KoBIS Termine

KoBIS März 2018

6.03.2018 um 16:15 in Raum C10.3.36, THM Gießen

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  • "Hybrid assembly of the Chinese Hamster genome"

    2018-03-06-Rupp_thumb.JPG

    Technical developments in next-generation sequencing as well as decreasing prices have enabled various sequencing projects including the large genomes of higher eukaryotes. The two most popular and efficient methods for whole-genome sequencing are the short-read technology from Illumina and recently also the single-molecule real-time (SMRT) long-read sequencing technique from Pacific Biosciences. As a result, for some organisms numerous and diverse data sources are available. Combining these data into one high quality assembly still is a challenging task.
    For the Chinese hamster, Cricetulus griseus, data is available from (i) Illumina short-read data from whole-genome libraries (including mate-pair libraries from varying insert sizes), (ii) Illumina short-read data from libraries of separated chromosomes, and (iii) long-read data generated with PacBio's sequencing technology. In order to get a genome assembly as good as possible, we compared and rated different assembly strategies taking into account technical conditions such as computing power needed and total computing times. For this comparison, we used four different de novo assemblies and further on combined these assemblies in different orders to form meta-assemblies.

    Dipl.-Inf. Oliver Rupp, AG Bioinformatik und Systembiologie, Justus-Liebig-Universität Gießen

KoBIS Februar 2018

06.02.2018 um 16:15 in Raum C10.3.36, THM Gießen

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  • "I dream of Julia"

    Wir stellen den neuen Stern an Himmel der Datenanalysesprachen vor, der angetreten ist, Matlab, R, Python & Co. in den Ruhestand zu schicken.
    Ob er das Zeug dazu hat, kann jeder nach dem Vortrag für sich entscheiden.

    Prof. Dr. Andreas Dominik, Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

KoBIS Dezember 2017

6.12.2017 um 16:15 in Raum B10.2.23/24, THM Gießen

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  • "Bioinformatics for Medical Big Data Analyses"

    2017-12-Heider_thumb.JPG

    Our current decade is strongly associated with the term Big Data, which attracts attention in many different fields, e.g., genomics, meteorology, or complex physical simulations. For instance, next-generation sequencing technologies are able to generate millions or billions of reads, which enabled completely new ways for precision medicine, in particular by using statistical and machine learning approaches. However, many new problems arise due to the huge amount of data generated. Computational methods needs to be adapted to address these big data issues and to path the way for precision medicine. As an example, recent developments in HIV diagnostics and therapy will be shown and discussed.

    Prof. Dr. Dominik Heider, Heiderlab, Mathematik und Informatik, Philipps-Universität Marburg, Marburg

  • "SCOTCH: Subtype A Coreceptor Tropism Classification in HIV-1"

    2017-12-Loechel-01_thumb.JPG2017-12-Loechel-02_thumb.JPG

    The V3 loop of the gp120 glycoprotein of the Human Immunodeficiency Virus 1 (HIV-1) is considered to be responsible for viral coreceptor tropism. gp120 interacts with the CD4 receptor of the host cell and subsequently V3 binds either CCR5 or CXCR4. Due to the fact that the CCR5 coreceptor is targeted by entry inhibitors, a reliable prediction of the coreceptor usage of HIV-1 is of great interest for antiretroviral therapy. Although several methods for the prediction of coreceptor tropism are available, almost all of them have been developed based on only subtype B sequences, and it has been shown in several studies that the prediction of non-B sequences, in particular subtype A sequences, are less reliable. Thus, the aim of the current study was to develop a reliable prediction model for subtype A viruses. Our new model SCOTCH is based on a stacking approach of classifier ensembles and shows a significantly better performance for subtype A sequences compared to other available models. In particular for low false positive rates (between 0.05 and 0.2, i.e., recommendation in the German and European Guidelines for tropism prediction), SCOTCH shows significantly better prediction performances in terms of partial area under the curves and diagnostic odds ratios compared to existing tools, and thus can be used to reliably predict coreceptor tropism for subtype A sequences.

    Hannah Franziska Löchel, M.Sc., Heiderlab, Mathematik und Informatik, Philipps-Universität Marburg, Marburg

KoBIS November 2017

7.11.2017 um 16:15 in Raum C10.3.36, THM Gießen

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  • "UROPA_GUI: a tool for Universal RObust Peak Annotation"

    2017-11-Fazelzadeh_thumb.JPG

    The annotation of genomic ranges such as peaks resulting from ChIP-seq/ATAC-seq or other experimental techniques represents a fundamental task for bioinformatics analysis with crucial impact on many downstream analyses. In our previous work, we introduced the Universal Robust Peak Annotator (UROPA), a flexible command line based tool which considerably extends the functionality of existing annotation software. In order to reduce the complexity for biologists and clinicians, we have implemented an intuitive web-based graphical user interface (GUI) for UROPA. This extension will empower all users to generate dynamic and specific annotation for a wide range of experimental setups considering reference feature type, position, orientation, and precedence.

    Parastoo Fazelzadeh, PhD, Max-Planck-Institut für Herz- und Lungenforschung, Bad Nauheim

  • "De novo transcriptome assembly and training of support vector machine classifiers for prediction of antimicrobial peptides"

    2017-11-Tann_thumb.JPG

    Mis- and overuse of antibiotics as well as the "new antibiotic paradoxon" have been leading to an increase in bacterial restistance against antibiotics and stagnation in new antibiotic development. An alternative to those classical antibiotics are antimicrobial peptides (AMPs). They are highly specific for prokaryotic cells and co-evolving in nature apart from synthetical derivates. With machine learning methods, it is possible to predict functional sequences. This was performed by support vector machines with preliminary principle component analysis. In parallel, a de novo transcriptome assembly was done for healthy and infected groups of Hirudo verbana (leech) with the goal to find new AMPs via comparative transcriptome analysis.

