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IBsquare Toolbox for Oligogenic Analysis

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.


The TCRex webtool allows functional interpretation of full human T-cell repertoire data derived from next generation sequencing.

TCRex is the first tool of its kind and is able to link T-cell receptor sequences to a rapidly expanding list of 49 different important immunogenic epitopes, consisting of 44 viral and 5 cancer epitopes. Additional epitopes can be added by users for their own use. The tool is able to calculate enrichment statistics and baseline prediction rates to evaluate full repertoires. It is unique among TCR-epitope prediction tools in that it allows processing of full human repertoires. It has also brought together the largest database on TCR-epitope data to train the underlying machine learning models through manual curation of various online resources and scientific literature.

TCRex was developed at the University of Antwerp and has been released for public use in 2018. The manuscript describing the webtool is available as a preprint in BioRXiv.  TCRex can also be followed on Twitter: @TCRexTool.

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).


Scop3P provides a unique and powerful resource to explore and understand the impact of phospho-sites on human protein structure and function, and can thus serve as a springboard for researchers seeking to analyse and interpret a given phosphosite or phosphoprotein in a structural, biophysical, and biological context.

The resource re-uses public domain data from a variety of leading international resources, including UniProtKB and PDB, but also uses reprocessed mass spectrometry-based phospho-proteomics data from PRIDE/ProteomExchange, which is in turn globally collected and thus wholly international-driven.

Scop3P was developed at Ghent University and is online since June 2019. The manuscript describing Scop3P is available as a preprint in BioRXiv.


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.


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


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.


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).


Unipept is an open source web application developed at Ghent University that is designed for metaproteomics data analysis with a focus on interactive data visualizations. Unipept is powered by an index containing all UniProt entries, a tweaked version of the NCBI taxonomy and a custom lowest common ancestor algorithm. This combination enables a blazingly fast biodiversity analysis of large and complex metaproteome samples. Next to these core functions, Unipept also has a tool for selecting unique peptides for targeted proteomics and for comparing genomes based on peptide similarity.

Unipept 4.0 is the latest release of the Unipept framework and includes a completely new functional analysis tool and an updated database based on UniProt 2018.06. A tutorial on Unipept has been published in 2018. All Unipept journal articles can be found on this page. Unipept can be followed on Twitter: @unipept.


WiNGS is being developed to succeed NGS-Logistics, the genomics data sharing platform established at KULeuven. The NGS-Logistics platform is currently deployed at 6 of the 8 genetic centres in Belgium and lets users identify clinically relevant mutations at other genetic centres. Its scope is similar but broader than the more recent Beacon project of the Global Alliance for Genomics and Health (GA4GH) and ELIXIR Europe, which provides a framework for public web services for variant discovery against distributed genomic data collections. The ELIXIR Belgium Human Data consortium, led by KULeuven, has been participating in the ELIXIR Beacon project and maintains a Belgian Beacon node.

The WiNGS (Widely integrated NGS) platform holds significant scalability improvements to tackle the complexity of analysing of Whole-Genome Sequencing (WGS) data. Because of the sensitivity of patient genomes and GDPR requirements, enhanced access control and privacy protection for this integrated platform will be developed. The first manuscript of the platform can be downloaded here.