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The Bio2Byte SARS-CoV-2 website provides sequence-based predictions to grasp the behaviour of proteins that compose the SARS-CoV-2 virus. This unique resource gives a different view on the SARS-CoV-2 proteins, for some of which very little is known about their behaviour.

This SARS-CoV-2 specific website was established by the Bio2Byte group of the Interuniversity Institute of Bioinformatics in Brussels (IB)² in collaboration with VU Amsterdam. The manuscript describing the website is available as a preprint in BioRXiv.

QCQuan is a webtool for automated quality control and statistical analysis of protein expression following labelled mass spectrometry experiments. The tool is focused on differential expression analysis generated by isobaric labelling experiments. Flat text files from maxQuant, Proteome Discoverer or any other tools can be uploaded for a post-identification analysis. In this process, the user is offered very few options to modify the workflow in order to enhance comparable outcomes from different proteomic experiments. 

QCQuan was developed at the University of Hasselt in collaboration with VITO, the manuscript published in the Journal of Proteome Research can be accessed here.

Galaxy @Sciensano is a Galaxy server dedicated towards public health applications, focusing on making available tools, pipelines and databases for whole genome sequencing in routine pathogen typing and characterization. The application combines specialised in-house developed tools, pipelines and databases with general bioinformatics tools available in the Galaxy Toolshed.

The Galaxy instance of Sciensano has been in development since 2016 and is being used since 2017 by scientists working at Sciensano. The public Galaxy instance has been launched in October 2019.

A series of training videos on how to use the application are available on YouTube. The introduction videos are accompanied with tutorials on the basics of next-generation sequencing data analysis and more specialized topics of interest in public health (such as AMR detection and cgMLST analysis).

Fairdomhub.be is a research data management tool that enables researchers to manage research data and produce FAIR data by design. The platform keeps together metadata about experiments and links to the associated data files. An important feature of the platform is the integration of standard metadata used by EBI repositories, which makes Fairdomhub.be also a tool for brokering data towards ELIXIR deposition databases.

This Belgian instance of the FAIRDOMhub open source platform is developed by ELIXIR Belgium and currently tested by several local research groups. Additional templates and new features are being added to address the specific needs of life science researchers. The platform is hosted on the Flemish Supercomputer Centre.

Usegalaxy.be is a public Galaxy instance enabling reproducible data analysis through workflows. Next to providing support for reproducible science, the platform facilitates sharing of data and results, and removes the need for users to install tools.

UseGalaxy.be was developed by ELIXIR Belgium and has been released for public use in 2019. The platform is hosted on the Flemish Supercomputer Centre.

Globally, Galaxy is broadly used in life sciences and beyond. Publications referring to Galaxy can be found here. In March 2020, the Belgian Galaxy instance was used to re-analyse available COVID-19 data and assess the reproducibility of initial papers on the COVID-19 genome. The joint paper by Galaxy teams from the United States, Australia and ELIXIR, indicated the importance of open access data and open reproducible analytics for fast and efficient response to global health crises as the COVID-19 pandemic.

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.

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 2015 – update 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.