Die QBiC-TechTalk Seminare nehmen technologische Aspekte der Quantitativen Biologie in den Fokus. Sie erhalten Einblicke in innovative Technologien, Methoden und praktischen Anwendungen in der Biowissenschaft. Ausgewiesene Experten, die eine intensive Forschung betreiben und/oder mit dem Schwerpunkt der Dienstleistung der quantitativen Biologie vertraut sind, präsentieren Ihnen die modernsten Felder der Quantitativen Biologie.
- Dr. Gary Bader, 05.11.2020, 15:00-16:00, Online via Zoom und
Youtube (+ Möglichkeit im Anschluss mit dem Experten zu sprechen): https://youtu.be/LMT7xY6KsAg, "Systems biology from single cell genomics to network data integration"
Single-cell RNA sequencing (scRNAseq) can map cell types, states and transitions during dynamic biological processes such as development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNAseq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNAseq data. In performance comparison tests, Tempora accurately inferred developmental lineages in human skeletal myoblast differentiation and murine cerebral cortex development, beating state of the art methods. Tempora uses biological pathway information to help identify cell type relationships and can identifyimportant time-dependent pathways to help interpret the inferred trajectory. Our results demonstrate the utility of time information to supervise trajectory inference for scRNA-seq based analysis.
Biological networks have the power to map cellular function, but only when unified to overcome their individual limitations such as bias and noise. Unsupervised network integration addresses this, automatically weighting input information to obtain an accurate, unified result. However, existing unsupervised network integration methods do not adequately scale to the number of nodes and networks present in genome-scale data and do not handle frequently encountered data characteristics (e.g. partial network overlap). To address this, we have developed an unsupervised deep learning-based network integration algorithm that incorporates recent advances in reasoning over unstructured data – namely the Graph Convolutional Network (GCN) – that can effectively learn dependencies between physical, co-expression and genetic interaction network topologies. Our method, BIONIC (Biological Network Integration using Convolutions), produces high quality gene and protein features which capture and unify information across many diverse functional interaction networks. BIONIC learns features which contain substantially more functional information compared to existing approaches, linking genes and proteins that share co-complex, pathway and bioprocess relationships.
- Dr. Kenneth Berendzen, 08.10.2020, 16:00-17:00, Online via Zoom und
Youtube (+ Möglichkeit im Anschluss mit dem Experten zu sprechen): https://youtu.be/04UiqvgZ9R0, "Using flow cytometry in plant science: advantages and limitations"
Flow cytometry is a technique whereby cellular components and states can be measured directly in whole cells using fluorescence techniques. By nature, flow cytometry consists of multi-dimensional data which is traditionally broken down into two-dimensional bivariate plots. While flow cytometry has been applied to plant science since its inception, its use is still not as widespread as that in animal science. This talk aims at introducing flow cytometry and its applications routinely used in plant sciences.
- Dr. Evangelia Petsalaki (EMBL-EBI, UK), 10.09.2020, 16:00-17:00, Online via Zoom und YouTube (+ Möglichkeit im Anschluss mit der Expertin zu sprechen): https://youtu.be/Kmx69gBAD9g, "Integrative studies of context-specific signalling"
Our group aims to understand and describe the organisation principles of cell signalling that allow the diverse and context-specific cell responses and phenotypes. It is well established that signalling responses happen through complex networks. However, most signalling research still uses linear pathways as the ground truth. Moreover, signalling responses are highly dependent on context, such as tissue type, genetic background etc and therefore these static pathways are not always suitable. There is also a high bias in the literature towards kinases and pathways for which reagents and prior knowledge is readily available. This leaves a huge dark space in our understanding of cell signalling and significantly hinders studies of its general principles. Data-driven methods to study cell signalling using phosphoproteomics have typically been developed on transcriptomics data. Due to fundamental differences of these data types, including the sparsity of phosphoproteomics, these methods perform poorly on signalling network inference. Finally, large-scale essentiality datasets that can shed light on the context in which different signalling-related genes and pathways are crucial have thus far been severely underused in largescale omics data integrative efforts, likely due to their inaccessibility to non-expert users.
In this talk I will present two projects where we try to mitigate some of the above issues. For the first one I will present a method for data-driven machine-learning-based approach that takes advantage of global phosphoproteomics datasets to predict kinase regulatory networks, including their direction and sign of regulation. Our predicted kinase regulatory network is able to recapitulate known signalling pathways and agrees with orthogonal validation datasets. We were able to provide predictions for a large fraction of the understudied kinase space and found that kinase regulatory networks are denser than previously suspected.