    Fabian Tann, M.Sc., Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

KoBIS Oktober 2017

10.10.2017 um 16:15 in Raum C10.3.36, THM Gießen

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  • "Algorithmische Lösungen für den Umgang mit Big Data und Tiniest Devices"

    2017-10-10-Kammer_thumb.jpg

    Aufgrund von Big Data mit beispielsweise Anwendungen in der Bioinformatik sowie der Verwendung von kleinsten Geräten mit einem kleinen Speicher gibt es ein verstärktes Interesse an der Entwicklung platzeffizienter Algorithmen, d.h. Algorithmen mit einem Arbeitsplatzverbrauch, welcher kleiner ist als der der Standardalgorithmen für das betrachtete Problem. Der Vortrag gibt eine Übersicht der existierenden platzeffizienten Algorithmen für grundlegende Graphenprobleme und zeigt anhand des Beispiels von Tiefensuche wie Algorithmen platzeffizient gemacht werden können ohne dass dies zu wirklichen Laufzeiteinbußen führen muss.

    Dr. habil. Frank Kammer, Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

KoBIS Juni 2017

13.06.2017 um 16:15 in Raum C10.8.01, THM Gießen

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  • "Bioinformatik als Data Science: Wie quetscht man das Wissen aus den Daten?"

    2017-06-13-Gogol-Doering-diagram_thumb.png

    Wer als Biologie, Ökologe oder Mediziner mehr über das Leben und die in ihm wirkenden Mechanismen herausfinden will, ist als empirischer Forscher natürlich auf Daten angewiesen. Mit neuen Methoden zur Datenerhebung ist es inzwischen möglich, schnell und kostengünstig große Mengen an Daten zu erheben. Beispielsweise produziert ein einzelner moderner DNA-Sequenzierer pro Woche das Hundertfache der Datenmenge, die man zur Jahrtausendwende zur Entschlüsselung des menschlichen Genoms gesammelt hatte, damals noch mit großem Aufwand und nach jahrelanger Arbeit. Während es also in weiten Bereichen der Biowissenschaften immer leichter wird, Daten in ausreichender Menge zu sammeln, wird es gleichzeitig immer schwerer, diese Daten angemessen auszuwerten. Die seit Jahrzehnten exponentiell anwachsende Datenflut stellt die Bioinformatik vor eine große Herausforderung. In diesem Vortrag werde ich anhand von Beispielen aus meinen Forschungsprojekten der letzten Jahre zeigen, wie die angewandte Bioinformatik durch Kombination unterschiedlicher Daten versucht, neue Einblicke in biologische Prozesse zu gewinnen.

    Dieser KoBIS-Vortrag ist gleichzeitig Teil der Ringvorlesung des Masterstudiengangs „Bioinformatik und Systembiologie“. Studierende der Bioinformatikstudiengänge erhalten einen Einblick in mögliche Themen für Laborpraktika oder Abschlussarbeiten.

    Prof. Dr. Andreas Gogol-Döring, Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

KoBIS Mai 2017

02.05.2017 um 16:15 in Raum C10.3.36, THM Gießen

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  • "TAPscan: Accurate genome-wide gene family annotation and web representation"

    2017-05-02-Muehlich_thumb.JPG2017-05-02-Wilhemsson_thumb.JPG

    Transcription associated proteins (TAPs) are major players in gene regulatory networks and contribute to increasing the potential complexity of gene network circuitry. We have updated the workflow established by Lang et al. (2010) consisting of a set of domain-based classification rules aimed to identify plant transcription factors (TFs), transcriptional regulators (TRs) and putative (in silico predicted) TAPs (PTs) amongst a given set of proteins. With a combination of custom built and existing Hidden Markov Model (HMM) domain profiles a total of 122 TAP families can now be distinguished. We have also developed a web interface that can be used to browse through and download our results. The setup of the web interface has been designed with the aims to maximize applicability and alleviate maintenance.

    Cornelia Mühlich, M. Sc. u. Per Wilhemsson, M.Sc., Rensing lab, Zellbiologie, Philipps-Universität Marburg, Marburg

KoBIS Februar 2017

07.02.2017 um 16:15 in Raum B10.2.23/24, THM Gießen

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  • "Using self-organising maps to reveal cell-to-cell heterogeneity in populations of single cells"

    2017-02-Preussner-01_thumb.JPG2017-02-Preussner-02_thumb.JPG

    Jens Preußner, M.Sc., Max-Planck-Institut für Herz- und Lungenforschung, Bad Nauheim

  • "Genome wide investigation of chromatin accessibility via NGS"

    2017-02-Looso_thumb.JPG

    Dr. Mario Looso, Max-Planck-Institut für Herz- und Lungenforschung, Bad Nauheim

KoBIS Dezember 2016

06.12.2016 um 16:15 in Raum C10.3.36, THM Gießen

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  • "Stable Isotope Tracing Models to understand elemental cycling in Terrestrial ecosystems"

    2016-12-Mueller_thumb.jpg

    Gross elemental transformation rates in terrestrial ecosystems are typically quantified via stable isotope tracing techniques. Basics of stable isotope chemistry and the development of suitable numerical stable isotope models are a prerequisite for successful quantification. In this presentation development of a suitable numerical tracing techniques will be presented and applications in MatLab and Simulink will be discussed.

