News from the group:
Research Exchange Fellowships - IAESTE (apply)
CAMDA 2023 - ISMB Conference Track, 26-27 July, Lyon, France (read more)
World-leading patient stratification - graph based cancer data integration (read more)
Confirming molecular mechanisms of tendon regeneration - a powerful ovine fetal model (read more)
CAMDA 2022
ISMB Conference Track,
11-12 July, Madison, USA
(read more)
NVIDIA GTC Best Poster Award
for MM Kańduła
at GTC'18
Outstanding Presentation Prize
for MM Kańduła
at CAMDA'17
Outstanding Presentation Prize
for PP Łabaj
at CAMDA'15 (photo)
Austrian Marshall Plan Foundation scholarship
for MM Kańduła
at Boston University
OeAW APART fellowship
for PP Łabaj

Sequencing Quality Control (SEQC) project,
MAQC Consortium 2011–2014 (read more)
Host–parasite interactions in biocontrol, WWTF grant 2010–2013 (read more)

Power and limitations of RNA-Seq,
FDA SEQC, Nature Biotechnology (read more)
Characterization and improvement of RNA-Seq precision,
Bioinformatics (read more)
Impact of heavy tails in microarray analysis, Bioinformatics (read more)
Novel conserved repeats in sorting signals,
FEBS Journal (read more)
Sound sensation gene,
Nature communications
(read more)
RNA interference in ageing research,
Gerontology (read more)
Advanced models and tools for expression profiling
Efficient analysis of microarray data often balances two needs: the requirement for close interaction with experimentalists and the power of advanced models.

While existing tools like limmaGUI make it easy to rerun an existing model on new data, there is no support for the definition of the model. Many efficient analyses follow a balanced design and are based on an ANOVA. Defining a mixed effects linear model and the relevant contrasts, however, is FSPMA logo not trivial for biologists. In Sykcek et al. (2005) we have introduced a new approach to facilitate the interaction between experimentalists and data analysis experts for that task. Processing is controlled by a simple text file that defines and documents all steps of the analysis. The Friendly Statistics Package for Microarray Analysis provides the tool as well as extensive instructions. In addition, we offer hands-on lecture courses.

A probabilistic model for the analysis of shared gene function.a probabilistic model for the analysis of shared gene function (follow link for full figure and legend) In Sykcek et al. (2007) we have introduced and evaluated the power of a hierarchical Bayesian model for the integrated analysis of several microarray data sets in order to identify shared gene function. The approach has been been tested in simulations as well as in the combined analysis of in vitro and in vivo experiments using well understood experimental conditions as a reference. As expected, genes associated with apoptosis were identified as implicated both in mouse mammary gland development and cell line growth factor withdrawal. Our approach identified relevant genes with higher sensitivity and specificity than traditional threshold based alternatives.


  1. Sykacek P, Furlong RA, Micklem G (2005) A friendly statistics package for microarray analysis. Bioinformatics 21, 4069–70. (read more)
  2. Sykacek P, Clarkson R, Print C, Furlong R, Micklem G (2007) Bayesian modelling of shared gene function. Bioinformatics 23, 1936–44. (read more | Supplement)

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