For the second one I will present CEN-tools, an integrative webserver and python package, that allows users to navigate the contexts of different gene essentialities. I will demonstrate examples of its use in discovering new gene-gene relationships and important putative signalling targets for different cancers.
- Natalie Krieger, Sven zur Oven-Krockhaus, 04.06.2020, 16:00-17:00, Online via Zoom und YouTube (+ Möglichkeit im Anschluss mit den Experten zu sprechen), "Application of optical microscopy methods in plant biology"
The visibility of organs, cells, subcellular structures, or even the subcellular localization of proteins is becoming more and more important in cell biology and can be achieved by the use of different microscopes and microscopy techniques. In contrast to mammalian cells, living plant cells possess subcellular structures causing strong autofluorescence background and make the spectral separation of the fluorophore of interest challenging.
To analyze the variety of plant samples, there are, in addition to “common” widefield microscopes, three confocal laser scanning microscopes available at the Centre for Molecular Biology of Plants (ZMBP) at the University of Tübingen. These confocal laser scanning microscopes (CLSM) allow higher resolution not only of fixed but also of living cells. Equipped with add-ons, these CLSM allow a subset of spectro-microscopy techniques based on fluorescence to analyze the interaction behavior of proteins/molecules in living plants. Furthermore, the usage of deconvolution software and/or specialized detectors allow the visualization of samples beyond the resolution limit.
In addition to these commercial instruments, a self-built microscope for special techniques is also available, which was designed and built in cooperation with the Institute for Theoretical and Physical Chemistry. On the one hand, this has the advantage that new or modified techniques can be quickly tested and evaluated, and on the other hand, the microscopy methods can be adapted to the specific requirements of plant samples.
Our research with this instrument currently focuses on the use of so-called super-resolution techniques to visualize structures beyond the optical diffraction limit (200 nm). In particular, we are investigating the dynamics of receptor complexes in living plant membranes, which will be demonstrated with a few examples.
The evaluation of the large data sets generated by super-resolution techniques and the optimization of measurement parameters can significantly benefit from the use of modern computational strategies, including deep-learning-based frameworks and machine learning. This synergy will likely play an increasingly important role not only here, but in all biomedical imaging procedures in the future.
- Jerven Bolleman, 20.05.2020, 15:00-16:00, Online via Zoom und YouTube (+ Möglichkeit im Anschluss mit dem Experten zu sprechen): https://youtu.be/zfBhRqEr1h8, "Interoperabilty in the sciences: it is in the semantics"
FAIR you should have heard of it by now ;) Findable, Accessible, Interoperable and Reusable. Lofty goals, which sound grandiose, but are actually grounded in technical realities. The SIB Swiss Institute of Bioinformatics, as a confederation of independent research and service groups covering wide areas of interests, is always trying to meet those goals. The pragmatic bottom up choice by a number of SIB groups is to use RDF and SPARQL to achieve more Interoperable and Reusable data resources. RDF is a knowledge representation framework, while SPARQL is a graph query language that allows for federated querying. There will be a demonstration using Rhea, Hamap and UniProt as examples of how this plays out in practice. With a small hint on how these resources are interoperable with data sources outside of the life sciences such as the European Patent Office datasets.Many of the technologies we choose are great at answering certain questions, yet are extremely inflexible. Leading to high (re)development costs and too little data integration. This is not required and we can do better together. An example is SpOdgi, which takes a highly compressed Odgi genome variation graph and presents it as a SPARQL/rdf graph database in less than 900 lines of code. SpOdgi is used to link 1000+ SARS-CoV-2 genomes with UniProt, Bgee, Oma, Rhea, and NeXtProt into one knowledge graph at https://covid-19-sparql.expasy.org/.
- Dr. Benjamin Schubert, 06.02.2020, 16:00-17:00, Hörsaal: 7E02, Auf der Morgenstelle 16, 72076 Tübingen
"Computer-aided Vaccine Design: How Algorithms can help during the Selection and Assembly of Epitope-based Vaccines"
- Dr. Markus List, 12.12.2019, 16:00-17:00, Hörsaal: N2, Auf der Morgenstelle 16, 72076 Tübingen
"Analysing large-scale epigenomic data"
Large amounts of epigenomic data are publicly available, yet their retrieval for downstream analysis is a research bottleneck. Typically, users download huge files that span the entire genome, even if they are only interested in a small subset (e.g. promoter regions) or an aggregation thereof. Moreover, complex operations on genome-level data are not always feasible on a local computer due to resource limitations. The DeepBlue Epigenomic Data Server mitigates this issue by providing a powerful programmatic API that can be accessed by data analysts and software tool developers. To optimally support biomedical researchers, we also provide DeepBlueR, an extensively documented Bioconductor R package and present typical analysis work-flows. While a majority of DNA methylation based studies focus on average methylation levels, we also show that bisulfite sequencing data uniquely enables us to capture intra-sample DNA methylation heterogeneity.
