Proteomics

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

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.

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.

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).

Tabloid Proteome

The online Tabloid Proteome is a database of protein association network generated using publically available mass spectrometry based experiments in PRIDE. These associations represent a broad scala of biological associations between pairs of proteins that goes well beyond mere binary protein interactions. As such, the provided information is almost completely complementary to traditional direct protein interaction studies.

In addition to the collection of co-occurring protein pairs, Tabloid Proteome also links to their biological relation in existing knowledgebases. Moreover, pathway links from Reactome, protein-protein interactions from IntAct and BioGRID, protein complexes from CORUM, and paralog information from Ensembl are also superimposed. Functional annotation is provided by disease information from DisGeNET, and Gene Ontology annotations.

Tabloid Proteome was developed at the Ghent university in 2017 and published in Nucleid Acids Research (manuscript). In October 2018, the first training on Tabloid Proteome took place in Gent.

Unipept

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.