Services

The Belgian ELIXIR node offers specialized bioinformatics resources accelerating research in life-science. These include databases, data resources, tools, workflows, web services, trainings, … that have applications in Plant Sciences, Human Data and Proteomics. Services that have been positively evaluated by the international ELIXIR Belgium Scientific Advisory Board are added to ELIXIR Belgium’s Service Delivery Plan. These Node Services are labelled as important resources produced by the Belgian bioinformatics community. Below, an overview of all ELIXIR Belgium services is given. A complete service list of all ELIXIR Nodes can be found on this link.

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Logo DIDA

DIDA (DIgenic diseases DAtabase) is a database that provides detailed information on genes and associated genetic variants involved in digenic diseases, the simplest form of oligogenic inheritance. The database was developed at the Interuniversity Institute of Bioinformatics in Brussels (IB)² and currently includes 258 digenic combinations involved in 54 different digenic diseases. These combinations are composed of 448 distinct variants, which are distributed over 169 distinct genes. The web interface provides browsing, exploration and search functionalities, as well as documentation and help pages, general database statistics and references to the original publications from which the data have been collected. The possibility to submit novel digenic data to DIDA is also provided.

DIDA is published in the Nucleic Acids Research Database issue 2016 and has been selected as a NAR 2016 Breakthrough paper. The manuscript can be accessed here and an in-depth analysis of the different types of digenic diseases, i.e. true digenic and composite, in DIDA can be found here.

DIDA is part of the IBsquare Toolbox for Oligogenic Analysis which is one of ELIXIR Belgium’s Node Services.

Logo Dodona

Dodona is an e-learning environment containing thousands of programming exercises that can be used to master several programming languages. Currently, we support Python, Java, JavaScript, R, Haskell, Prolog and Bash. All exercises come with automatic feedback on correctness, execution time and/or programming style.

The Dodona platform has been developed at Ghent University and was launched in September 2016. Over the last few years, the platform provided automated feedback on more than five million submissions from Ghent University students. Dodona is registered as a service provider with Belnet to enable access to a wide range of international research and education institutions that have been registered as identity providers in the eduGAIN project. In addition, ELIXIR AAI accounts can be used to access the platform. Currently, the platform is under active development.

Get a glimpse of Dodona by these examples:

Follow Dodona on Twitter: @DodonaEdu

Service Tags

Logo DynaMine

DynaMine is a fast predictor of protein backbone dynamics using only sequence information as input. DynaMine is able to accurately distinguish regions of different structural organization within proteins, such as folded domains and disordered linkers of different sizes. Additionally, it can identify disordered regions within proteins with an accuracy comparable to the most sophisticated existing disorder predictors. DynaMine achieves this high performance without depending on prior disorder knowledge or three-dimensional structural information, which makes it a unique approach on the field as well as providing independent proof of the relationship between dynamics and structural disorder in protein regions.

DynaMine was developed at the Interuniversity Institute of Bioinformatics in Brussels (IB)² and published in Nature Communications (the method) and Nucleic Acids Research (the web-server).

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The IBsquare Toolbox for Oligogenic Analysis comprises three tools that are meant to assist researchers and doctors alike in the identification of genetic diseases. These three tools are closely intertwined as the data contained in the DIDA database was used to train the VarCopp predictor, which in turn is part of the pipeline for ORVAL.

DIDA: DIgenic Diseases DAtabase is a novel database that provides for the first time detailed information on genes and associated genetic variants involved in digenic diseases, the simplest form of oligogenic inheritance.

VarCoPP: Variant Combination Pathogenicity Predictor is a machine-learning method that predicts the potential pathogenicity of any bi-locus variant combination (i.e. a combination of two to four variant alleles between two genes). It has been trained on digenic disease data present in the Digenic Diseases Database (DIDA) and variant data derived from control individuals of the 1000 Genomes Project (1KGP). VarCoPP consists of an ensemble of 500 individual Random Forest predictors that predict whether a variant combination is disease-causing (i.e. candidate or probably pathogenic) or neutral (i.e. probably neutral).