Dr. Angel Angelov, 14.11.2019, 16:00-17:00, Hörsaal N2, Auf der Morgenstelle 16, 72076 Tübingen
"Basecaller accuracy in Nanopore sequencing"
In Nanopore sequencing, basecalling is the computational process of translating raw electrical signal (squiggle) to nucleotide sequence. Basecalling is therefore a key factor determining usability and acceptance of Nanopore sequencing in the scientific community. Oxford Nanopore Technologies (ONT) have recently released a new basecalling algorithm (called flip-flop), which promised a much better read accuracy than the default one.
In this talk, I will present a comparison of the basecalling accuracy of the default and the flip-flop algorithms, using a real-life bacterial genome sequencing dataset from our lab. The implications of using better basecallers on the accuracy of bacterial genome assemblies will be discussed, as well as the perspectives of using Nanopore-only data to obtain near-complete, error-free bacterial whole genome sequences.
Prof. Dr. Chris-Carolin Schön, 24.01.2019, 16:15-17:15, Hörsaal 7E02, Auf der Morgenstelle 16, 72076 Tübingen
"Accuracy of genomic prediction in structured plant populations"
Most traits of importance in plant and livestock breeding are regulated by many genes and follow a quantitative distribution. The assessment of these quantitative traits in performance tests is costly and time consuming. Thus, prediction of the genetic potential of individuals from their DNA sequence is highly desirable.
This talk will present advancements in genome-based prediction using genomic, phenotypic and genealogical data. Statistical models for prediction of genetic values and phenotypes will be introduced. I will discuss challenges arising from the high- dimensional nature of genomic information as well as from the large genetic diversity and genome complexity of maize. The effect of integrating knowledge on marker-trait associations from functional or QTL studies and the role of feature selection for improving prediction accuracy will be shown. Factors driving prediction accuracy besides ancestral relatedness will be presented for traits of different genetic architecture. How to make best use of the massive collections of genomic and phenotypic data not only for prediction of phenotypes but also to understand the genetic architecture of complex traits and genetic phenomena such as epistasis and pleiotropy will be discussed.
Dr. Phil Ewels, 08.03.2018, 16:00-17:00, Hörsaal: N2, Auf der Morgenstelle 16, 72076 Tübingen
“Standardising Swedish genomics analyses at SciLifeLab NGI”
The SciLifeLab National Genomics Infrastructure is one of the largest sequencing facilities in Europe. We are an accredited facility providing library preparation, sequencing, basic analysis and quality control for Swedish research groups. Our sample throughput requires a highly automated and robust bioinformatics platform.
In this talk I will describe how we handle data flow, analysis and quality control. I'll describe our work with tools such as Nextflow and MultiQC; some of our pipeline projects for Cancer analysis and RNA; and touch on some upcoming projects such as MegaQC and nf-core.
Holger Gantikow, 20.09.2018, 16:00-17:00, Seminarraum D4A19, D-Bau, Auf der Morgenstelle 16, 72076 Tübingen
“Linux Containers for HPC - Container Technology/Engine Architecture 101”
Container technology has not only spread in "normal" IT in recent years, but has also established itself as an important basic component in the field of High Performance Computing (HPC). However, here Docker is not the omnipresent solution, but there are a number of alternatives that can often be better integrated into existing HPC environments. The presentation will introduce which problems of HPC can be mitigated with containers and will refresh which technologies are based on containers.
