CCRI Bioinformatics Team

The bioinformatics team provides advice and support for CCRI researchers with regard to analysis, integration and interpretation of large scale genomics datasets. We maintain and run computational pipelines for processing raw next-generation sequencing (NGS) data and data quality checks for all genomics data held within the CCRI/St.Anna Children´s Hospital. We are also performing high quality data analyses in close collaboration with CCRI/St.Anna Children´s Hospital researchers in the following thematic areas:

  • Variant analyses (small variants, SVs, CNVs, fusions) in genomics data (WGS, WES, low-coverage WGS, targeted sequencing, array technologies)
  • Transcriptomics (RNA-Seq, scRNA-Seq)
  • Epigenomics (WGBS, ChIP-Seq, ATAC-Seq)
  • Functional genomics (integrated analyses of the above)
  • Database and scientific software development and maintenance

We are furthermore conducting translational bioinformatics research in cooperation with CCRI researchers and external professionals.


Staff

Niko Popitsch (acting head)
I hold a PhD in computer science and an MSc in molecular biology and work as a bioinformatician with NGS data since 2011, doing both: working with researchers on challenging biological/medical projects as well as doing basic bioinformatics research and method development. I started my bioinformatics career in Arndt von Haeseler's lab (CIBIV) and then moved to Jenny Taylor’s group at the WTCHG/Oxford University in 2014. There, I was involved in several large-scale, translational whole-genome sequencing projects such as the 100,000 Genomes and the WGS500/HICF2 projects. Working with big rare disease and cancer datasets helped me to fully appreciate the power of genomics analyses for addressing complex medical and biological questions. I joined the CCRI in 2017 where I am now applying innovative genomics methods to paediatric cancer datasets. I am very happy to be embedded in a highly interdisciplinary setting that enables me to further extend my knowledge in various areas of cancer genomics and allows me to combine basic and applied bioinformatics research.
Publications:     https://www.researchgate.net/profile/Niko_Popitsch
Reviews:           https://publons.com/author/1385539/niko-popitsch
GitHub:              https://github.com/popitsch/


Maximilian Kauer
Coming from a "classical" biology background, I conducted a PhD in Drosophila population genetics at the VMU Vienna  (Schlötterer lab). During my Postdoc I worked on an Evo-Devo Drosophila genomics project (Yale School of Medicine, White lab) and started to focus on data analysis and bioinformatics. In 2007 I joined the CCRI to conduct data analyses of various pediatric cancer projects.  Coming from a biological background and having worked at the bench helps me to understand the biological problems being addressed at the CCRI and to apply state of the art bioinformatics analysis tools to answer these questions.

Gerda Modarres
After working in diagnostics in a cytogenetics laboratory as medical-technical assistant for several years, I decided in 2008 to do a Master’s degree in Bioinformatics in extra-occupational studies at FH-Campus-Wien. While still studying, I changed my job in 2013 to work in the bioinformatics field. I started to work for ACIB and did analyses in functional genomics and proteomics for biotechnological research projects. In 2016 I joined the CCRI where I am now developing a database and an application to link different queries for laboratory results for projects and studies.

Dagmar Schinnerl
I am a Postdoc in the Genetics of Leukemia Group and have a profound background in life sciences due to my master thesis at the University of Technology and my PhD in molecular biology and genetics, which I conducted at the CCRI. During this time I started with bioinformatics analysis of microarray and next generation sequencing data (in particular RNAseq and ChIPseq). Combining wet lab experiments with data analysis gives me the unique opportunity to shape research projects from beginning to end.


Florian Kromp
While working at the IT department of the St. Anna Kinderspital and studying Medical Informatics at the TU Wien, I started my scientific career in the department of immunological diagnostics working on the automation of B-Cell-ALL MRD analysis using flow cytometry and unsupervised machine learning methods. During my Master’s degree, I started to focus on biomedical image analysis of neuroblastoma tumors, specializing in supervised machine learning and, in particular, deep learning methods for an automated segmentation of fluorescence microscopy images. In addition to image segmentation, I  developed pipelines to analyse antibody expression of cellular populations using fluorescence microscopy. Recently, I have started to work as technical coordinator of the FFG project VISIOMICS, focusing on the integration of multiOMICs datasets including the application of bioinformatics and statistics approaches, data visualisation and visual analytics methods.

