Our Services
Customer-oriented in scientific consulting, contract research and customised training, LifeGlimmer provides you with efficient means to outsource your bioinformatics and systems biology needs. Our experts work in close collaboration with you, providing tailor-made solutions in a result-driven manner, whilst remaining flexible with preferred formats and workflows.
Let our biological data analysts work for you. Discover new links in complex datasets, visualise and understand dynamic networks, and predict systems behaviour.
Pathway analysis
A thorough understanding of any biological system requires not only the knowledge of which genes, proteins or metabolites are responding to a stimulus but how the network of these molecules interact and regulate each other in biological pathways. Pathway analysis can be applied to interpret omics data in a broad variety of experimental and clinical contexts. In medical applications we can gain insights into the molecular basis of disease by comparing healthy and diseased tissue or investigating the response to drug treatments. In synthetic biology, genome engineering can be used to introduce new traits into bacteria and the effect of these on a pathway level can be explored. LifeGlimmer combines a variety of analytical algorithms that utilise data from publicly available bioinformatics resources for pathway analysis in these application areas. LifeGlimmer’s solutions include:
- Gene Set Enrichment Analysis using a variety of Gene Sets, e.g. Gene Ontology and MSigDB Broad Institute
- Use of topological methods implemented in R for investigating interactions between differentially expressed genes
- Weighted gene co-expression network analysis to search for correlation patterns in RNASeq data
- Implementation of Cytoscape apps (e.g. SyncVis) for visualizing omics data on molecular pathways
Text mining
Text remains one of the richest sources of information. The purpose of text mining is to convert unstructured descriptive text into a machine-readable format that can be used for downstream analysis. LifeGlimmer uses text mining techniques to automatically extract information from medical records and the abstracts of scientific papers. These data can then be incorporated into a variety of applications, such as defining parameters for patient stratification and the identification of biomarkers.
As part of the SysmedIBD project, LifeGlimmer has implemented a text mining pipeline that scans Electronic Health Records (EHRs) to find predictors of patient symptoms and disease courses.
LifeGlimmer’s solutions in this area include:
- Digitalization of the text
- De-identification of the text (with removal of protected health information)
- Data cleaning and pre-processing
- Creation of different types of datasets specific for the downstream analyses
- Clustering of patients
- Prediction of patient outcomes
Decision support systems (DSSs)
With the exponential increase and variety of health-related data, the methods to combine and make practical use of these data become ever more complex. But this also means that resulting applications have the potential to be more accurate and more powerful than ever. Decision support systems agglomerate such data to create adequate and easy-to-use tools that facilitate decision-making processes.
LifeGlimmer focuses on medical DSSs and offers the following:
- Development of disease-specific DSSs that help clinical decision-makers to more accurately treat their patients
- Inclusion of feedback from our partners and users to adapt tailored customizations
- Deployment of DSSs to desktop and mobile solutions
Data integration and management
There is an increasing amount and diversity of medical and biological data available in public databases. Valuable data can also be siloed in proprietary databases (for example at hospitals or Pharma companies) and use legacy formats that require standardisation before they can be integrated with other data types. Moreover new types of data, e.g. data from sensors and wearables are increasingly being collected. Analyzing and exploring any of these data types alone has only limited value. LifeGlimmer addresses the challenge of converting and integrating these data into a single framework in order to gain a better understanding of biological complexity.
To get the best value from data it needs to be stored and managed in a way that ensures it can be easily understood and shared between researchers in multiple institutions across geographical boundaries. LifeGlimmer employs strategies to ensure data are Findable, Accessible, Interoperable and Reusable (FAIR principle). Standardisation of data facilitates its reuse and integration with other data sources.