    Prof. PhD Christoph Müller, Institut für Pflanzenökologie, Justus-Liebig-Universität Gießen

KoBIS November 2016

01.11.2016 um 14:15 in Raum C.3.34, THM Gießen

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  • "Classification of Normal/Abnormal Heart Sound Recording: the PhysioNet/Computing in Cardiology Challenge 2016"

    2016-11-Dominik_thumb.JPG

    The 2016 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify heart sound recordings collected from a variety of clinical or nonclinical (such as in-home visits) environments. The aim is to identify, from a single short recording (10-60s) from a single precordial location, whether the subject of the recording should be referred on for an expert diagnosis.

    Prof. Dr. Andreas Dominik, Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

  • "Recognition of Abnormalities in Heart Sound Recordings by Reconstruction of Idealised Beats"

    We present algorithms to distinguish between healthy and diseased condition of the heart, based on the analysis of phonocardiograms. The software tries to mimic the decision-making process of a cardiologist by identifying heart beats (S1 and S2), finding extra sounds and murmurs while ignoring all kinds of artefacts and noise.
    Two different solutions have been submitted to the PhysioNet Challenge 2016: The entry for phase I aims to reconstruct the signal of an ideal heartbeat by calculating the median of an overlay of all beats of a recording. An LVQ-classifier, trained with the ideal beat of 3240 PCGs of the challenge training set, achieved a specificity of 0.85 and a sensitivity of 0.40, resulting in a total score of 0.63.
    Our entry for the official phase of the challenge searches for abnormalities in every single beat of a PCG. The results display a sensitivity of 0.91, a specificity of 0.29, and a total score of 0.60.

    Simon Hofmann, M.Sc., Fachbereich GES, Technische Hochschule Mittelhessen, Gießen

  • "Using Deep Gated RNN with a Convolutional Front End for End-to-End Classification of Heart Sound"

    2016-11-Thomae-01_thumb.JPG2016-11-Thomae-02_thumb.JPG

    Classification of heart sounds of a diverse set of phonocardiograms (PCGs) from different recording settings is the challenging objective of the 2016 PhysioNet Challenge. We suggest an end-to-end deep neural network, which is fed with raw PCGs and which learns to autonomously extract features and to classify the recordings. Our architecture combines convolutional and recurrent layers, followed by an attention mechanism, which weights time steps by importance and a dense multilayer perceptron as classifier.
    Whereas currently trending deep neural networks in speech recognition or computer vision use up to a million of training samples, a restricted set of only 3,153 heart sound recordings is available as training data. We workaround this limitation by artificially increasing the training set by means of augmentation of the raw PCGs using various audio effects.
    Using this moderately sized neural network, we attain high validation scores of 0.89 on validation data; however the resulting scores on the hidden test data of the challenge diverge in range (0.82).

    Christian Thomae, B.Sc., Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

  • "Can Electrocardiogram Classification be Applied to Phonocardiogram Data? – An Analysis Using Recurrent Neural Networks"

    2016-11-Schoelzel-01_thumb.JPG2016-11-Schoelzel-02_thumb.JPG

    Both a Phonocardiogram (PCG) and an Electrocardiogram (ECG) are sequential measurements of heart activity used to distinguish normal from abnormal heart function. Although they measure different physical quantities, we show that training a long short-term memory network on the Physionet challenge using only the ECG data available for the MIT heart sounds database still yields a score of 0.74 compared to the reference score of 0.82 for a similar net trained on the PCG data.
    This finding suggests that it may be valuable to train a transformational neural network to produce an artificial ECG from a PCG. Such a transformational net would allow to harness the know-how of decades of research on ECG classification to improve PCG classification. Unfortunately, this task seems too hard for current state-of-the art architectures for neural networks given the data of the Physionet challenge 2016. However, it may be worthwhile to further pursue this approach using data with less variance in the ECG signals or a specialized network architecture.

    Christopher Schölzel, M.Sc., Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

KoBIS Oktober 2016

04.10.2016 um 14:15 in Raum D12.0.10, THM Gießen

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  • "Using different combinations of Illumina short read data and PacBio long read data for the de novo assembly of the Chinese hamster genome"

    Technical developments in next-generation sequencing as well as decreasing prices have enabled various sequencing projects including the large genomes of higher eukaryotes. The two most popular and efficient methods for whole-genome sequencing are the short-read technology from Illumina and recently also the single-molecule real-time (SMRT) long-read sequencing technique from Pacific Biosciences. As a result, for some organisms numerous and diverse data sources are available. Combining these data into one high quality assembly still is a challenging task.
    For the Chinese hamster, Cricetulus griseus, data is available from (i) Illumina short-read data from whole-genome libraries (including mate-pair libraries from varying insert sizes), (ii) Illumina short-read data from libraries of separated chromosomes, and (iii) long-read data generated with PacBio's sequencing technology. In order to get a genome assembly as good as possible, we compared and rated different assembly strategies taking into account technical conditions such as computing power needed and total computing times. For this comparison, we used four different de novo assemblies and further on combined these assemblies in different orders to form meta-assemblies. The evaluation of the four assemblies and the four meta-assemblies relied on 65 different metrics that include parameters such as the total genome size, the number of contigs and scaffolds, and the correct allocation of the contigs to the single chromosomes. It showed that the meta-assemblies improved the initial assemblies in terms of contig and scaffold length, the N50, the L50 numbers, the gene content, and the sequence quality. Furthermore our results suggest that a meta-assembly that is started from an assembly computed with PacBio data outperforms meta-assemblies that are computed on the basis of Illumina assemblies.