 Former members

  • Murat Tugrul
  • Christian Frech (Seven Bridges Genomics)

 

 

Selected Articles

Robbe P, Popitsch N, …,  Clinical whole-genome sequencing from routine formalin-fixed, paraffin-embedded specimens: pilot study for the 100,000 Genomes Project. (2018) Genet Med, doi:10.1038/gim.2017.241

Popitsch N, WGS500 Consortium, Schuh A, Taylor JC. (2017) ReliableGenome : Annotation of Genomic Regions with High/Low Variant Calling Concordance, Bioinformatics 33(2)

Vesely  C, Frech  C, …, R. Panzer-Grümayer. Genomic and transcriptional landscape of P2RY8-CRLF2-positive childhood acute lymphoblastic leukemia. Leukemia, 2016.

Taylor JC, …, Popitsch N, …, Gilean McVean, Factors influencing success of clinical genome sequencing across a broad spectrum of disorders (2015). Nature Genet 27(7)

Malinowska-Ozdowy K, Frech C, …, Panzer-Grümayer R. KRAS and CREBBP mutations: a relapse-linked malicious liaison in childhood high hyperdiploid acute lymphoblastic leukemia. Leukemia. 2015 Aug;29(8):1656-67

Popitsch N, von Haeseler A. (2013) NGC: lossless and lossy compression of aligned high-throughput sequencing data. Nucleic Acids Res. 41(1)

Bilke S, Schwentner R, Yang F, Kauer M, Jug G, Walker RL, Davis S, Zhu YJ, Pineda M, Meltzer PS, Kovar H. (2013) Oncogenic ETS fusions deregulate E2F3 target genes in Ewing sarcoma and prostate cancer. Genome Res. 23(11):1797-809

Hutter C, Kauer M, Simonitsch-Klupp I, Jug G, Leitner J, Bock P, Steinberger P, Bauer W, Carlesso N, Minkov M, Gadner H, Stingl G, Kovar H, Kriehuber E. (2012) Notch is active in Langerhans Cell Histiocytosis and confers pathognomonic features on dendritic cells. Blood. 120(26):5199-208

Fuka G, Kauer M, Kofler R, Haas OA, Panzer-Grümayer R. (2011) The leukemia-specific fusion gene ETV6/RUNX1 perturbs distinct key biological functions primarily by gene repression. PLoS One. 2011;6(10):e26348.

Ban J, Jug G, Mestdagh P, Schwentner R, Kauer M, Aryee DN, Schaefer KL, Nakatani F, Scotlandi K, Reiter M, Strunk D, Speleman F, Vandesompele J, Kovar H. (2011) hsa-mir-145 is the top EWS-FLI1-repressed microRNA involved in a positive feedback loop in Ewing's sarcoma. Oncogene. 2011 May 5;30(18):2173-80. doi: 10.1038/onc.2010.581

Kauer M, Ban J, Kofler R, Walker B, Davis S, Meltzer P, Kovar H. (2009) A molecular function map of Ewing's sarcoma. PLOS ONE 4: E5415

For more publications see the CCRI publications site or the individual researchers´ profiles.

 

Software

•    VARAN-GIE (since 2018) is an extension to the IGV genome browser that adds functionality to curate and annotate sets of genomic intervals. By this, VARAN supports an integrative approach to viewing and annotating large genomic data sets.
•    RG (since 2016) is a method for partitioning genomes into regions that can be genotyped with high/low confidence.
•    ARGOS (since 2014) is pipeline for extracting signals from a genomic sequence that characterize its repetitiveness.
•    CODOC (since 2014) is a format and API for the efficient representation and processing of depth-of-coverage data (complementing formats such as TDF or BigWig). It supports highly-efficient lossless and lossy data compression.