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: 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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Welcome Arne Smits

Arne has a PhD degree from the Radboud University (Nijmegen, The Netherlands). His previous research in the laboratory of Prof. Michiel Vermeulen focused on the identification and quantification of protein-protein and DNA-protein interactions using quantitative proteomics. As a EIPOD postdoctoral fellow at EMBL, he is studying the systems-wide effects of drug treatment in collaboration with Cellzome and the Steinmetz group.

CSAMA 2016 Statistics and Computing in Genome Data Science

CSAMA 2016 (14th edition)
Statistics and Computing in Genome Data Science
Bressanone-Brixen, Italy (South Tyrol Alps)
July 10-15, 2016


  • Simon Anders, Institute for Molecular Medicine, Helsinki
  • Jennifer Bryan, University of British Columbia, Vancouver
  • Vincent J. Carey, Channing Laboratory, Harvard Medical School
  • Wolfgang Huber, European Molecular Biology Laboratory (EMBL), Heidelberg
  • Michael Love, Dana Farber Cancer Institute and the Harvard School of Public Health
  • Martin Morgan, Roswell Park Cancer Institute, Buffalo, New York.
  • Charlotte Soneson, University of Zurich
  • Levi Waldron, CUNY School of Public Health at Hunter College, New York

Teaching Assistants:

  • Simone Bell, EMBL, Heidelberg
  • Alejandro Reyes, EMBL, Heidelberg
  • Mike L. Smith, EMBL, Heidelberg

The one-week intensive course Statistics and Computing in Genome Data Science 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). The course covers the primary analysis of high-throughput sequencing based assays in functional genomics and integrative methods including efficiently operating with genomic intervals, statistical testing, linear models, machine learning, bioinformatic annotation and visualization. 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 Bioconductor
  • Elements of statistics: hypothesis testing, multiple testing, regression, regularization, clustering and classification (machine learning), visualization
  • Computing with sequences and genomic intervals
  • Integrating multiple layers of ‘omic data
  • Working with annotation – genes, genomic features and variants
  • RNA-Seq data analysis and differential expression
  • Single-cell RNA-Seq
  • Proteomics primers
  • Interactive displays with Shiny

The course consists of

  • morning lectures: 20 x 45 minutes: Monday to Friday 8:30h – 12:00h
  • 4 practical computer tutorials in the afternoons (14:00h – 17:00h) on Monday, Tuesday, Thursday and Friday

The registration for CSAMA 2016 closed on June 15th 2016

Visit the course’s website at:

Congratulations Sophie Rabe

Sophie studied Molecular Biotechnology at the University of Heidelberg. She joined the Huber Group in Spring 2014 as student research assistant and worked on genomic biomarkers of drug sensitivity in primary blood cancer cells. For her PhD thesis, Sophie is now joining the group of Sascha Dietrich at NCT Heidelberg, where she will work on the SYMPATHY project, an integrated system medicine approach to personalized and targeted therapy in leukaemia and lymphoma. This multidisciplinary project is intended to have direct clinical impact on patient care in blood cancers.

Welcome Karsten Bach

Karsten Bach has a B.Sc. in Biology from the University of Bonn. He joined the Huber group in March 2016. At EMBL, he is finishing up his M.Sc. in Molecular Biotechnology and working on quantitative proteomics together with Dorothee Childs and Nikos Ignatiadis.

SOUND second consortium meeting

The second consortium meeting of the SOUND project, which is led by Wolfgang Huber, was hosted by our colleagues from IDMEC Lisbon. Principal investigators, postdocs and PhD students attended the meeting and presented recent results and progress in the work packages. All partners are well on track with their project aims and the atmosphere of sharing research developments was quite enthusiastic. Junyan Lu held a talk on “Mechanistic study of drug response using ex-vivo drug testing and multi-omics technology“ and Małgorzata Oleś provided a presentation on “Modulators of drug response identified by multivariate lasso regression”. Wolfgang Huber gave a lecture on “Data driven hypothesis weighting and false discovery rates”.

SOUND consortium

SOUND consortium







Photos from Lisbon by Junyan Lu.

Presentation by Dorothee

Dorothee Childs presented her work at the EMBL-Wellcome Genome Campus Conference “Target Validation using Genomics and Informatics 2015″, which took place from 8th to 10th December 2015 in Cambridge.
More than 200 people attended this ground-breaking conference. Dorothee gave a talk on “Screening for drug targets in intact cells by thermal proteome profiling”, a project in which she is involved as part of a collaboration between EMBL Heidelberg and GSK’s SME Cellzome, which is also based in Heidelberg.”


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Welcome Annika

Annika Gable has a B.Sc. in Molecular Biotechnology from the University of Heidelberg. She joined the Huber group in December 2015. At EMBL, she is finishing up her M.Sc. in Molecular Biotechnology (major: Bioinformatics) with a thesis on analyzing high-throughput chromatin conformation capture (Hi-C) data.