    Dipl.-Inf. Oliver Rupp, AG Bioinformatik und Systembiologie, Justus-Liebig-Universität Gießen

  • "MGX - A flexible metagenome analysis framework"

    The characterization of microbial communities based on sequencing and analysis of their genetic information has become a popular approach also referred to as metagenomics; in particular, the recent advances in sequencing technologies have enabled researchers to study even the most complex communities consisting of thousands of species. Metagenome analysis, the assignment of sequences to taxonomic and functional entities, however, remains a tedious task: large amounts of data need to be processed. There are a number of approaches that aim to solve this problem addressing particular aspects, however, scientific questions are often too specific to be answered by a general-purpose method. For this reason, we developed MGX as an extensible framework for the management and analysis of metagenomic datasets; MGX provides a complete set of workflows required for taxonomic and functional metagenome analysis. In contrast to existing platforms, MGX is easily extendable and allows researchers not only to execute predefined but also custom analysis pipelines within its framework. MGX enables fast adoption of novel algorithms e.g. for the taxonomic classification of metagenomic sequences, thereby offering researchers to use the most current tools for the analysis of their datasets.

    Dipl.-Inf. Sebastian Jaenicke, AG Bioinformatik und Systembiologie, Justus-Liebig-Universität Gießen

KoBIS Juli 2016

05.07.2016 um 14:15 in Raum C.3.34, THM Gießen

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  • "Stem cell models in preclinical and clinical setting: current status and future prospects in healthcare system"

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    Dr. rer. nat Shibashish Giri, Applied Stem Cell Biology and Cell Technology, Biomedical and Biotechnological Center (BBZ), Medical faculty, University of Leipzig

KoBIS Juni 2016

07.06.2016 um 14:15 in Raum C.3.34, THM Gießen

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  • "Metabolic lifestyle and energy conservation tricks of giant, symbiotic bacteria ― the special case of Epulopiscium"

    2016-06-Ngugi-01_thumb.JPG

    Research in the last few decades has brought considerable advances in our understanding of the biology of several giant bacteria that seem to bypass the diffusional limitations of unicellular prokaryotes lacking specialized intracellular transport mechanisms, including two marine sulfur-oxidizing species (Beggiatoa and Thiomargarita) and the gut symbiont of herbivorous surgeonfishes, Epulopiscium fishelsonii. The latter bacteria are morphologically diverse, sometimes longer than 600 µm, and extraordinarily polyploidic, carrying as much as 600,000 genome copies per cell. Unlike other bacteria, they form multiple intracellular offspring in a circadian reproductive cycle that is synchronized with the feeding behaviour of their hosts. However, very little is known about the metabolic traits of Epulopiscium and their potential roles in the digestive process. In this talk, I will provide genomic evidence that links metabolism, lifestyle, and mechanisms for energy-conservation in Epulopiscium and advances our understanding of their potential physiological roles in their symbiosis with surgeonfishes.

    Dr. David Kamanda Ngugi, Red Sea Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia

  • "Introduction to the IT infrastructure of the Bioinformatics Core Facility (BCF)"

    2016-06-Bruckskotten-01_thumb.JPG2016-06-Linke-01_thumb.JPG

    The Bioinformatics Core Facility (BCF) hosts a centralized IT-infrastructure for all bio-computational services, storage, computing and specialized hardware systems. The facility also provides a comprehensive bioinformatics-IT-environment, software support and automated pipelines for batch processing of typical high-throughput workflows, a collection of bioinformatics databases and other ressources. Furthermore we offer bioinformatics teaching and training, consulting and support for our partners and research collaborations.

    Dr. Burkhard Linke u. Dr. Marc Bruckskotten, AG Bioinformatik und Systembiologie, Justus-Liebig-Universität Gießen

KoBIS Mai 2016

03.05.2016 um 14:15 in Raum C.3.34, THM Gießen

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  • "High-throughput software pipelines for genotypic antimicrobial resistance testing"

    2016-05-Sträßer-01_thumb.JPG2016-05-Sträßer-02_thumb.JPG

    Das Ziel der Arbeit war die Erstellung eines Programms zur Erkennung verschiedener Antibiotikaresistenzen in S. aureus. Ausgehend von publizierten resistenzverursachenden Mutationen wurde die Implementierung mit 924 Testfiles überprüft und optimiert und eine Erkennungsrate innerhalb der FDA Limits erzielt.

    Jonas Sträßer, Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

  • "Strategies to identify genomic signatures of allopolyploidisation in Brassica napus"

    2016-05-Samans-01_thumb.JPG

    Allopolyploidization leads to the formation of new species with the potential of fast adaptation to rapidly changing environmental conditions and plays therefore an important role in plant diversification. The genome of the allopolyploid plant species Brassica napus (AACC, 2n=38; oilseed rape, canola) derived from the interspecific hybridization of the ancestral hexaploid genomes of B. rapa (AA, 2n=20) and B. oleracea (CC, 2n=18). Synthetic combination of the two diploid genomes leads to chromosomal rearrangements induced by meiotic errors during the first generations after hybridization, potentially creating variation which is relevant for both natural and artificial selection. In my talk I will describe a data analysis pipeline to identify chromosome restructuring patterns in polyploid genomes based on whole-genome resequencing datasets. Using this method we investigated polyploidisation signatures in 51 diverse natural and synthetic accessions of Brassica napus.

