Huber Group
Computational biology and genomics
Example image from a large-scale RNAi screen on populations of human cells stained for DNA (blue), tubulin (green) and actin (red). Images are automatically segmented, quantitative cell descriptors are computed and analysed for biological phenotypes by machine learning methods.
Time courses of cellular states observed by live cell imaging are mathematically modeled by a dynamical system with differential equations of motion
Independent filtering increases detection power for high-throughput experiments
Previous and current research
The group develops mathematical and statistical methods for the understanding of genomic data. We apply these methods to the design and analysis of novel, cutting-edge experimental approaches in genetics and cell biology. Our aim is to understand genetic and phenotypic variation on a genome-wide scale: how is the information that is encoded in the genome expressed? How is it processed in networks of interacting molecules in space and time? How does it differ between individuals and how does that affect their condition? How can we predict and tweak a biological system’s behaviour from such information?
The group brings together expertise from quantitative disciplines – mathematics, statistics, physics, and computer science – with the design and analysis of genomic experiments and their biological interpretation. Computational and statistical methods are often at the heart of systematic, large-scale experimental approaches. Our aim is to develop high-quality methods of general applicability that can be widely used in genomic research. We regard the publication of scientific software as an integral part of the publication of new methodical approaches and contribute to the Bioconductor project.
Future projects and goals
Progress in biology will continue to be driven by advances in technology. Sequencing now allows us to know the genomes of individual people and is providing transcription and DNA-protein interaction data for many different cellular systems at unprecedented precision. Light microscopy of single, live cells is providing data on molecular interactions and life cycles within the cell and is becoming increasingly automated. We can observe the dynamics of signalling, the cell cycle, and of cell migration both under normal conditions and many different perturbations (RNAi, drugs), and we start to understand the basis of biological variation between cells.
An emphasis of our work is on project-oriented collaborations with experimenters. We aim to develop the computational methods in statistics, probabilistic modelling, image analysis and bioinformatics that are needed to make new types of experiments feasible, and to turn the data into biology. Current projects include:
- Automated phenotyping from high-throughput microscopy: pattern recognition, machine learning, inference of dynamical models;
- DNA-, RNA- and ChIP-Seq and their applications to genetic variation and transcription regulation: data analysis, statistical modelling, regression and testing;
- genetic interactions by large-scale RNAi: design and analysis of combinatorial experimental approaches, sparse model building;
- protein expression and turnover from imaging: parameter estimation, model selection;
- open source soft ware for the analysis of sequencing data, for high-throughput phenotyping from automated microscopy, and for integrative bioinformatics.
10 years of the human genome
EMBL scientists mark a decade since the first draft human genome sequence. Click on the image to play.
Duration: 6 mins.