Dr. Thomas Soddemann, 25.02.2016, 16:00-17:00, Hörsaal: N1, Auf der Morgenstelle 16, 72076 Tübingen
“Compute Intensive Applications: fast everywhere” (Fraunhofer Institute for Algorithms and Scientific Computing SCAI)
Dr. Dieter Beule, 03.03.2016, 16:00-17:00, Hörsaal: N1, Auf der Morgenstelle 16, 72076 Tübingen
"Tools Selection for Neo-Epitope Selection" (Berlin Institute of Health)
Prof. Dr. Frank Oliver Glöckner, 17.08.2016, 11:00-13:00, Hörsaal: N1, Auf der Morgenstelle 16, 72076 Tübingen
"The German Federation for Biological Data (GFBio): Services for Data Management and Long-term Archiving” (Head of the Microbial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Jacobs University Bremen gGmbH)
Prof. Guowang Xu, 09.04.2015, 16:00-17:00, Hörsaal: N1, Auf der Morgenstelle 16, 72076 Tübingen
" Liquid chromatography – mass spectrometry methods for lipidomics” (Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China)
Dr. Nico Pfeiffer, 30.06.2015, 16:00-17:00, Hörsaal: N1, Auf der Morgenstelle 16, 72076 Tübingen
“Machine Learning for Personalized Medicine: How to Integrate and Interpret Data from Different Molecular Measurements” (MPI für Informatik, Saarbrücken)
Dr. Anne Zeck, 12.11.2015, 16:00-17:00, Hörsaal: N1, Auf der Morgenstelle 16, 72076 Tübingen
"Mass spectrometry of intact proteins for quality control and biological function elucidation” (Natural and Medical Sciences Institute (NMI), Reutlingen)
Dr. Thomas Joos, 01.01.2014, 16:00-17:00, Lecture hall: N2, Auf der Morgenstelle 16, 72076 Tübingen
"Immunoassays in Multiplex for Biomarker Discovery and Validation” (Deputy Managing Director, NMI Natural and Medical Sciences Institute at the University of Tübingen)
Prof. Rainer Bischoff, 22.05.2014, 16:00-17:00, Lecture hall: N1, Auf der Morgenstelle 16, 72076 Tübingen
“Biomarker Discovery and Validation” (University of Groningen, Department of Pharmacy, Analytical Biochemistry, 9713 AV Groningen, The Netherlands)
Prof. Dr. Christoph Borchers, 15.08.2014, 16:00-17:00, NMI Reutlingen, Markwiesenstraße 55, 72770 Reutlingen
"MRM and iMALDI, two emerging mass spectrometric approaches for biomedical and clinical research“ (University of Victoria, BC, Canada)
Dr. Oliver Schilling, 11.12.2014, 16:00-17:00, Lecture hall: N1, Auf der Morgenstelle 16, 72076 Tübingen
"How to use proteomics to investigate protease substrates and specificity"
& Dr. Lars Nilse: “Reliable Peptide Quantification” (Institute of Molecular Medicine and Cell Research, University of Freiburg)
- Prof. Detlef Weigel, 18.04.2013, 16:00-17:00, Lecture hall: N1, Auf der Morgenstelle 16, 72076 Tübingen, "Next generation genetics enabled by high-throughput sequencing technologies" (Max-Planck-Institute for Developmental Biology, Tübingen)
Prof. Boris Macek, 25.04.2013, 16:00-17:00, Lecture hall: N1, Auf der Morgenstelle 16, 72076 Tübingen, "Global detection of kinase substrates using quantitative mass spectrometry-based proteomics" (Proteome Center Tübingen, University of Tübingen)
Prof. Peter Bauer, 02.05.2013, 16:00-17:00, Lecture hall: N2, Auf der Morgenstelle 16, 72076 Tübingen
“Next generation sequencing goes diagnostics" (Department of Medical Genetics, University of Tübingen)
Prof. Olaf Riess
Prof. Dr. Marius Ueffing, 06.06.2013, 16:00-17:00, Lecture hall: N2, Auf der Morgenstelle 16, 72076 Tübingen, "Molecular dissection and scaled integration of functional protein networks: analyzing the molecular basis of human vision” (Institute for Ophtalmic Research, University of Tübingen)
Dr. Marc Stahl, 04.07.2013, 16:00-17:00, Lecture hall: N1, Auf der Morgenstelle 16, 72076 Tübingen, "Metabolomics: What we can do – what we cannot do” (Center for Plant Molecular Biology, University of Tübingen)
Prof. Peter Martus, 25.07.2013, 16:00-17:00, Lecture hall: N1, Auf der Morgenstelle 16, 72076 Tübingen, “Multiple Imputation of Missing Values – strategies and pitfalls” (Institute for Clinical Epidemiology and Applied Biometry, University Hospital Tübingen)
Dr. Oliver Poetz, 24.10.2013, 16:00-17:00, Lecture hall: N1, Auf der Morgenstelle 16, 72076 Tübingen, "Trap & Measure: Immunoassays In Targeted and Non-Targeted Proteomics” (NMI Natural and Medical Sciences Institute at the University of Tübingen)
Dr. Dr. Saskia Biskup, 07.11.2013, 16:00-17:00, Lecture hall: N1, Auf der Morgenstelle 16, 72076 Tübingen, "Next generation sequencing in human genetic diagnostics” (CeGaT Tübingen)
Der Workshop wird Vorträge und relevante Anwendungen in dem Bereich “Bioinformatik in der Krebsforschung” beinhalten. Mehrere ausgewählte Gastredner sowie Experten aus dem QBiC Team werden Ihre Arbeit und Forschung aus unterschiedlichen Bereichen der Krebsforschung vorstellen.