    Dr. Birgit Samans, Institut für Pflanzenbau und Pflanzenzüchtung, Justus-Liebig-Universität

KoBIS April 2016

05.04.2016 um 14:15 in Raum A20.0.07, THM Gießen

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  • "Differentielle Genexpressionsanalyse von in vivo markierter RNA / small-RNA-Seq Daten der Honigbiene: Eine Zeitreihenanalyse"

    2016-04-Arend-02_thumb.JPG

    Die Honigbiene (Apis mellifera) ist ein Schlüsselmodell für hoch soziale
    Spezies. Ihre sehr komplexen Verhaltensstrukturen, einschließlich Lernen und Gedächtnisbildung, sowie die Reaktion auf verschiedene Stressfaktoren, können auf molekularer Ebene als Änderung in der Genexpression gesehen werden. Die Genexpression wird durch die Aktivierung von Transkriptionsmaschinerie und Chromatinstruktur reguliert. Auf post-transkriptionaler Ebene erfolgt die Regulation durch kurze, hoch konservierte, nicht codierende RNAs (miRNA).
    Um verschiedene Fragestellungen ansprechen zu können, adaptierten und wendeten wir eine Technik an, die eine in vivo Markierung von de novo transkribierter mRNA in einem definierten Zeitfenster erlaubt. So erfolgten bereits eine Reihe differentieller Genexpressionsstudien auf Basis von Lernexperimenten.
    Schwerpunkt meiner Arbeit ist die Etablierung einer Pipeline zur Analyse von Zeitreihen RNA- / small-RNA Sequenzierdaten.

    Marie-Theres Arend, M.Sc., AG Zoologie/Physiologie-Neurobiologie , Universität des Saarlandes

  • "Efficient analysis of NGS data by workflow based systems"

    2016-04-Zimmermann-01_thumb.JPG

    Current chromatin research often involves the application of next generation sequencing (NGS)-based methods in order to gain structural as well as functional insights into how the chromatin template exerts its regulatory effects on nuclear processes like transcription, replication or DNA repair. Analysis of NGS data comes along with the requirements for computational infrastructure that allows the execution of relevant analysis tools. In order to enable our co-workers to perform analysis of their NGS-based data sets without expert programming knowledge we would like to provide an integrated workflow environment system. The workflow environment will be accessible to experimental biologists in order to enable them to perform standard ChIP-seq, RNA-seq and other chromatin-related data analysis workflows that are based on NGS-related techniques (MNase-seq, "3C"-based methods, etc.). In addition to basic workflows for execution of principal analysis steps like mapping, peak calling and annotation as well as visualization for exploratory analysis we would like to integrate more specific and complex workflows dedicated to project-specific questions. For this purpose we have decided to set up a GALAXY server, which allows maximum flexibility with respect to integration of already existing tools as well as extensibility in order to include additional more and specific data analysis tools. In example the Conveyer workflow engine will be used to sum up complex analysis steps to reduce the complexity and time of NGS analysis.

    Tobias Zimmermann, M.Sc., AG Bioinformatik und Systembiologie, Justus-Liebig-Universität

  • "Analysis of newborn screening data: New approaches in detecting metabolic disorders"

    2016-04-Menzel-01_thumb.JPG

    Newborn screening is applied to every newborn baby within the first 48 hours of life and includes measurement of 92 metabolies and aminoacids. The aim of this screening is to detect diseases as early as possible. Screening results do not confirm a disease, but identify abnormal values. More specific tests are then used to determine if a disease condition is present.
    There are 12 disease conditions which are covered by the current screening. Most of them are rare metabolic diseases. About 1 in 1623 children suffers from one of these. But in the screening about every second child shows at least one abnormal value. To improve this high false-positive rate a dataset of 20,000 anonymised screenings results were evaluated with different approaches. The primary focus was to build a basis for the selection of cut-off values, which determine what is labeled as abnormal. Furthermore, it was evaluated whether a multi-dimensional analysis, with the usage of machine-learning, can support the identification of special conditions.
    In this presentation methods and results of this analysis will be displayed.

    Michael Menzel, B.Sc., Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

KoBIS März 2016

01.03.2016 um 14:15 in Raum A12.3.04, THM Gießen

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  • "Bio Entity Graph Visualizer"

    2016-03-Marhold-02_thumb.JPG

    Beim "Bio Entity Graph Visualizer" handelt es sich um eine Visualisierung von Relationen zwischen Datenbankeinträgen verschiedener biologischer Strukturen anhand von gerichteten Graphen. Der Fokus liegt auf einer effizienten Strukturierung und Darstellung der vorhandenen Daten, Usability sowie Erweiterbarkeit.

    Ellen Marhold, Fachbereich MNI, Technische Hochschule Mittelhessen

  • "Genome Analyzer - Präsentation des Softwaretechnik-Praktikums im Wintersemester 2015/16"

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    Die Analyse von Integrationsstellen – Positionen im Genom, an denen fremde DNA eingefügt wird – ist ein wichtiges Forschungsgebiet, um beispielsweise Viren typisieren zu können. Mit den üblichen Genomebrowsern lässt sich die Analyse sehr vieler dieser Positionen innerhalb eines Genoms jedoch nicht bewerkstelligen. Daher entwickelten Bioinformatik-Studierende innerhalb des Softwaretechnik-Praktikums den "Genome Analyzer", welcher nicht einzelne Positionen anzeigt, sondern Statistiken über die Charakteristik der Integrationsstellen erstellt. Das achtköpfige Team nutzte dazu Technologien und Tools wie Java, Maven, Git und Redmine. Ihr persönliches Fazit und das fertige Produkt werden die Studierenden beim Abschluss des Projekts am 01. März vorstellen.

    Studenten des Softwaretechnik-Praktikums, Fachbereich MNI, Technische Hochschule Mittelhessen

KoBIS Februar 2016

2.2.2016 um 14:15 in Raum D12.0.10, THM Gießen

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  • "Data preparation for transcriptome analysis with microarrays"

    2016-02-Windhorst-01_thumb.JPG

    Transcriptome analysis has become more and more important in the development of diagnostics and the search for biomarkers. Microarray analysis bears the advantage to analyze a great number of transcripts simultaneously and facilitate a better understanding of the role and involvement of specific transcripts. It may provide hints to biological mechanisms resulting from the gene expression profile or underlying mechanisms resulting in the observed gene expression profiles. To detect meaningful gene expression it is important to assure data quality in the preprocessing of microarray data.

