Computational Omics and Systems Biology Group

Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions

A. S. Ferreira Diamantino Coelho e Silva, R. Bouwmeester, L. Martens, and S. Degroeve
BIOINFORMATICS, 2020

Anatomy and evolution of database search engines : a central component of mass spectrometry based proteomic workflows

K. Verheggen, H. Raeder, F. S. Berven, L. Martens, H. Barsnes, and M. Vaudel
MASS SPECTROMETRY REVIEWS, 2020

A comparison of collision cross section values obtained via travelling wave ion mobility-mass spectrometry and ultra high performance liquid chromatography-ion mobility-mass spectrometry : application to the characterisation of metabolites in rat urine

L. C. Nye, J. P. Williams, N. C. Munjoma, M. P. Letertre, M. Coen, R. Bouwmeester, L. Martens, J. R. Swann, J. K. Nicholson, R. S. Plumb, M. McCullagh, L. A. Gethings, S. Lai, J. I. Langridge, J. P. Vissers, and I. D. Wilson
JOURNAL OF CHROMATOGRAPHY A, 2019

Updated MS²PIP web server delivers fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques

R. Gabriels, L. Martens, and S. Degroeve
NUCLEIC ACIDS RESEARCH, 2019

(MSPIP)-P-2 is a data-driven tool that accurately predicts peak intensities for a given peptide's fragmentation mass spectrum. Since the release of the (MSPIP)-P-2 web server in 2015, we have brought significant updates to both the tool and the web server. In addition to the original models for CID and HCD fragmentation, we have added specialized models for the TripleTOF 5600+ mass spectrometer, for TMT-labeled peptides, for iTRAQ-labeled peptides, and for iTRAQ-labeled phosphopeptides. Because the fragmentation pattern is heavily altered in each of these cases, these additional models greatly improve the prediction accuracy for their corresponding data types. We have also substantially reduced the computational resources required to run (MSPIP)-P-2, and have completely rebuilt the web server, which now allows predictions of up to 100 000 peptide sequences in a single request. The MS(2)PIPweb server is freely available at https://iomics.ugent.be/ms2pip/.

Challenges and promise at the interface of metaproteomics and genomics : an overview of recent progress in metaproteogenomic data analysis

H. Schiebenhoefer, T. Van Den Bossche, S. Fuchs, B. Y. Renard, T. Muth, and L. Martens
EXPERT REVIEW OF PROTEOMICS, 2019

Introduction: The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.

Comprehensive and empirical evaluation of machine learning algorithms for small molecule LC retention time prediction

R. Bouwmeester, L. Martens, and S. Degroeve
ANALYTICAL CHEMISTRY, 2019

Liquid chromatography is a core component of almost all mass spectrometric analyses of (bio)molecules. Because of the high-throughput nature of mass spectrometric analyses, the interpretation of these chromatographic data increasingly relies on informatics solutions that attempt to predict an analyte's retention time. The key components of such predictive algorithms are the features these are supplies with, and the actual machine learning algorithm used to fit the model parameters. Therefore, we have evaluated the performance of seven machine learning algorithms on 36 distinct metabolomics data sets, using two distinct feature sets. Interestingly, the results show that no single learning algorithm performs optimally for all data sets, with different types of algorithms achieving top performance for different types of analytes or different protocols. Our results thus show that an evaluation of machine learning algorithms for retention time prediction is needed to find a suitable algorithm for specific analytes or protocols. Importantly, however, our results also show that blending different types of models together decreases the error on outliers, indicating that the combination of several approaches holds substantial promise for the development of more generic, high-performing algorithms.

An update on the moFF algorithm for label-free quantitative proteomics

A. Argentini, A. Staes, B. A. Grüning, S. Mehta, C. Easterly, T. Griffin, P. Jagtap, F. Impens, and L. Martens
JOURNAL OF PROTEOME RESEARCH, 2019

moFF is a modular and operating-system-independent tool for quantitative analysis of label-free mass-spectrometry-based proteomics data. The moFF workflow, comprising matching-between-runs and apex quantification, can be applied to any upstream search engine's output, along with the corresponding Thermo or mzML raw file. We here present moFF 2.0, with improvements in speed through multithreading, the use of a new raw file access library, and a novel filtering approach in the matching-between-runs module. This filter allows moFF to correctly identify features that are present in one run but not in another, as demonstrated using spiked-in iRT peptides. Moreover, moFF 2.0 also provides a new peptide summary export that can be used in downstream statistical analysis. moFF is open source and freely available and can be downloaded from https://github.com/compomics/moFF

DoRes within CellMissy : dose-response analysis on cell migration and related data

G. Sergeant, L. Martens, M. Van Troys, and P. Masuzzo
BIOINFORMATICS, 2019

In cancer research, cell-based assays are used to assess cell migration and invasion. The major bottleneck is the lack of automated tools to visualize and analyse the large amounts of biological dose-response data produced. To address this challenge, we have developed an automated and free software package for dose-response analyses, DoRes, which is released as an add-on of the freely available and open-source tool CellMissy, dedicated to the management and analysis of cell migration data. DoRes implements non-linear curve fitting functionality into a robust, user-friendly and flexible software package with the possibility of importing a tabular file or starting from a cell migration experiment. We demonstrate the ability of the software by analysing public dose-response data and a typical cell migration experiment, and show that the extracted dose-response parameters and the calculated statistical values are consistently comparable to those of the widely used, commercial software GraphPad Prism.

Unipept 4.0 : functional analysis of metaproteome data

R. Gurdeep Singh, A. Tanca, A. Palomba, F. Van der Jeugt, P. Verschaffelt, S. Uzzau, L. Martens, P. Dawyndt, and B. Mesuere
JOURNAL OF PROTEOME RESEARCH, 2019

Unipept (https://unipept.ugent.be) is a web application for metaproteome data analysis, with an initial focus on tryptic-peptide-based biodiversity analysis of MS/MS samples. Because the true potential of metaproteomics lies in gaining insight into the expressed functions of complex environmental samples, the 4.0 release of Unipept introduces complementary functional analysis based on GO terms and EC numbers. Integration of this new functional analysis with the existing biodiversity analysis is an important asset of the extended pipeline. As a proof of concept, a human faecal metaproteome data set from 15 healthy subjects was reanalyzed with Unipept 4.0, yielding fast, detailed, and straightforward characterization of taxon-specific catalytic functions that is shown to be consistent with previous results from a BLAST-based functional analysis of the same data.

LNCipedia 5 : towards a reference set of human long non-coding RNAs

P. Volders, J. Anckaert, K. Verheggen, J. Nuytens, L. Martens, P. Mestdagh, and J. Vandesompele
NUCLEIC ACIDS RESEARCH, 2019

While long non-coding RNA (lncRNA) research in the past has primarily focused on the discovery of novel genes, today it has shifted towards functional annotation of this large class of genes. With thousands of lncRNA studies published every year, the current challenge lies in keeping track of which lncRNAs are functionally described. This is further complicated by the fact that lncRNA nomenclature is not straightforward and lncRNA annotation is scattered across different resources with their own quality metrics and definition of a lncRNA. To overcome this issue, large scale curation and annotation is needed. Here, we present the fifth release of the human lncRNA database LNCipedia (https://lncipedia.org). The most notable improvements include manual literature curation of 2482 lncRNA articles and the use of official gene symbols when available. In addition, an improved filtering pipeline results in a higher quality reference lncRNA gene set.