LifeGlimmer’s solutions include:
- Harmonisation of data from multiple sources to use shared terminologies e.g. ICD-10 terms for disease, Human Phenotype Ontology (HPO)
- Conversion of data into formats for sharing, e.g. XML, RDF and Systems Biology Markup Language (SBML)
- Liaison with researchers to prepare experimental and computational modeling data and associated metadata for sharing via platforms such as FAIRDOMHub
- Exploiting semantic web technologies to create extensible and flexible databases for biological data
Biostatistics
With the increasing amount of data generation, the methods to understand and extract useful information from it have been evolving rapidly. LifeGlimmer uses state of the art machine learning algorithms to predict features and outcomes with heterogeneous big data and also to reveal the predictive power in subsets of the data.
We apply various selected statistical modelling approaches to test your hypotheses.
LifeGlimmer’s solutions include:
- Statistical hypothesis testing
- Statistical modelling (generalized linear model, differential expression analysis, survival model, ...)
- Meta-analysis
- Machine learning algorithms (random forest, support vector machine, neural network, …)
- Co-expression network analysis
App development
Mobile apps are playing an ever increasing role in our everyday life, and the health area is not an exception. On the contrary: Apps are an integral part in personalized medicine and will become even more so in the near future. With the benefit of being always connected to the cloud, independent of location and time, apps can constantly monitor a user’s well-being and improve their quality of life. This effect is enhanced when using apps in combination with wearable devices, like sensors. LifeGlimmer’s solutions in this area include:
- Apps for clinical decision support (as developed for the INFECT project)
- Apps for personalized medicine
- Apps for citizen/patient adherence
- Development of native Android apps
- Experience with the Unity (game) engine for streamlined cross-platform software development:
- mobile (e.g. iOS or Android)
- stationary (e.g. web browser or VR/AR environments)
Scientific project management
Having an outstanding expertise in the management of international research projects in the life sciences (in particular in the systems biology/medicine sector), our services comprise the whole project pipeline from grant preparation to project management and financial monitoring.
Data visualization
Capturing complex biological data as clear, accurate and understandable network representations that license their proper examination, communication and understanding.
Computational modeling
- Genome-scale metabolic network reconstruction
- Metabolic flux balance and variability analysis
- Model-based gene knockout prediction
- Dynamic modeling of signalling and metabolic pathways for understanding, targeting and optimising dynamic network behaviour
- Predictive modeling for biomarkers, physiological effects, and clinical outcomes
Knowledge discovery
- Unveiling hidden links and correlations within and among your datasets through advanced biostatistics
- Extracting information via text and data mining and linking these data with bioinformatics tools
Training
In case you require training in bioinformatics-related tools and/or specific programming languages, our experienced team of computational biologists will be happy to help you out:
- R
- Python
- Java
- Perl
- PHP/SQL
- HTML/XML
- CSS/JavaScript
- RDF/SPARQL
- Shell scripting (free sample)
- ...
- Cytoscape
- R Shiny
- Android
- Unity
- ...
Your partner in Germany, in the EU and worldwide
If you are interested in our scientific partnering, consultancy or training solutions, please This email address is being protected from spambots. You need JavaScript enabled to view it. us!
The INFECT consortium takes on a novel systems biology approach towards necrotizing soft tissue infection (NSTI). NSTI is an aggressively progressing bacterial infection that usually starts from a small wound. The diagnosis of this infection is often challenging due to clinical heterogeneity like presence of comorbidities. The INFECT consortium takes on a novel systems biology approach by integrating clinical data with the underlying molecular data in order to understand infection specific molecular mechanisms.
LifeGlimmer is involved as a computational partner in the consortium. We integrate interactions with experimental omics data to pinpoint disease-specific molecular mechanisms and develop statistical methods to reveal associations between clinical parameters (for example comorbidities, age, or BMI) and infection types. Furthermore, we develop effective classification methods to identify patient subgroups from a heterogeneous patient cohort aiming for a better diagnosis and ultimately treatment.
The partners of SysmedIBD are working towards a systems medicine approach of Inflammatory Bowel Disease. Mathematical experts develop models describing the process of chronic inflammation starting with one central pathway present in each cell type, the NF-kb pathway. Biologists with expertise in animal models generate data to validate the mathematical models and samples from patients are used to match experimental data to patient cohorts. Within the consortium, specialists in chemical computing will suggest potential drugs to treat chronic inflammation.
