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)

IAESTE - Research Exchange Fellowships

You will contribute to an international analysis team studying biomedical Big Data sets (

Typical analysis tasks include

  1. Building truly personal genome profiles for cancer prognosis
    Variation of individual human genomes was found to add over 300Mb of DNA to the human reference genome (over 10%!) making it necessary to assemble individual genomes to which known and novel genes need to be mapped. We explore the role of these truly personal genes in precision cancer prognosis.
  2. Applying Artificial Intelligence to Information Retrieval from the scientific literature to decode Drug Induced Liver Toxicity
    More than 2/3 of all drugs fail in the clinical phase due to unexpected toxicity. We are working with the US FDA on novel approaches to using Artificial Intelligence to identify and map scientific papers relevant to Drug Induced Liver Injury. On this use case, we develop and validate more generic powerful information retrieval algorithms.
  3. Exploring non-genetic sources of individuality
    We determine and investigate non-genetic molecular mechanisms that make you unique! For this we use both public data and can validate ideas in house on a Drosophila model.
  4. Pathway Analysis with Detection Bias Compensation
    All genome-scale screens exhibit a non-linear detection response. You will work with us to compensate for the resulting bias for more sensitive and accurate detection of biologically relevant patterns in genome-scale molecular profiles.

Our group also runs the world-wide Camda data analysis competition and conference to which you can contribute! (

Depending on your personal interests and skills with analysis environments like R/Bioconductor, modern machine learning, scientific data analysis, or tools for next generation sequencing analysis, you will be assigned to contribute to one or two of these topics for deeper study. You will be expected to provide a concise scientific report describing your findings and conclusions as backed by solid evidence from your analysis.

Join us! We offer a first class academic research environment. We provide intense support yet require a hard work ethic and personal independence.

Next steps: