• Dr. Evangelina López de Maturana and Dr. Sergio Sabroso, 31.05.2022, 16:00-17:00, Online via Zoom und
  Youtube, “Integration of multi-omics and non-omics data: AI approaches and challenges”

  Zusammenfassung:
  During the last years, a plethora of cancer-specific data generated by high-throughput biotechnological platforms along with clinico-pathological information are being generated and available on-line, facilitating the investigation of cancer biology or classification of patients, among other aims. However, the majority of the studies have used unimodal approaches, missing the opportunity of integrating data of different nature.

  Motivations for data integration include extracting knowledge for various purposes by exploiting the complementary nature of multimodal data, obtaining a more unified and global view of the systems and improving decision-making. However, integrating multimodal data is a challenging task because the data generation processes may be heterogenous, there may be numerous underlying processes depending on unavailable variables and the sparsity of most medical datasets.
  In this webinar, we will discuss modern machine learning techniques to integrate omics (genomics, transcriptomics, radiomics and pathomics) and non-omics (demographical and clinico-pathological) data to improve PC patient diagnosis, prognosis stratification and treatment response dealing with the aforementioned challenges.

• Prof. Dr. Giovanni Maglia, 22.03.2022, 16:00-17:00, Online via Zoom und
  Youtube, “Single-molecule nanopore proteomics”

  Zusammenfassung:
  Proteins form the basis for life. To understand these biological processes and their (dys)regulation in health and disease it is critical to decode the cellular language of proteins. However, it is now becoming evident that resolving the proteome remains an enormous challenge in biology that might only be solved by developing new technologies capable of single-molecule protein analysis.

  Biological nanopores are single-molecule sensors that are now used to sequence DNA at the single-molecule level. Protein analysis, however, has been challenged by the heterogeneous structure and non-uniform charge of proteins, which complicates their electrophoretic capture and translocation by nanopores. Here, I will show our recent efforts in designing new nanopores to control the unfolded translocation of proteins across a nanopore, and how to engineer nanopores to capture and characterize single proteins or peptides. These nanopores have application in the identification of protein isoforms, and will provide unique tools for single-molecule proteomic analysis.

• Lorenz Lamm, 20.09.2022, 16:00-17:00, Online via Zoom und
  Youtube, “MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms”

  Zusammenfassung:

  Cryo-electron tomography (cryo-ET) enables 3D visualization of the native cellular environment at sub-nanometer resolution. However, cryo-electron tomograms suffer from low signal-to-noise ratios and anisotropic resolution, which makes subsequent image analysis challenging. In particular, the detection of membrane-embedded proteins is a problem still lacking satisfactory solutions: Classical computer vision approaches, like template matching or PySeg either require manual guidance, or can struggle detecting the embedded proteins at all.

  MemBrain is a deep learning-aided pipeline that automatically detects membrane-bound protein complexes in cryo-electron tomograms. To simplify the detection task, MemBrain samples subvolumes along a segmented membrane and aligns them. Thus, while other state-of-the-art DL approaches – e.g., DeepFinder – require large annotated tomogram regions, MemBrain is very annotation-efficient and gives good results with already a single annotated membrane. In addition to protein positions, MemBrain can also estimate particle orientations for certain particle shapes, which is beyond the capabilities of other DL methods for Cryo-ET.

• Prof. Joakim Lundeberg, 23.09.2021, 16:00-17:00, Online via Zoom und
  Youtube, "Exploring the spatial omics landscape in normal tissues and disease"
  
  Zusammenfassung:
  The cell is a fundamental unit of life, yet we know surprisingly little about them. Specific types of cells exist in every organ, and serve specialized functions defined by the specific genes and proteins active in each cell type. Comprehensive maps of molecularly defined human cell types are underway through the Human Cell Atlas (https://www.humancellatlas.org) effort using primarily single cell RNA sequencing as foundation. The technologies to assemble spatial tissue maps of transcription that will describe and define the cellular basis of health and disease is less well clear. We have developed and established the Spatial Transcriptomics technology (aka Visium), in which tissue imaging is merged with spatial RNA sequencing and resolved by computational means. Spatial Transcriptomics technology was the first method to provide unbiased whole transcriptome analysis with spatial information from tissue using barcoded array surfaces and has since the initial publication been used in multiple biological systems in health and disease. Data from our ongoing projects will be presented together with description of future drivers of spatial technologies.
   

• Dr. Malte Luecken, 09.09.2021, 16:00-17:00, Online via Zoom und
  Youtube, "Integrating diverse single-cell datasets: a consistent view of the healthy human lung"
  
  Zusammenfassung:
  The human cell atlas consortium has promoted the generation of single-cell atlases of healthy human tissues. Each generated dataset represents a particular view of a tissue with its own share of signal and bias. The true distribution of healthy tissue cells can only be approximated by combining the available datasets. However, complex batch effects between individual datasets complicate the generation of a joint representation of the data. Thus, data integration has been named as a "grand challenge" of single-cell research. In this talk, I will show how data integration methods can be used to generate a consistent representation of a healthy tissue using the human lung as an example. Building upon our benchmark of data integration methods, we have built the first version of the integrated human lung cell atlas. I will showcase how this atlas consolidates cellular identity, identifies areas of further research, and is a powerful tool for the analysis of new lung data by acting as a reference.
   

• Dr. Venera Weinhardt, 06.05.2021, 16:00-17:00, Online via Zoom und
  Youtube, "Correlated light and soft x-ray microscopy of single cells"
  
  Zusammenfassung:
  The structure of a cell is crucial to its identity, function and health. A great deal has been learned about how a cell's structure arises from light and electron microscopy. Yet despite advances of these imaging techniques for volumetric imaging, we still have only a limited ability to observe structural changes of individual whole cells at statistically significant sample sizes. Our group and other labs are attempting to overcome these limitations through the development of a single-cell 3D imaging technique – called soft x-ray tomography – which can examine intact cells without labelling or fixation, at high throughput and spatial resolution. As the most comprehensive view of a cell is unlikely to come from a single microscope, I will talk on advances of soft x-ray microscopy and the effort to combine it with cryogenic fluorescence microscopy to obtain physiological and morphological data that more accurately reflect the complexities of single cells.
   

• Olga Botvinnik, PhD, 18.03.2021, 16:00-17:00, Online via Zoom und Youtube, "Single-cell transcriptomics for the 99.9% of species without reference genomes"
  
  Zusammenfassung:
  Single-cell RNA-seq (scRNA-seq) is a powerful tool for cell type identification but is not readily applicable to organisms without well-annotated reference genomes. Of the approximately 10 million animal species predicted to exist on earth, >99.9% do not have any submitted genome assembly. To enable scRNA-seq for the vast majority of animals on the planet, here we introduce the concept of “k-mer homology,” implemented as the Nextflow pipeline kmermaid, to directly detect orthologous genes from transcriptomic data without the need for a reference genome. Underpinning kmermaid is the tool orpheum, a memory-efficient method for extracting high-confidence protein-coding sequences from RNA-seq data. To represent fragments of orthologous genes, k-long amino acid words or kaa-mers are subsampled from the protein sequences. To retain evolutionarily conserved biochemical properties, kaa-mers are converted into degenerate amino acid alphabets, representing each cell in a common feature space of amino acid words, allowing for direct comparison across millions of years of evolution. After validating kmermaid between human and mouse lung, we applied kmermaid to the Chinese horseshoe bat (Rhinolophus sinicus), where we detected granzymes and other immune genes not present in the bat genome assembly. By enabling analyses across divergent species’ transcriptomes in a genome-, gene annotation-, and orthology-agnostic manner, kmermaid and orpheum can identify cell type-specific sequences that may be missing from genome annotations and empower molecular cellular phenotyping for novel model organism species.
   

• Dr. Radovan Bast, 28.10.2021, 16:00-17:00, Online via Zoom und
  Youtube, “Next steps to FAIRifying coding in an academic setting”

  Zusammenfassung:
  CodeRefinery is a project which supports students and researchers across all academic disciplines by advancing the FAIRness of research software and development practices so that they can collaboratively develop, review, discuss, test, share, and reuse their codes. This initiative teaches which software is needed but is often missed in a traditional university curriculum. Thus far, over 1500 researchers from the Nordic countries have been trained, and with the received follow-up funding, the plan is to turn this project into a sustainable organization reaching beyond the Nordics. During this presentation, he will touch upon the knowledge that was acquired as well as the focus of the collaborative co-teaching approach to reach 100 participants per event. Furthermore, the plans of the CodeRefinery project and its support will be discussed.

• Dr. Gary Bader, 05.11.2020, 15:00-16:00, Online via Zoom und Youtube, "Systems biology from single cell genomics to network data integration"
  
  Zusammenfassung:
  Topic 1 
  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.
   
  Topic 2
  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, "Using flow cytometry in plant science: advantages and limitations"
  
  Zusammenfassung:
  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, "Integrative studies of context-specific signalling"
  
  Zusammenfassung:
  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, "Application of optical microscopy methods in plant biology"
  
  Zusammenfassung:
  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, "Interoperabilty in the sciences: it is in the semantics"
  
  Zusammenfassung:
  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"
  
  Zusammenfassung:
  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"
  
  Zusammenfassung:
  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"
  
  Zusammenfassung:
  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”

  Zusammenfassung:
  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”
  
  Zusammenfassung:
  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)

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.

Registrierung

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.

Agenda

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