    A workflow for data preprocessing and preparation has been developed for the open-source platform in R (r-project.com) with the help of R- and Bioconductor packages. It will be shown, that preprocessing plays an important role in the discovery and correction of handling errors, e.g., mislabeled arrays.

    Dr. Anita Windhorst, Institut für Medizinische Informatik, Justus-Liebig-Universität Gießen

  • "A microarray study based on tracheal aspirate fibroblasts with regard to treatment of severe lung diseases in premature born infants"

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    Microarray analysis is a powerful tool for simultaneously screening a high amount of transcripts resulting for example from different disease states or treatment variants. Statistical analysis of different experiments or conditions results in sets of significantly differentially regulated genes. In order to develop a hypothesis about gene function or functional changes, an in depth analysis of the resulting gene sets has to be conducted by applying diverse strategies and tools. This procedure will exemplary be shown by presenting a micro array study based on tracheal aspirate fibroblasts with regard to treatment of severe lung diseases in premature born infants.

    Application of glucocorticoids in preterm infants is aimed at reducing risk of inflammation, since in these patients high oxygen concentrations immediately after delivery are associated with elevated cytokine plasma levels and bronchopulmonary dysplasia (BPD). The glucocorticoid receptor (GR), which predominantly mediates the effect of glucocorticoid (GC) is involved in many homeostasis regulating processes and diverse stress pathways. The application of artificial glucocorticoid – dexamethasone (DEX) – on cultivated tracheal aspirate fibroblasts has been used to infer genes, which are involved in these processes. The results of this study will be presented.

    Dr. Melanie Markmann, Medizinische Mikrobiologie, Justus-Liebig-Universität Gießen

KoBIS November 2015

03.11.2015 um 14:30 in Raum D12.0.10, THM Gießen

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  • "Modeling Biology in Modelica: The Human Baroreflex"

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    Modelica is a declarative object-oriented acausal programming language designed for modeling complex physical systems. Using the example of a model of the human baroreflex, we show that this language can produce the same simulation results as the reference implementation in C. With our implementation we demonstrate that Modelica is perfectly suited to for building biological systems in a natural representation.

    Christopher Schölzel, M.Sc., Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

  • "Silicon Heart: An Easy to Use Interactive Real-Time Baroreflex Simulator"

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    A simulator of the baroreflex loop is implemented as a distributed system, including independent functional units with each of them running without synchronisation and in real-time. Individual components are build from extended equations of the well established Seidel-Herzel-model.
    The system includes five small computers representing five independent sub-models. Each component has a computer mouse connected that allows for real-time manipulation of simulation parameters in the respective part of the model. This way, numerical values of variables, such as neurotransmitter concentrations or breathing frequency, can easily be altered by turning the associated adjusting wheel.
    Virtual administration of drug substances and virtual disease simulations are performed and show that the asynchronous simulation is robust enough to be used as an intuitive model to study heart rate dynamics.

    Michael Menzel, B.Sc., Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

  • "Modeling the kinetics of the cardiac acetylcholine receptor M2"

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    The muscarinic acetylcholine recepor M2 located at the heart muscle cells is modeled using Modelica. The model includes the receptor, the different stages of the G-protein and the decomposition of acetylcholine by acetylcholinesterase.

    Hannah Franziska Löchel, B.Sc., Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

KoBIS Oktober 2015

06.10.2015 um 14:15 in Raum D12.0.10, THM Gießen

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  • "Bryophyte-specific orphan genes inferred from the onekp project"

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    Here, we analyzed transcript data from 7 hornworts, 26 liverworts and 41 mosses from the 1KP project (www.onekp.com) to determine the origin of genes and the abundance of genome duplication events in bryophytes. Sequence comparisons of the transcriptome data with published plant genomes and a representative set of genomes outside the green tree of life were not only used to construct phylostratigraphic gene age maps, but also to infer orthologous groups within and between the analyzed bryophyte species.

    Dr. Kristian Ullrich, Rensing lab, Zellbiologie, Philipps-Universität Marburg, Marburg

  • "Gene family classification using HMM domain-based rule sets"

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    Using profile Hidden Markov Models (HMMs) proteins can be scanned for domains. Combining the result with domain-based rule sets makes it possible to classify proteins into different gene families based on their domain architecture.

    Per Wilhemsson, M.Sc., Rensing lab, Zellbiologie, Philipps-Universität Marburg, Marburg

  • "Reproducibility and normalization issues of RNA-seq experiments"

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    The number of RNA-seq data in the databases rises quickly. To analyze and compare the different data sets, normalization is indispensable. Not only digital normalization is the method of choose. Adding spike-ins to your library could also be an interesting option.

    Fabian Haas, M.Sc., Rensing lab, Zellbiologie, Philipps-Universität Marburg, Marburg

KoBIS Juli 2015

07.07.2015 um 14:15 in Raum D12.0.10, THM Gießen

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  • "Computergestützte Annotation von Aminosäuresequenzen am Beispiel von rekombinanten Antikörpern und CARs (Chimeric Antigen Receptors)"

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    In der Arzneistoffentwicklung werden sämtliche Sequenzen registriert und in Datenbanken gelagert, um auch anderen Forschern zur Verfügung zu stehen. Die Pflege solcher Datenmengen ist eine mühevolle Aufgabe. Daher kommen für die Annotationen dieser Sequenzen computergestützte Services zum Einsatz. An Beispielen der aktuellen Antikörperforschung wird der Aufbau, die Funktionsweise und die Erweiterung eines solchen Sequence Annotation Service vorgestellt.