LifeGlimmer provides advanced statistical methods in differential expression analysis of time series RNA sequencing. We implement an interactive database that integrates the data produced during the project and allows users to perform differential expression analysis with their own data. Health medical records from follow-up cohorts are digitalised, anonymised and mined with specific text-mining tools to reveal information that was not systematically collected before. We investigate new characterisation of patients with those new data using machine learning algorithms.
PerICo is a multi-partner European Training Network under the European Union programme Horizon 2020. PerICo aims at uncovering how peroxisomes, important cellular organelles, participate in the intricate cellular interaction and signalling network. In light of the current understanding of the central role of peroxisomes in a variety of metabolic diseases, cancer and aging – this timely research programme will benefit society. PerICo trains a new generation of highly-qualified ESRs (Early stage researchers) focusing on the identification and functional characterization of peroxisomal membrane contact-sites and transporters.
Research at LifeGlimmer will specifically contribute to the model-driven discovery and network modelling of peroxisomal interactions.
The overarching aim of IBISBA is to create a coordinated network of research infrastructure facilities and thus provide a novel and timely response to the main challenges that are currently hampering the development of industrial biotechnology (IB) as a key enabling technology of Europe’s bioeconomy.
Building upon its expertise in large-scale functional annotation, analysis and integration of heterogeneous data sets (in particular omics), semantic technologies and metabolic modeling, Lifeglimmer participates in various WPs, in particular on the development and deployment of computational workflows for biocatalyst and process design, semantic analysis and integration of data using a decision support system.
In silico analysis of algal metabolism for industrial application is often a challenge due to the fact that algae are in general less understood in the research field than plants or bacteria. In the SPLASH project LifeGlimmer is involved in genome-scale metabolic modelling and analysis of the green microalga Botryococcus braunii which is capable of overproducing long-chain hydrocarbons, botryococcenes, and exopolysaccharides etc. Microalgae are promising new renewable feedstocks for chemicals and plastics. They can be cultivated on non-arable land and can yield valuable compounds for chemical industries. If microcalgae can be sustainably utilized at an industrial scale this will provide new opportunities for decreased dependency on fossil feedstocks and potentially contribute to climate mitigation and reduced pressure on land resources.
The Marie Curie Initial Training Network PerFuMe (PERoxisome Formation, Function, Metabolism) is an interdisciplinary and intersectoral initial stage training network (ITN) at the interface of medicine, plant and fungal biology, devoted to understanding the principles of peroxisome biology.
EmPowerPutida answers current market needs by the thorough application of Synthetic Biology. This industrially driven project will pave the way to the production of so far non-accessible biological compounds. Therefore, the lifestyle of Pseudomonas putida, a bacterium with remarkable metabolic endowment and stress tolerance, will be engineered. The guiding idea is to embed optimised biosynthetic pathways into an optimised chassis offering new functionalities. We have the ambition to establish Pseudomonas putida as the major European platform of choice for industrial, large-scale whole-cell biocatalysis directed at the sustainable biotechnological production of bulk and specialty chemicals.
As part of the project LifeGlimmer engages in data management and dissemination activities. Intensive interaction with relevant stakeholder groups contributes to the public debate on Synthetic Biology and maximises the socio-economic impact of the project.
The Chromatin3D project explores the molecular, cellular and systems level biology of chromatin through the cross-disciplinary expertise of academic and non-academic partners. Chromatin is a key component of the cell nucleus and the destabilization of regulatory mechanisms that act on the chromatin structure are implicated in pathologies, such as cancer. The aim of this project is to gain mechanistic insight of the dynamic assembly and modification of chromatin during developmental processes and its deregulation during the onset of disease.
The aim of NORM-SYS is to incorporate established community standards for data and in silico models in systems biology into generalized concepts that enable easy communication between expert groups and standardization organisations.
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