Department of Bioinformatics – BiGCaT

BiGCaT Science Cafe


Schedule 2018

Date Time Room Presenter Topic Abstract Slides
11 Jan 16:00-17:00 UNS50/4.140a Alexander Koch The human cancer DNA methylation marker atlas Abstract
25 Jan UM Symposium ‘The Future of a Data-Driven Society’ Link
08 Feb 16:00-17:00 UNS50/4.140a Egon Willighagen NanoCommons: continued interoperability of nanosafety data Abstract
22 Feb 16:00-17:00 UNS50/4.140a Friederike Ehrhart MolData2 implementation study Abstract
22 Mar 12:00-13:00 UNS60/Co-greep BiGCaT Science Cafe meets IDS Research in Progress Abstract
19 Apr 16:00-17:00 UNS50/4.140a Elisa Cirillo Precision medicine – the role of bioinformatics and systems biology
03 May 16:00-17:00 UNS50/4.140a Marvin Martens Introducing WikiPathways as a data source for Adverse Outcome Pathways Abstract
17 May 16:00-17:00 UNS50/4.140a Mirella Kalafati Relevance of cell type composition in subcutaneous adipose tissue samples; the role of immune cells Abstract
14 Jun 16:00-17:00 UNS50/4.140a Egon Willighagen Wikidata and Scholia as a hub linking chemical knowledge Abstract
28 Jun 16:00-17:00 UNS50/4.140a Nasim Sangani Rett syndrome, epilepsy and the potential involvement of the endoplasmic reticulum

Schedule 2017
Schedule 2016
Previous schedule

Abstracts

14 June 2018 – Egon Willighagen

Title: Wikidata and Scholia as a hub linking chemical knowledge

Making chemical databases more FAIR (findable, accessible, interoperable, and reusable) benefits computational chemistry and cheminformatics. We here discuss Wikidata, a young sister project of Wikipedia but with one big difference: it is a machine readable database, making it far more useful for interoperability of molecular databases in systems biology [1]. Thanks to the Wikidata:WikiProject Chemistry community, there is a growing amount of information about chemical compounds: Wikidata currently has over 150 thousand chemical compounds, of which more than 95% is associated with InChIKeys and has more than 70 thousand CAS registry numbers. Ongoing work by this WikiProject includes capturing chemical classes and chemical compounds in the various Wikipedias as machine readable data. Other projects include covering human drugs [2], MeSH Chemicals and Drugs, and volatile organic compounds. This work is supported the many tools around Wikidata, such as Mix’n’Match which is used to include ChEBI. We here introduce our contributions to the WikiProject Chemistry to support FAIR-ification of open chemical knowledge. For example, we proposed new Wikidata properties to annotate compounds with external database identifiers for the EPA CompTox Dashboard [3], the SPLASH [4], and MetaboLights. Furthermore, we used a combination of Bioclipse and QuickStatements to add missing chemical compounds for biological pathways from WikiPathways [5]. Finally, we introduce an extension of Scholia [6], visualizing data about compounds and compound classes, including external identifiers, physicochemical properties, and an overview of the literature from which the knowledge is derived.

[1] https://doi.org/10.3897/rio.1.e7573
[2] https://doi.org/10.1093/database/bax025
[3] https://doi.org/10.1186/s13321-017-0247-6
[4] https://doi.org/10.1038/nbt.3689
[5] https://doi.org/10.1093/nar/gkx1064
[6] https://doi.org/10.1007/978-3-319-70407-4_36


17 May 2018 – Mirella Kalafati

Title: Relevance of cell type composition in subcutaneous adipose tissue samples; the role of immune cells

Adipose tissue is comprised of heterogeneous cell types which can differentially impact disease phenotypes. Although the characteristic cell of adipose tissue is the adipocyte, this is not the only cell type present in adipose tissue, neither the most abundant (Rafols et al. 2013). Other cell types in adipose tissue described include stem cells, preadipocytes, macrophages, neutrophils, lymphocytes, and endothelial cells. All these cell types play a very important role in the function of adipose tissue as an endocrine organ, energy depot and energy metabolism. The balance between these different cell types and their expression profile is also important as it is closely related to the maintenance of energy homeostasis.
Common experimental methods for studying cell heterogeneity are immunohistochemistry and flow cytometry; recently computational methods have also been reported for characterizing cell composition of complex tissues from their gene expression profiles. Briefly, these algorithms identify cell-type specific marker genes from purified cell transcriptome profiles to construct a tissue-specific signature matrix, a set of differentially expressed genes across all cell types and utilize this signature matrix to perform the deconvolution step. On that accord, several gene expression studies that are publicly available can be used to estimate cell composition with these computational methods. A computational method, TissueDecoder (Lenz et al. 2018, submitted), was used to determine the cell type composition of 779 adipose tissue samples from four different depots (subcutaneous, n=616; omental, n=51; pericardial, n=66; epicardial, N=46). This algorithm determines the relative fraction of 21 different cell types.
In this project, we focus on the contribution of immune cells and investigated the differences in immune cell type composition of 12 immune cell types in the adipose tissue samples: B-cells, CD4T-cells, CD8T-cells, eosinophils, erythroblasts, macrophages, monocytes, myeloid dendritic cells, neutrophils, natural killer cells, plasmacytoid dendritic cells and platelets.