    Jochen Sieg, Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

  • "Genzentrierte Datenvisualisierung von TCGA Daten"

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    Seit Beginn des Projekts „The Cancer Genome Atlas“ wurden Genom-Daten von über 30 Krebsgruppen erhoben und der Öffentlichkeit zur Verfügung gestellt. Für die weitere Analyse der Datensätze wurde ein Genzentrischer Target CV aus den unter anderem eruierten mRNA Expressions-Werten, Kopierzahlvariationen und DNA-Änderungen generiert.

    Julian Kreis, Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

  • "Global Data Share - Improving research in the field of oncology by providing easy access to omic data. The first part of the project phase, completed tasks and prospects for the upcoming Bachelor Thesis."

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    Sebastian Spänig, Fachbereich MNI, Technische Hochschule Mittelhessen, Gießen

KoBIS Juni 2015

09.06.2015 um 14:15 in Raum D12.0.10, THM Gießen

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  • "EDGAR 2.0: an enhanced software platform for comparative gene content analyses"

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    The deployment of next generation sequencing approaches has caused a rapid increase in the number of completely sequenced genomes. As one result of this development, it is feasible to analyze not only single genomes, but large groups of related genomes in a comparative approach. A main task in comparative genomics is the identification of differential gene content in a number of genomes, i.e., the core genome or singleton genes. To support these studies the EDGAR software was developed. Using a generic orthology criterion based on the distribution of alignment hits within a genus EDGAR is designed to automatically perform genome comparisons in a high throughput approach. Comparative analyses for 2072 genomes across 161 genera taken from the NCBI genomes database were conducted with the software and the results are provided as a free-to-use public database. EDGAR provides several analysis and visualization features and significantly simplifies the comparative analysis of related genomes. The web-based user interface offers Venn diagrams, synteny plots, and a comparative view of the genomic neighbourhood of orthologous genes. Recently, the software was extended with various new features like statistical and phylogenetic analyses, replicon grouping options and second level analyses of meta-genesets. Noteworthy, EDGAR calculates phylogenetic trees as well as amino acid identity (AAI) matrices based on all genes of the core genome, providing a solid basis for both analyses. Thus the software supports a quick survey of evolutionary relationships and simplifies the process of obtaining new biological insights into the differential gene content of kindred genomes. EDGAR is among the most established tools in the field of comparative genomics. In addition to the publicly available projects more than 150 private projects with more than 5000 analyzed genomes have been computed during the last 5 years. EDGAR is available via the public web server http://edgar.computational.bio.

    Dr. Jochen Blom, AG Bioinformatik und Systembiologie, Justus-Liebig-Universität Gießen

  • "Viral genetic diversity from next-generation-sequencing-data: Challenges and Opportunities from the bioinformatics point of view"

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    RNA viruses, exist in large populations and display high genetic heterogeneity within and between infected hosts. Assessing intra-patient viral genetic diversity is essential for understanding the evolutionary dynamics of viruses, for designing effective vaccines, and for the success of antiviral therapy. Next-generation sequencing (NGS) technologies allow the rapid and cost-effective acquisition of thousands to millions of short DNA sequences from a single sample. However, this approach entails several challenges in terms of experimental design and computational data analysis. Although the massively parallel sequencing approach can detect low-frequency mutations and it provides a snapshot of the entire virus population, analyzing deep sequencing data obtained from diverse virus populations is challenging because of PCR and sequencing errors and short read lengths, such that the experiment provides only indirect evidence of the underlying viral population structure. Recent computational and statistical advances allow for accommodating some of the confounding factors, including methods for read error correction, haplotype reconstruction, and haplotype frequency estimation. In this talk we will give an overview of the state-of-the-art in this field and review opportunities and shortcomings of the statistical models and algorithms involved and discuss their implications of these shortcomings in terms of experimental design and sequencing strategies.

    Prof. Dr. Franz Cemic, Fachbereich MNI, Technische Hochschule Mittelhessen, Giessen

KoBIS Mai 2015

05.05.2015 um 16:15 in Raum D10.3.01, THM Gießen

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  • "de.NBI - Das Deutsche Netzwerk für Bioinformatik-Infrastrukturen"

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    Das BMBF finanziert das „Deutsche Netzwerk für Bioinformatik-Infrastruktur“ (de.NBI) bis 2020 mit insgesamt 22 Millionen Euro. In dem Netzwerk arbeiten acht deutsche Zentren zusammen, die für die Bearbeitung bioinformatischer Daten auf dem Gebiet der Lebenswissenschaften ausgewiesen sind. Als Gemeinschaftseinrichtung bieten sie künftig bioinformatische Dienstleistungen für Forschungsprojekte aus Biotechnologie und Biomedizin an. Außerdem bildet das Netzwerk Forscherinnen und Forscher in der Nutzung von Bioinformatik-Software aus. In meinem Vortrag gebe ich einen Überblick über das Deutsche Netzwerk für Bioinformatik-Infrastruktur und beschreibe die Aufgaben und Zielsetzungen des Leistungszentrums BiGi - Bielefeld-Gießen Zentrum für Mikrobielle Bioinformatik.