ORVAL: Oligogenic Resource for Variant AnaLysis is a platform for the prediction and exploration of candidate disease-causing oligogenic variant combinations.

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Logo iMONdb

iMonDB (Instrument MONitoring DataBase) is a unique tool and resource for online, longitudinal quality control monitoring of mass spectrometry instruments and the data they produce. The advantage of instrument information at the lowest level is the high sensitivity to detect emerging defects in a timely fashion. To this end, iMonDB allows to automatically extract, store, and manage the instrument parameters from raw-data objects into a highly efficient database structure. iMONdb was developed at the University of Antwerp, the manuscript can be accessed here.

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Logo MS DataConnect

The MSDataConnect consortium (UHasselt) connects partners involved in Multiple Sclerosis (MS) care, rehabilitation, and research, with partners involved in IT development, database management, data sharing procedures, statistics, machine learning and prediction modelling.

MS DataConnect focuses on developing (1) data collection procedures and tools to create data that is FAIR, (2) IT solutions to allow (temporarily) pooling and linking of FAIR data sets, (3) statistical methods to define minimal requirements for data sets, and (4) new analytical methods for optimal mining of connected and pooled FAIR data sets.

The MS DataConnect consortium is the project coordinator of the international project Multiple Sclerosis Data Alliance (MSDA), which brings together registry holders, patients, medical societies, academia, industry, the European Medicine Agency (EMA), and Health Technology Assessment (HTA) bodies. One of the first goals of the MSDA is the development of the MSDA cohort explorer. This tool will enable searching aggregated data across different MS registries and cohorts after data is mapped to a harmonized data template. The subject and variable selection tools in the MSDA cohort explorer will allow end-users to identify MS data cohorts suitable for their (research-)question, and will facilitate the initiation of (new) collaborations with these MS cohorts.

Follow MS DataConnect on Twitter: @MSDataConnect1.

Logo MS2PIP Server

The MS2PIP Server is a tool to accurately predict peptide fragment ion intensities for mass spectrometry-based proteomics data. It employs the XGBoost machine learning algorithm and is accessible through a RESTful API.

The MS²PIP Server enables any interested researcher to make use of MS²PIP, regardless of their computational experience. It is the only peak intensity prediction server that can predict peak intensities for multiple fragmentation methods, instruments and labelling techniques. The MS2PIP Server has been used as the benchmark for comparison of other recently published tools for the prediction of MS² peak intensities.

The MS2PIP Server was developed at Ghent University and published in Bioinformatics (original server) and Nucleic Acids Research (update 2015update 2019).

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Logo MutaFrame

MutaFrame enables you to explore the likely effect of amino acid variants (mutations) on human proteins. It provides predictions of the ‘deleteriousness’ of mutations in human proteins, with interpretation of the underlying machine learning decisions, access to other resources (EXaC, dbSNP), and the connection to protein structure information from the Protein Data Bank (PDB). MutaFrame aims to visualise these data to make them understandable for non-expert users, and serves as a knowledge base by providing information for all possible mutations in all human proteins.

MutaFrame is developed at the Interuniversity Institute of Bioinformatics in Brussels (IB)², the manuscript can be accessed here. Follow MutaFrame on Twitter: @MutaFrame.

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Logo ORCAE

ORCAE (Online Resource for Community Annotation of Eukaryotes) is a wiki style genome annotation and curation portal for eukaryotic species which offers the community the opportunity and means to improve genome annotations and to keep gene annotations up-to-date.

ORCAE was developed at the Ghent University and published in Nature Methods (manuscript - method). ORCAE-AOCC is a branch that is dedicated to the genomes of African orphan crops (resource - manuscript).

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Logo PIPPA

PIPPA is a central web interface and database that provides the tools for the management of different types of WIWAM plant phenotyping robots and for the analysis of images and data.

Due to significant advancements in imaging sensors and automation, digital phenotyping is becoming a routine tool in plant sciences requiring tools to manage and analyse the huge datasets produced. PIPPA is being developed at the Ghent University and the VIB-UGent Center for Plant Systems Biology. The website provides detailed information, demo’s and an illustrating flowchart.