Welcome Cécile Le Sueur

Cécile is a master student in Computational Science at EPFL (Switzerland). She joined the Huber group for an internship in August 2017 and will work with Dorothee and Arne on proteomics data to study drug (off-)targets and systems-wide effects of drug treatment.

The Huber Group on the 6th NCT Run

The race event of the National Center for Tumor Diseases (NCT) Heidelberg took place for the 6th time last Friday, 7 July 2017. More than 4.500 participants including patients and their families, physichians, scientists and friends of the NCT run “to opose cancer with a positive note” joined the event this year. An EMBL team supports this initiative every year.
See more (in German)

NCT Run - Huber Group

From the left to the right: Simone Bell, Olena Yavorska, Vladislav Kim, Frederik Ziebell, Almut Lütge and Britta Velten from the Huber Group.

EMBL Charity Cycle Challenge

On 30 June 2017 a group of 14 EMBL cyclists set off on an epic ride from Heidelberg to EMBL Grenoble over five days. Their goal is to raise money for the Kinderplanet, a charity that supports the families of children treated at the Heidelberg University Hospital. As the Kindergarten relies solely on donations in order to operate, their mission is to help Kinderplanet in supporting families of sick children.

30 June – 4 July 2017
5 days – 850 km distance – 16.500 m ascent
By completing this physically and mentally demanding cycling challenge they hope to spread the word about Kinderplanet charity and encourage our colleagues, friends and family members to donate for a great cause.

Almut Lütge and Mike Smith from the Huber Group are taking part in this epic ride!







CSAMA 2017 – Statistical Data Analysis for Genome Scale Biology

CSAMA 2017 (15th edition)
Statistical Data Analysis for Genome Scale Biology
Bressanone-Brixen, Italy (South Tyrol Alps)
June 11-16, 2017


  • Jennifer Bryan, RStudio and UBC
  • Vincent J. Carey, Harvard Medical School
  • Laurent Gatto, University of Cambridge
  • Wolfgang Huber, European Molecular Biology Laboratory (EMBL), Heidelberg
  • Martin Morgan, Roswell Park Cancer Institute, Buffalo
  • Johannes Rainer, European Academy of Bozen (EURAC)
  • Charlotte Soneson, University of Zurich
  • Levi Waldron, CUNY School of Public Health at Hunter College, New York

Teaching Assistants:

  • Simone Bell, EMBL, Heidelberg
  • Vladislav Kim, EMBL, Heidelberg
  • Lori Shepherd, RPCI, Buffalo
  • Mike L. Smith, EMBL, Heidelberg

The one-week intensive course Statistical Data Analysis for Genome-Scale Biology teaches statistical and computational analysis of multi-omics studies in biology and biomedicine. It covers the underlying theory and state of the art (the morning lectures) and practical hands-on exercises based on the R / Bioconductor environment (the afternoon labs). At the end of the course, you should be able to run analysis workflows on your own (multi-)omic data, adapt and combine different tools, and make informed and scientifically sound choices about analysis strategies.

Topics include:

  • Introduction to R and Bioconductor
  • The elements of statistics: hypothesis testing, multiple testing, regression, regularization, clustering and classification, parallelization and performance (machine learning), visualisation
  • RNA-Seq data analysis
  • Computing with sequences and genomic intervals
  • Working with annotation – genes, genomic features, variants, transcripts and proteins
  • Gene set enrichment analysis
  • Mass spec proteomics and metabolomics
  • Basis of microbiome analysis
  • Experimental design, batch effects and confounding
  • Reproducible research and workflow authoring with R markdown
  • Package development, version control and developer tools (incl. git, github, RStudio)
  • Working with large data: performance parallelisation and cloud computing

The course consists of

  • morning lectures: 20 x 45 minutes: Monday to Friday 8:30h – 12:00h
  • 4 practical computer tutorials in the afternoons (13:30h – 16:30h) on Monday, Tuesday, Thursday and Friday

Visit the course’s website at: http://www.huber.embl.de/csama

Welcome Frederik Ziebell

Frederik has a PhD in Applied Mathematics from Heidelberg University and the German Cancer Research Center (DKFZ). He works on developing statistical methods for high-dimensional heterogeneous data and the analysis of multi-omic level drug treatment effects in collaboration with Cellzome.

Welcome Almut Lütge

Almut is a master student in molecular biotechnology at the University of Heidelberg. She joined the Huber group in 2017 and works on the analysis of RNA-seq data from Chronic lymphatic leukemia (CLL) samples.

CSAMA 2016 – we were there

The 14th edition of the course Statistical Data Analysis for Genome Scale Biology took place in July 2016 in Bressanone-Brixen. More than 70 participants spent a fantastic week studying in a mix of lectures and practical computer tutorials, learning from the lecturers Wolfgang Huber, Martin Morgan, Vincent Carey, Charlotte Soneson, Simon Anders, Michael Love, Levi Waldron and Jennifer Bryan. Senior scientists, Postdocs and students from the Huber group participated to this course as well. Additionally, everybody enjoyed biking and hiking in the high-alpine regions. One of the highlights of that week was a wonderful hiking tour into the Plose Mountain.



















Wolfgang Huber, Mike Smith, Alejandro Reyes and Levi Waldron enjoyed a mountain bike tour in the late afternoon after the course.







Wolfgang Huber, Karsten Bach and Nils Kurzawa on another mountain bike tour on Saturday after the course.







The 2017 course will take place from 11 to 16 June.

Photos from Brixen by Junyan Lu.

The Huber Group on the 5th NCT Run

The race event of the National Center for Tumor Diseases (NCT) Heidelberg took place for the 5th time last Friday, 8 July 2016. Tousands of participants including patients and their families, physichians, scientists and friends of the NCT run “to opose cancer with a positive note” participated this year. An EMBL team supports this initiative every year.
See more (in German only)


From the left to the right: Annika Gable, Mike Smith, Britta Velten, Karsten Bach, Junyan Lu, Nils Kurzawa and Alejandro Reyes from the Huber Group.

New paper: Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

Abstract: Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values under the null hypothesis but informative of each test’s power or prior probability of the null hypothesis: www.bioconductor.org/packages/IHW. IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.

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