    Prof. Dr. Alexander Goesmann, AG Bioinformatik und Systembiologie, Justus-Liebig-Universität Gießen

  • "Glycosciences.de und MonosaccharideDB: Glyco-Bioinformatik in Gießen"

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    Die Glyco-Bioinformatik ist ein recht junges Teilgebiet der Bioinformatik. Im Fokus stehen hier Kohlenhydrate sowie Glycokonjugate (insb. Glycoproteine und Glycolipide) sowie die Wechselwirkungen zwischen Kohlenhydraten und anderen Molekülen. Diese Wechselwirkungen spielen eine wichtige Rolle bei der Kommunikation zwischen Zellen, aber auch bei verschiedenen Krankheiten sowie der Immunantwort. Das Gießener Glycosciences.de Portal ist eines der größten Glyco-Bioinformatik-Portale weltweit. Der Hauptfokus liegt hier auf der Modellierung und Analyse von 3D-Strukturdaten, wobei die Protein Data Bank (PDB) eine wichtige Datenquelle ist.
    Ein Hindernis bei der weltweiten Entwicklung der Glyco-Bioinformatik ist die geringe Vernetzung der verschiedenen Datenbanken und Programme. Der Austausch von Daten sowie die Querverlinkung der Datenbanken scheitern häufig schon an unterschiedlichen Sequenzformaten zur Speicherung der Kohlenhydrate. Bei der Konvertierung dieser Formate müssen insbesondere auch die Namen für die einzelnen Bausteine, die Monosaccharide, übersetzt werden. Hierzu wurde die MonosaccharideDB entwickelt, die ebenfalls in Gießen gepflegt wird. Sie fungiert als Referenzdatenbank zur Monosaccharid-Nomenklatur und ist dabei so aufgebaut, dass nicht nur die zurzeit knapp 800 vorgespeicherten Monosaccharide abgefragt werden können, sondern über Parser- und Encoder-Routinen auch zu noch nicht gespeicherten Bausteinen Informationen „on the fly“ bereitgestellt werden können. Damit liefert MonosaccharideDB einen wichtigen Beitrag bei der weltweiten Vernetzung von Glyco-Bioinformatik-Ressourcen und somit zur weiteren Entwicklung der Glyco-Bioinformatik.

    Dr. Thomas Lütteke, Institut für Veterinärphysiologie und Biochemie, Justus-Liebig-Universität Gießen

KoBIS April 2015

07.04.2015 um 16:15 in Raum A20.0.07, THM Gießen

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  • "BioLib - Präsentation des Softwaretechnik-Praktikums im Wintersemester 2014/15"

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    Ein essenzieller Bestandteil der Arbeit eines Bioinformatikers ist das Entwickeln von Software. Um für biologische Fragestellungen Software zu programmieren, ist das fundierte Hintergrundwissen der Lebenswissenschaft unersetzlich. Das musste die letzte Gruppe des Softwaretechnik-Praktikums erfahren, die mit nur einem Bioinformatiker in ihren Reihen eine Bibliothek für Sequenzalgorithmen weiterentwickelte. Unter Verwendung von Technologien und Tools wie Java, Git und Redmine führten sie ein umfassendes Refactoring der bereits bestehenden Bibliothek durch und erweiterten sie um einige Algorithmen wie etwa BLAST. Wie die 14-köpfige Gruppe ihre Arbeit organisierte, welche Hindernisse und Lösungen es gab und wie das endgültige Produkt aussieht, präsentieren die Studenten am 07.04.2015.

    Studenten des Softwaretechnik-Praktikums, Fachbereich MNI, Technische Hochschule Mittelhessen

KoBIS März 2015

03.03.2015 um 10:00 in Raum D10.3.02, THM Gießen

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  • "Cardiovascular Bioinformatics: Perspective on long noncoding RNAs"

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    Dr. Shizuka Uchida, Institute of Cardiovascular Regeneration, Goethe-Universität Frankfurt

  • "C-It-Loci: A knowledge database for tissue-enriched loci"

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    Tyler Weirick, M.Sc., Institute of Cardiovascular Regeneration, Goethe-Universität Frankfurt

  • "RNAeditor: a tool to identify editing events from RNA-seq data"

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    Dipl.-Bioinf. David John, Institute of Cardiovascular Regeneration, Goethe-Universität Frankfurt

About:

Das Kolloquium für Bioinformatik und Systembiologie Mittelhessen (KoBIS) ist eine gemeinsame Initiative der Technischen Hochschule Mittelhessen und der Justus-Liebig-Universität Gießen zum wissenschaftlichen Austausch zwischen Arbeitsgruppen und Hochschulen, die sich mit dem Thema Bioinformatik und Systembiologie beschäftigen. Seit Oktober 2015 gehört auch die Phillips-Universität Marburg zu den regelmäßig teilnehmenden Hochschulen. Präsentiert werden Projekt- und Abschlussarbeiten sowie aktuelle Forschungsthemen von Mitarbeitern und externen Gästen.

Interessierte sind herzlich willkommen!

Logo:

Bei dem KoBIS-Logo handelt es sich um eine Abwandlung eines Modells von H. Vogel zur Beschreibung des Blüten- bzw. Samenstands der Sonnenblume. Es symbolisiert damit also die Verbindung zwischen Biologie und Informatik bzw. Mathematik. Für Interessierte gibt es auch noch eine längere Nerd-Variante dieser Erklärung.

Zum Erstellen von Präsentationsfolien oder Artikeln zu KoBIS gibt es das Logo natürlich auch als Vektorgrafik.

Sonstiges:

Bildergalerie

Poster

Kontakt:

Technische Hochschule Mittelhessen
Fachbereich MNI
Life Science Informatics Laboratory (Prof. Dominik, Prof. Gogol-Döring)

Christopher Schölzel
E-Mail:

Valeria Blesius
E-Mail:

Impressum