors leading to specific gene expression.

Epigenetic modification altered by exposure to electromagnetic fields
Based on epidemiological evidence supporting an association between residential exposure to extremely low frequency magnetic fields (ELF MF) and childhood leukaemia, ELF MF have been classified as possibly carcinogenic to humans. The EU FP7 project “Advanced Research on Interaction Mechanisms of electromagnetic exposures with Organisms for Risk Assessment” (ARIMMORA) aims to scrutinize the underlying biophysical mechanisms. The Computational PathoGenOmics group has a leading role in the data analysis in the investigation of a potential effect of ELF MF on epigenetic DNA modifications.

Gene regulation in P. falciparum
The malaria parasite Plasmodium falciparum features a complex life cycle and large phenotypic variability. The underlying mechanisms regulating gene expression
The Computational PathoGenOmics group contributes in collaborations with the Gene Regulation unit and with the Molecular Parasitology unit at Swiss TPH to the analysis of expression data derived from microarray hybridizations and from RNA-seq assays.

Selected publications

Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, and Vinkemeier U (2014). STAT1-cooperative DNA binding distinguishes type 1 from type 2 interferon signaling. Nat. Immunol. 15, 168–176. PM:24413774


Weirauch et al. (2013). Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31, 126–134. PM:23354101

Witmer K, Schmid CD, Brancucci NMB, Luah Y-H, Preiser PR, Bozdech Z, and Voss TS (2012). Analysis of subtelomeric virulence gene families in Plasmodium falciparum by comparative transcriptional profiling. Mol. Microbiol. 84, 243–259. PM:22435676

Szalkowski AM, and Schmid CD (2010) Rapid innovation in ChIP-seq peak-calling algorithms is outdistancing benchmarking efforts. Briefings in Bioinformatics 12, 626-633. PM:21059603

Schmid CD, and Bucher P (2010) MER41 repeat sequences contain inducible STAT1 binding sites. PLoS ONE 5, e11425. PM:20625510

Schmid CD, and Bucher P (2007) ChIP-Seq Data Reveal Nucleosome Architecture of Human Promoters. Cell 131, 831-832. PM:18045524

Schmid CD, Praz V, Delorenzi M, Perier R, and Bucher P (2004) The Eukaryotic Promoter Database EPD: the impact of in silico primer extension. Nucleic Acids Res 32, D82-D85. PM:14681364