03 May 2018 – Marvin Martens

Title: Introducing WikiPathways as a data source for Adverse Outcome Pathways

In the last decade, omics-based approaches such as transcriptomics, proteomics and metabolomics have become valuable tools in toxicological research, and are finding their way into regulatory toxicity. A promising framework to bridge the gap between the molecular-level measurements and risk assessment is the concept of Adverse Outcome Pathways (AOPs). These pathways comprise mechanistic knowledge and connect biological events from a molecular level towards an adverse effect after exposure to a chemical or nanomaterial. However, the implementation of omics-based approaches in the AOPs and acceptance by the risk assessment community is still a challenge. Therefore, tools are required for omics-based data analysis and visualization, and to link the data to the traditional AOPs.
During the presentation, there will be an introduction to the concept of AOPs, and the repositories and tools that are currently used for them. These will be compared and I will show how WikiPathways will fit in, its added values and how AOPs are currently implemented and will discuss about future developments that could help implementing WikiPathways as a data source for AOPs.


22 March 2018 – BiGCaT Science Cafe meets IDS Research in Progress (Martina Summer-Kutmon and Amrapali Zaveri)

Title: WikiPathways – using crowdsourcing to enrich biological pathway models

WikiPathways is a community curated pathway database that enables researchers to capture rich, intuitive models of biological pathways. Building on the same MediaWiki software that powers Wikipedia, WikiPathways provides a framework that facilitates broad participation by the entire community, ranging from students to senior experts in each field. The database and the associated tools are developed as open source projects with a lot of community engagement.
In this talk, I will introduce WikiPathways and highlight the latest developments and ongoing projects. Together with researchers from IDS, we are investigating different project ideas regarding the use of crowdsourcing to extend and enrich our biological pathway collection, and I will present two concrete ideas regarding ontology annotations and image processing.

Discussion:
Crowdsourcing involves breaking down a large task into smaller – micro – tasks called HITs (Human Intelligent Tasks), submitting them to a crowdsourcing platform (e.g. Amazon Mechanical Turk, CrowdFlower etc.) and providing a monetary reward for each HIT.
The tasks primarily rely on basic human abilities and natural language understanding but less on acquired skills such as domain knowledge.
Crowdsourcing has been successfully utilized in biomedical research such as to annotate and extract gene expression signatures from GEO, to improve automated mining of biomedical text for annotating diseases, curation of gene-mutation relations, identifying relationships between drugs and side-effects, drugs and their indications, as well as annotation of microRNA functions.
In this session, we will discuss the features, challenges and possible applications of crowdsourcing for current problems such as enriching biological pathways in WikiPathways and assessing quality of biomedical metadata.


22 February 2018 – Friederike Ehrhart

Title: The MolData2 implementation study

To establish the functional consequences of putative or known genetic variants associated with disease, it is imperative to search worldwide resources and data for evidence that is already available. In the rare disease field, the challenges of doing this are pronounced as the data is often fragmented as a result of the fact that disease cohorts are small and managed by specialized researchers/physicians. The aim of this study was to map out the requirements for making sources more usable, and the current ways to fulfill these. This work has a focus on interoperability as well as enabling federated queries, thus strengthening the F and I aspects of FAIR. The overall aim is to accelerate the functional interpretation of genetic variants. This project is in line with ELIXIRs general data linkage plan, a coordinated action in the rare disease domain between BBMRI, ELIXIR and RD-Connect. The full description of this implementation study can be found here.


8 February 2018 – Egon Willighagen

Title: NanoCommons: continued interoperability of nanosafety data

The European Nanotechnology Community Informatics Platform: Bridging data and disciplinary gaps for industry and regulators (NanoCommons) four year project started in January funded under the H2020 programme (project ID: 731032). It aims to connect the nanotechnology and nanosafety communities, which are now disparate and unconnected. Consequently, the knowledge and data remain fragmented and inaccessible, such that from a data integrating and mining perspective it is clearly a “starting community”. NanoCommons will facilitate pooling and harmonising of methods and data for modelling, safe-by-design product development and regulatory approval purposes. Partners are mostly located in Europe, but also include an American partner.
Maastricht University’s involvement (close to 300 kEuro) focusses on FAIR-ness and interoperability of data more specifically. A large component is the continued development of the eNanoMapper ontology, to further improve interoperability. Smaller tasks around data quality, service discovery, and research data management plans will reflect the modern FAIR requirements. A second component focuses on read across and bioactivity prediction, where the kick-off meeting already hinted at an important role for adverse outcome pathways. As a knowledge infrastructure project, community engagement has significant attention, and UM will organize one annual conference (third year) and act as host to partners to visit our group (Department of Bioinformatics – BiGCaT) to learn about ontologies.


11 January 2018 – Alexander Koch

Title: The human cancer DNA methylation marker atlas

For decades, scientists have been searching for cancer biomarkers to improve the diagnosis and treatment of cancer. This resource-intensive endeavor has resulted in thousands of biomarker publications, but translation of these markers into clinical practice hardly takes place. Estimations by Poste (2011) and Kern (2012) put the number of published markers that are used in the clinic below one percent.

If we are to cross this so-called biomedical “valley of death” for DNA methylation markers, we have to find a way to increase the scientific quality and reproducibility of biomarker research, reduce the number of markers lost in translation, and accelerate the development of clinically useful markers.

We have devised a strategy to tackle these issues and improve the reliability, efficiency and translation of cancer DNA methylation markers, while at the same time promoting data sharing. At the heart of this strategy is the construction of a database of all published markers. This unique database would provide researchers with a valuable resource where they can find and evaluate existing markers.

In my presentation for the BiGCaT Science Cafe, I will focus on the technical implementation of this marker database, including some the hurdles I have encountered and things I have learned along the way.