26.11.2020, 10 - 16 Uhr via Zoom
PD Dr. med. Juliane Walz (KKE Translationale Immunologie)
Dr. med. Markus Löffler (Department of General, Visceral and Transplant Surgery)
Dr. Nicolas Casadei (Institute of Medical Genetics and Applied Genomics)
Dr. Andre F. Martins (Werner Siemens Imaging Center)
The aim of this workshop is to present a wide range of novel bioinformatics methodologies in cancer research and to create a platform to interact with experts in their respective fields.
Major breakthroughs in this field during the past decade have revolutionized cancer research, allowing omics and biomedical imaging data to be generated at ever increasing volumes and resolution. The challenge however is the increasing complexity in handling the resulting datasets which can be large and highly heterogeneous.
We will introduce computational methods for management, processing and analysis of complex omics and imaging data that are currently available at the Tübingen Life Science Campus and discuss how they are applied to cancer research.
|10:00-10:10||Dr. Sven Nahnsen||Einführung in den Workshop|
|10:10-10:45||PD Dr. med. Juliane Walz|| |
Peptide-based Immunotherapy - Antigens, Combinations, Translation
|10:50-11:25||Dr. med. Markus Löffler||Cancer Immunotherapy: From Standard Therapy to Individualized Medicine|
|13:15-13:50||Friederike Hanssen||Bioinformatics analyses with portable and reproducible pipelines on public datasets|
|13:50-14:25||Dr. Nicolas Casadei|| |
Overview of methods used in molecular oncology
(DNA & RNA sequencing, ATAC-seq, bulk vs single-cell, methylation, short vs long reads).
|14:40-15:15||Dr. Andre F. Martins||Imaging tissue physiology with non-invasive metabolic approaches|
|15:15-15:45||Dr. Luis Kuhn||Semantic segmentation for medical image analysis|
|15:45-15:50||Dr. Sven Nahnsen||Schlusswort|
|15:50-16:30||Post Workshop Breakout-Sessions: |
Die Entwicklung von reproduzierbaren, portierfähigen Workflows spielt in der alltäglichen Datenanalyse am QBiC eine immer stärkere Rolle. Die technologischen Anforderungen an solche Workflows und Analysemethoden spiegeln sich hierbei in der Entwicklung von diversen domänenspezifischen Sprachen wieder, die vermehrt in der wissenschaftlichen Analyse von großen Daten eine Rolle spielen. Am QBiC wurde hierfür auf die Plattform Nextflow gesetzt, die in Kombination mit anderen Projekten wie nf-core zu deutlich verbesserten Workflows und einer Standardisierung im Bereich Pipeline Entwicklung geführt hat.
Um dieser Entwicklung weiteren Nachklang zu verleihen, wurde ein Workshop mit den Kernentwicklern von Nextflow (Paolo Di Tommaso und Evan Floden) initiiert, der Anfängern, aber auch Fortgeschrittenen Usern die Möglichkeit bieten soll, selbst mit Nextflow Projekte und Pipelineentwicklungen anzustoßen.
Der Workshop wird am 11./12. April 2019 jeweils ganztags in Räumlichkeiten des QBiC an der Universitätsklinik Tübingen stattfinden. Eine genauere Agenda, Anmeldeformular und weitere Punkte werden in den nächsten Tagen über verschiedene Kommunikationskanäle verbreitet.
Die Registrierung ist seit 29. März geschlossen. Es wird eine Rechnung mit der Instituts/Firmenadresse generiert, welche bis zum 15. April 2019 bezahlt werden muss um eine erfolgreiche Registrierung vorzuweisen. First come, first serve.
Der Workshop findet Donnerstag und Freitag an der Universitätsklinik in Tübingen statt. Die Registrierungsgebühr enthält Verpflegung während des Workshops und die Gebühren für die Tutorials während des Workshops.
Die unten definierte Agenda wird noch detaillierter hier publiziert und von Zeit zu Zeit aktualisiert. Es wird außerdem ein Social Event am Donnerstag, 11. April nach dem Workshop geben - bitte zeitlich also auch einplanen!
Donnerstag, 11. April
Gemeinsames Abendessen um 19 Uhr im Restaurant Neckarmüller.
Freitag, 12. April