Holly Giles was awarded the Joachim-Herz Add-On Fellowship for Interdisciplinary Science. The fellowship enables interdisciplinary research and qualification (e.g. stays at a research lab and participation in conferences), specialized equipment and tools (laptops, software, etc.), participation in events of the Joachim Herz Foundation and fellowship meetings. Its a € 12.500 grant to be spent over a period of two years.
Dorothee Childs, Karsten Bach, Holger Franken, Simon Anders, Nils Kurzawa, Marcus Bantscheff, Mikhail Savitski and Wolfgang Huber
Detecting the targets of drugs and other molecules in intact cellular contexts is a major objective in drug discovery and in biology more broadly. Thermal proteome profiling (TPP) pursues this aim at proteome-wide scale by inferring target engagement from its effects on temperature-dependent protein denaturation. However, a key challenge of TPP is the statistical analysis of the measured melting curves with controlled false discovery rates at high proteome coverage and detection power. We present non-parametric analysis of response curves (NPARC), a statistical method for TPP based on functional data analysis and nonlinear regression. We evaluate NPARC on five independent TPP datasets and observe that it is able to detect subtle changes in any region of the melting curves, reliably detects the known targets, and outperforms a melting point-centric, single-parameter fitting approach in terms of specificity and sensitivity. NPARC can be combined with established analysis of variance (ANOVA) statistics and enables flexible, factorial experimental designs and replication levels. To facilitate access to a wide range of users, a freely available software implementation of NPARC is provided.
Arne H. Smits, Frederik Ziebell, Gerard Joberty, …, Lars M. Steinmetz, Gerard Drewes and Wolfgang Huber.
Gene knockouts (KOs) are efficiently engineered through CRISPR-Cas9-induced frameshift mutations. While DNA editing efficiency is readily verified by DNA sequencing, a systematic understanding of the efficiency of protein elimination has been lacking. Here, we devised an experimental strategy combining RNA-seq and triple-stage mass spectrometry to characterize 193 genetically verified deletions targeting 136 distinct genes generated by CRISPR-induced frameshifts in HAP1 cells. We observed residual protein expression for about one third of the quantified targets, at variable levels from low to original, and identified two causal mechanisms, translation reinitiation leading to N-terminally truncated target proteins, or skipping of the edited exon leading to protein isoforms with internal sequence deletions. Detailed analysis of three truncated targets, BRD4, DNMT1 and NGLY1, revealed partial preservation of protein function. Our results imply that systematic characterization of residual protein expression or function in CRISPR-Cas9 generated KO lines is necessary for phenotype interpretation.
After completing his pharmacy studies at University College Cork and the Royal College of Surgeons in Ireland, Donnacha joined the Huber group in October 2019 for a joint PhD with the Dietrich group at the National Centre for Tumour Diseases in Heidelberg. His current research focuses on using single-cell multi-omics to understand intra-tumour heterogeneity of drug response in blood cancers.
After a quite successful traineeship in the Huber group in 2018 and 2019, Emma Dann has moved to Cambridge to start her PhD studies at the Sanger Institute, located on the Wellcome Genome Campus and affiliated to Cambridge University. She is currently doing three months rotation projects before picking a lab for the PhD project. At the moment she is working on methods for alignment of scRNA-seq and scATAC-seq data in the lab of Sarah Teichmann.
Good luck and lots of success, Emma!
The Holmes and Huber labs collaborate on developing statistical tools for large multi-layer data analyses, for integrating large, heterogeneous biological data, and for finding applications in molecular medicine. They aim to deliver tools that are easy to use by domain-scientists to analyze their own data – for instance by providing the tools in the form of R / Bioconductor packages.
Together they want to help the next generation of biologists understand the “black box” of statistics by training them in quantitative statistical methods. They have written a textbook (Modern Statistics for Modern Biology) and together, they teach a summer course (Stats 366 – Bios 221) at Stanford. They keep further developing these materials, to take up new scientific developments (e.g. new data types), new methods, or new statistical or computational ideas.
The text book Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber has been published through Cambridge University Press (paperback). An online HTML version is also available. From the blurb: “If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you ‘cooking from scratch’, from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.”
The German Conference on Bioinformatics (GCB) is an annual, international conference devoted to all areas of bioinformatics and meant as a platform for the whole bioinformatics community. Recent meetings attracted a multinational audience with 250 – 300 participants each year.
In 2019, the conference focuses on bringing physicians, bioinformatics & medical informatics together and aims to showcase applications and opportunities beyond. Spearheading scientists will be presenting along with young researchers and industry representatives. Workshops will provide opportunities for hands-on experience.
The upcoming GCB will be held at the German Cancer Research Center in Heidelberg. The first day 16 September is reserved for workshops and satellite meetings. The main conference will take place from September 17-19. The schedule will allow for fly-in on Monday and fly-out on Thursday or Friday.
CSAMA 2019 (17th edition)
Statistical Data Analysis for Genome Scale Biology
Bressanone-Brixen, Italy (South Tyrol Alps)
July 21-26, 2019
- Vincent J. Carey, Harvard Medical School
- Laurent Gatto, University of Cambridge
- Robert Gentleman, 23andMe, Mountain View
- Wolfgang Huber, European Molecular Biology Laboratory (EMBL), Heidelberg
- Martin Morgan, Roswell Park Comprehensive Cancer Center, 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
- Simone Bell, EMBL, Heidelberg
- Lori Shepherd, RPCCC, 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.
- 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
The genome of pluripotent stem cells adopts a unique three-dimensional architecture featuring weakly condensed heterochromatin and large nucleosome-free regions. Yet, it is unknown whether structural loops and contact domains display characteristics that distinguish embryonic stem cells (ESCs) from differentiated cell types. We used genome-wide chromosome conformation capture and super-resolution imaging to determine nuclear organization in mouse ESC and neural stem cell (NSC) derivatives. We found that loss of pluripotency is accompanied by widespread gain of structural loops. This general architectural change correlates with enhanced binding of CTCF and cohesins and more pronounced insulation of contacts across chromatin boundaries in lineage-committed cells. Reprogramming NSCs to pluripotency restores the unique features of ESC domain topology. Domains defined by the anchors of loops established upon differentiation are enriched for developmental genes. Chromatin loop formation is a pervasive structural alteration to the genome that accompanies exit from pluripotency and delineates the spatial segregation of developmentally regulated genes.